From d14ae741175da1e1de4073809131388efdc1de05 Mon Sep 17 00:00:00 2001 From: "lichun.qu" <16975270+zzqlc@user.noreply.gitee.com> Date: Tue, 25 Aug 2026 09:56:25 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E6=9E=84RTK-IMU=E6=A0=87=E5=AE=9A?= =?UTF-8?q?=E9=93=BE=E8=B7=AF=E5=B9=B6=E5=AE=8C=E6=88=90=E6=9C=BA=E6=A2=B0?= =?UTF-8?q?=E5=85=88=E9=AA=8C=E5=B7=A5=E7=A8=8B=E9=AA=8C=E8=AF=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 19 + artifacts/rtk_imu_calibration_v3/README.md | 24 + .../engineering_release_decision.json | 128 + .../heldout_independent_innovation.json | 22231 +++++++ .../heldout_nonconverged_retry.json | 1799 + .../lever_information_window_selection.json | 3010 + ..._information_window_selection_refined.json | 21115 +++++++ ...echanical_prior_engineering_47_window.json | 1338 + .../mechanical_prior_engineering_heldout.json | 4944 ++ ...mechanical_prior_rotation_sensitivity.json | 4533 ++ ..._free_information_selected_mechanical.json | 1302 + .../propagation_bias_root_cause_audit.json | 49318 ++++++++++++++++ docs/rtk_imu_calibration.md | 208 +- docs/rtk_imu_engineering_6dof.md | 108 + docs/rtk_imu_multisource_v3.md | 175 + imu_lidar/rtk_attitude.py | 50 +- imu_lidar/rtk_imu_engineering.py | 1208 + imu_lidar/rtk_imu_multisource.py | 558 + imu_lidar/rtk_imu_node_graph.py | 529 + imu_lidar/rtk_imu_replay.py | 22 +- imu_lidar/rtk_imu_rotation.py | 353 +- imu_lidar/rtk_io.py | 29 +- tests/test_g90_multisource.py | 47 + tests/test_hi13_imu.py | 18 +- tests/test_rtk_imu_calibration.py | 33 +- tests/test_rtk_imu_engineering.py | 535 + tests/test_rtk_imu_multisource.py | 46 + tools/audit_rtk_imu_factor_consistency.py | 313 + tools/audit_rtk_imu_factor_yield.py | 151 + tools/audit_rtk_imu_heldout_innovation.py | 158 + tools/audit_rtk_imu_innovation_noise.py | 286 + tools/audit_rtk_imu_lever_excitation.py | 145 + tools/audit_rtk_imu_motion_excitation.py | 668 + tools/audit_rtk_imu_multihorizon.py | 149 + ...dit_rtk_imu_propagation_bias_root_cause.py | 304 + tools/export_g90_rtk_to_sessions.py | 6 + tools/export_rtk_imu_unified.py | 290 + tools/finalize_rtk_imu_engineering_release.py | 139 + ...e_rtk_imu_windows_by_actual_information.py | 137 + tools/retry_rtk_imu_heldout_nonconverged.py | 104 + tools/rscap_v2/g90_rtk.py | 173 +- tools/rscap_v2/hi13_imu.py | 75 +- tools/run_rtk_imu_calibration.py | 12 +- tools/run_rtk_imu_engineering_6dof.py | 125 + tools/run_rtk_imu_engineering_free_base.py | 254 + tools/run_rtk_imu_mechanical_prior_branch.py | 144 + tools/run_rtk_imu_mechanical_prior_heldout.py | 212 + ...u_mechanical_prior_rotation_sensitivity.py | 153 + tools/run_rtk_imu_multisource.py | 42 + tools/run_rtk_imu_node_graph_fixed_lever.py | 75 + tools/run_rtk_imu_node_graph_free_circle.py | 79 + tools/run_rtk_imu_node_graph_free_joint.py | 85 + tools/run_rtk_imu_node_graph_free_selected.py | 120 + tools/run_rtk_imu_node_graph_p05.py | 106 + ...ct_rtk_imu_windows_by_lever_information.py | 174 + tools/validate_rtk_hpr_dropout_bridge.py | 182 + 56 files changed, 118382 insertions(+), 159 deletions(-) create mode 100644 artifacts/rtk_imu_calibration_v3/README.md create mode 100644 artifacts/rtk_imu_calibration_v3/engineering_release_decision.json create mode 100644 artifacts/rtk_imu_calibration_v3/heldout_independent_innovation.json create mode 100644 artifacts/rtk_imu_calibration_v3/heldout_nonconverged_retry.json create mode 100644 artifacts/rtk_imu_calibration_v3/lever_information_window_selection.json create mode 100644 artifacts/rtk_imu_calibration_v3/lever_information_window_selection_refined.json create mode 100644 artifacts/rtk_imu_calibration_v3/mechanical_prior_engineering_47_window.json create mode 100644 artifacts/rtk_imu_calibration_v3/mechanical_prior_engineering_heldout.json create mode 100644 artifacts/rtk_imu_calibration_v3/mechanical_prior_rotation_sensitivity.json create mode 100644 artifacts/rtk_imu_calibration_v3/node_graph_free_information_selected_mechanical.json create mode 100644 artifacts/rtk_imu_calibration_v3/propagation_bias_root_cause_audit.json create mode 100644 docs/rtk_imu_engineering_6dof.md create mode 100644 docs/rtk_imu_multisource_v3.md create mode 100644 imu_lidar/rtk_imu_engineering.py create mode 100644 imu_lidar/rtk_imu_multisource.py create mode 100644 imu_lidar/rtk_imu_node_graph.py create mode 100644 tests/test_g90_multisource.py create mode 100644 tests/test_rtk_imu_engineering.py create mode 100644 tests/test_rtk_imu_multisource.py create mode 100644 tools/audit_rtk_imu_factor_consistency.py create mode 100644 tools/audit_rtk_imu_factor_yield.py create mode 100644 tools/audit_rtk_imu_heldout_innovation.py create mode 100644 tools/audit_rtk_imu_innovation_noise.py create mode 100644 tools/audit_rtk_imu_lever_excitation.py create mode 100644 tools/audit_rtk_imu_motion_excitation.py create mode 100644 tools/audit_rtk_imu_multihorizon.py create mode 100644 tools/audit_rtk_imu_propagation_bias_root_cause.py create mode 100644 tools/export_rtk_imu_unified.py create mode 100644 tools/finalize_rtk_imu_engineering_release.py create mode 100644 tools/refine_rtk_imu_windows_by_actual_information.py create mode 100644 tools/retry_rtk_imu_heldout_nonconverged.py create mode 100644 tools/run_rtk_imu_engineering_6dof.py create mode 100644 tools/run_rtk_imu_engineering_free_base.py create mode 100644 tools/run_rtk_imu_mechanical_prior_branch.py create mode 100644 tools/run_rtk_imu_mechanical_prior_heldout.py create mode 100644 tools/run_rtk_imu_mechanical_prior_rotation_sensitivity.py create mode 100644 tools/run_rtk_imu_multisource.py create mode 100644 tools/run_rtk_imu_node_graph_fixed_lever.py create mode 100644 tools/run_rtk_imu_node_graph_free_circle.py create mode 100644 tools/run_rtk_imu_node_graph_free_joint.py create mode 100644 tools/run_rtk_imu_node_graph_free_selected.py create mode 100644 tools/run_rtk_imu_node_graph_p05.py create mode 100644 tools/select_rtk_imu_windows_by_lever_information.py create mode 100644 tools/validate_rtk_hpr_dropout_bridge.py diff --git a/.gitignore b/.gitignore index b35a7a6..18bde27 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,22 @@ build/ examples/synthetic_session/ .venv/ venv/ + +# Local IDE state +.vs/ + +# RTK-IMU calibration process artifacts stay local. Keep only the reviewed +# V3 result bundle explicitly listed below under version control. +artifacts/rtk_imu_calibration_v2/ +artifacts/rtk_imu_calibration_v3/* +!artifacts/rtk_imu_calibration_v3/README.md +!artifacts/rtk_imu_calibration_v3/engineering_release_decision.json +!artifacts/rtk_imu_calibration_v3/heldout_independent_innovation.json +!artifacts/rtk_imu_calibration_v3/heldout_nonconverged_retry.json +!artifacts/rtk_imu_calibration_v3/lever_information_window_selection.json +!artifacts/rtk_imu_calibration_v3/lever_information_window_selection_refined.json +!artifacts/rtk_imu_calibration_v3/mechanical_prior_engineering_47_window.json +!artifacts/rtk_imu_calibration_v3/mechanical_prior_engineering_heldout.json +!artifacts/rtk_imu_calibration_v3/mechanical_prior_rotation_sensitivity.json +!artifacts/rtk_imu_calibration_v3/node_graph_free_information_selected_mechanical.json +!artifacts/rtk_imu_calibration_v3/propagation_bias_root_cause_audit.json diff --git a/artifacts/rtk_imu_calibration_v3/README.md b/artifacts/rtk_imu_calibration_v3/README.md new file mode 100644 index 0000000..973168c --- /dev/null +++ b/artifacts/rtk_imu_calibration_v3/README.md @@ -0,0 +1,24 @@ +# RTK-IMU V3 正式结果集 + +本目录只提交复现工程结论所需的审核结果。运行过程中的调试输出、状态快照、逐窗口 checkpoint 和旧版结果保留在本机，由仓库根目录 `.gitignore` 排除。 + +## 当前结论 + +- 固定旋转来源：`R2G_gravity_level_prior`。 +- 工程候选杆臂：`l_I = p_ANT1^I = [-0.4518015159, -0.2644749820, 0.7314656115] m`。 +- `data_only_translation_accepted=false`。 +- `engineering_translation_accepted=false`，原因是独立传播验证仍受公共加速度偏差影响。 +- `independent_extrinsic_sensitive_validation_passed=true`；当前结果可描述为机械杆臂经动态数据一致性验证的工程候选，不得描述为 data-only 平移标定结果。 + +## 文件说明 + +- `engineering_release_decision.json`：最终状态、变换和放行判定。 +- `mechanical_prior_engineering_47_window.json`：相同 47 个非重叠窗口上的 free/fixed/prior 对比。 +- `node_graph_free_information_selected_mechanical.json`：冻结的无先验 free-solve 基线。 +- `lever_information_window_selection*.json`：窗口选择及实际边缘信息复核。 +- `mechanical_prior_engineering_heldout.json`：未参与标定窗口的 held-out 验证。 +- `heldout_independent_innovation.json`、`heldout_nonconverged_retry.json`：独立创新与失败窗口重试结果。 +- `mechanical_prior_rotation_sensitivity.json`：固定候选杆臂的旋转扰动敏感性结果。 +- `propagation_bias_root_cause_audit.json`：独立传播公共加速度误差根因审计。 + +结果 JSON 是审核快照；需要重新生成时应通过 `tools/` 中相应入口运行，且不得提交运行产生的 checkpoint 或 `.npz` 状态文件。 diff --git a/artifacts/rtk_imu_calibration_v3/engineering_release_decision.json b/artifacts/rtk_imu_calibration_v3/engineering_release_decision.json new file mode 100644 index 0000000..05fb2ca --- /dev/null +++ b/artifacts/rtk_imu_calibration_v3/engineering_release_decision.json @@ -0,0 +1,128 @@ +{ + "scope": "final mechanical-prior RTK-IMU engineering release decision", + "no_refit_performed": true, + "data_only_full_free_called": false, + "bootstrap_called": false, + "loo_called": false, + "covariance_retuned": false, + "new_window_selection_called": false, + "parser_R0_modified": false, + "data_only_translation_accepted": false, + "translation_refined_by_data": false, + "mechanical_prior_consistent_with_calibration": true, + "heldout_physical_validation_passed": true, + "heldout_physical_gate_checks": { + "all_267_converged_after_retry": true, + "BEST_position_vector_p95_le_0p20_m": true, + "Doppler_vector_p95_le_0p50_m_s": true, + "HPR_normalized_p95_le_4": true, + "preintegration_normalized_p95_le_3": true + }, + "heldout_statistical_scale_passed": false, + "heldout_postfit_chi_square_per_dof": 0.12733913101711092, + "heldout_covariance_underdispersion_warning": true, + "independent_heldout_innovation_passed": false, + "common_constant_acceleration_error_detected": true, + "independent_propagation_validation_passed": false, + "independent_extrinsic_sensitive_validation_passed": true, + "rotation_sensitivity_passed": true, + "engineering_translation_acceptance_formula": "mechanical_prior_consistent_with_calibration AND heldout_physical_validation_passed AND independent_heldout_innovation_passed AND rotation_sensitivity_passed", + "engineering_translation_accepted": false, + "result_nature": "mechanically anchored + dynamically validated", + "summary": "Translation is mechanically anchored and dynamically validated. The current dataset does not independently observe translation accurately enough for data-only calibration, and does not provide meaningful refinement beyond the mechanical prior.", + "forbidden_descriptions": [ + "data-only calibrated translation", + "dynamically refined mechanical lever" + ], + "candidate_l_I_engineering_m": [ + -0.45180151590212486, + -0.26447498198536895, + 0.7314656114613277 + ], + "candidate_T_RTK_IMU": [ + [ + 0.9999999761265028, + -0.00021395911860711003, + -4.436766104011944e-05, + 0.45177737170031523 + ], + [ + 0.00021360059847267874, + 0.9999685415936667, + -0.007929072948303898, + 0.27036301129056084 + ], + [ + 4.606276276360097e-05, + 0.007929063282050246, + 0.9999685634227163, + -0.7293247665913783 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "candidate_T_IMU_RTK": [ + [ + 0.9999999761265029, + 0.00021360059847267876, + 4.606276276360098e-05, + -0.45180151590212486 + ], + [ + -0.00021395911860711008, + 0.9999685415936669, + 0.007929063282050248, + -0.264474981985369 + ], + [ + -4.436766104011945e-05, + -0.0079290729483039, + 0.9999685634227164, + 0.7314656114613278 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] + ], + "candidate_transform_inverse_error_norm": 1.3597553244868544e-16, + "l_I_engineering_m": null, + "T_RTK_IMU": null, + "T_IMU_RTK": null, + "transform_convention": { + "equation": "p_RTK = R_RTK_IMU * p_IMU + t_RTK_IMU", + "translation": "t_RTK_IMU = -R_RTK_IMU * l_I", + "RTK_origin": "ANT1 phase center" + }, + "rotation_source": "R2G_gravity_level_prior", + "translation_conditional_on_rotation": true, + "evidence": { + "calibration_path": "artifacts\\rtk_imu_calibration_v3\\mechanical_prior_engineering_47_window.json", + "heldout_postfit_path": "artifacts\\rtk_imu_calibration_v3\\mechanical_prior_engineering_heldout.json", + "innovation_path": "artifacts\\rtk_imu_calibration_v3\\heldout_independent_innovation.json", + "sensitivity_path": "artifacts\\rtk_imu_calibration_v3\\mechanical_prior_rotation_sensitivity.json", + "convergence_retry_path": "artifacts\\rtk_imu_calibration_v3\\heldout_nonconverged_retry.json", + "propagation_root_cause_path": "artifacts\\rtk_imu_calibration_v3\\propagation_bias_root_cause_audit.json", + "posterior_prior_variance_ratio": [ + 0.9841028247436912, + 0.9831529061304226, + 0.9921155953185713 + ], + "heldout_convergence_after_retry": 1.0, + "rotation_sensitivity_summary": { + "max_abs_delta_l_xyz_m": [ + 0.0024980444199615426, + 0.0006588674774769543, + 0.0007172660986609625 + ], + "max_delta_l_norm_m": 0.0025582764807350012, + "max_transform_translation_delta_norm_m": 0.00686033304738171 + } + } +} diff --git a/artifacts/rtk_imu_calibration_v3/heldout_independent_innovation.json b/artifacts/rtk_imu_calibration_v3/heldout_independent_innovation.json new file mode 100644 index 0000000..d60d9bc --- /dev/null +++ b/artifacts/rtk_imu_calibration_v3/heldout_independent_innovation.json @@ -0,0 +1,22231 @@ +{ + "scope": "267-window independent held-out prediction innovations", 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0.26626218145019825, + "passed": true + } + }, + "independent_propagation_validation_passed": false, + "independent_extrinsic_sensitive_validation_passed": true, + "engineering_translation_accepted": false, + "acceptance_modified_by_this_audit": false +} diff --git a/docs/rtk_imu_calibration.md b/docs/rtk_imu_calibration.md index 4ef0f68..1037691 100644 --- a/docs/rtk_imu_calibration.md +++ b/docs/rtk_imu_calibration.md @@ -1,72 +1,168 @@ -# RTK–IMU 独立标定链 +# RTK–IMU 标定链路审计与当前方案 -本分支将 RTK–IMU 标定与原 LiDAR–IMU Phase-A/B/C 隔离。只复用 SO(3)/SE(3)、IMU 数据契约和预积分数学，不复用 LiDAR 命名的结果对象或门限。 +> 2026-08-21 多源原始报文重构、R1b/R2V/R2G/R3 实现与三批数据实测结果， +> 见 [rtk_imu_multisource_v3.md](rtk_imu_multisource_v3.md)。本页保留旧式 +> GGA/GNHPR 链路的审计背景；完整 HPR 手眼仍只允许作为诊断。 -## 坐标与外参约定 +## 结论 -唯一输出约定为 `p_RTK = R_RTK_IMU p_IMU + t_RTK_IMU`，即 `T_RTK_IMU` 把 IMU 坐标变换到 RTK 传感器坐标。杆臂 `l_IMU_to_RTK_in_IMU` 是“IMU 原点指向 RTK 定位参考点”的向量，表达在 IMU 坐标中，因此 `t_RTK_IMU = -R_RTK_IMU @ l_IMU_to_RTK_in_IMU`。 +当前数据可以验证“双天线基线方向与 IMU 的一致性”，但不能单独标定一个无歧义的完整三自由度 RTK 姿态外参。 -世界坐标采用局部 ENU。GGA 的第一个有效固定解作为每会话 ENU 原点。GGA 高程在局部差分中使用；因为设备输出的高程基准尚未单独核验，Z 方向使用更大的噪声并执行独立门控。 +G90 的 heading 表示主天线 ANT1 到从天线 ANT2 的基线方位。实车中主天线在左、从天线在右，因此该基线指向车体右侧，不是前进方向。IMU 的 +Y 指向车前；结合静止重力数据支持 IMU +Z 向上，可得到安装先验：IMU +X 指向车右，和 ANT1→ANT2 同向。 -当前 GNHPR 解释候选把 heading 看作从真北顺时针，pitch 看作机头上仰为正，并按 Z-Y-X 生成 `R_ENU_RTK`。代码同时检查 heading/pitch 的反号候选。固定的 RTK 轴置换不能由手眼残差自行识别，因此 `frame_definition` 和 `reference_point` 为空时，最终状态必须是 diagnostic。 +双天线只能观测这根基线的方位和仰角，绕基线自身的旋转不可观。GNHPR pitch 是横向基线的仰角，更接近车体横滚响应，不能当作坡道上的纵向车体 pitch；roll 字段也不是独立的第三姿态观测。因此，旧链路把 HPR 拼成完整 SO(3) 再做三轴手眼，是坡道 RMS、会话牵引和不稳定 yaw/pitch 的主要来源。 -## 数据与时间规则 +当前实现保留旧式完整 HPR 结果作为诊断量，但不允许它通过完整旋转门禁；平移在完整旋转通过前被硬冻结。 -- IMU CSV：`t,gx,gy,gz,ax,ay,az`，陀螺为 rad/s，加速度为 m/s²。 -- RTK CSV：位置使用 GGA，姿态使用 GNHPR；仅接受位置质量 4/5、姿态质量 4/5。 -- RTK 的 `t` 必须是 NMEA 测量 UTC 经 IMU device→host affine clock 反变换得到的 IMU 设备时间。禁止回退到延迟约 3–4 s 的主机接收时间。 -- 姿态使用 `hpr_measurement_utc_s` 恢复的 HPR 测量时间，不挂在最近邻 GGA 时刻。这一修正把 `motion_sms` 的旋转 RMS 从约 4.60° 降到约 1.37°。 -- 残余时间偏移通过 RTK/IMU 角速度模长相关扫描。相关峰不唯一时保持 0 s，并把候选峰只作为诊断值。 +## 坐标系和参考点 -## 求解方法 - -R0 数据和时间审计：检查固定解比例、共同时间范围、HPR 有效性、ENU 原点和残余时间偏移。`motion_sms` 自动只使用固定位置且有效姿态的连续段。 - -R1 旋转：对 0.75 s、1.5 s、3.0 s 相对运动构造 `Delta_R_RTK R_RTK_IMU = R_RTK_IMU Delta_R_IMU`。IMU 侧采用陀螺预积分。先用无偏置闭式手眼解筛选四种 GNHPR 符号候选，只对最优候选联合优化共享旋转和每会话常值陀螺偏置。输出 RMS/median/P95、边缘协方差、信息奇异值、逐会话 RMS 和固定偏置 conditional LOO。 - -T1 杆臂和平移：不对 RTK 位置做二次差分。固定 R1 的旋转和陀螺偏置，以可配置节点构造 IMU 位置/速度预积分因子，联合求解跨会话共享杆臂、每节点速度和每会话常值加速度偏置。输出边缘协方差、信息奇异值/精度秩、位置/速度残差和 LOO。上游旋转未通过时，平移无条件标为 diagnostic。 - -## 代码结构 - -- `imu_lidar/geodesy.py`：WGS84 → ECEF → 局部 ENU。 -- `imu_lidar/rtk_io.py`：RTK CSV、质量掩码、GGA/HPR 独立测量时刻。 -- `imu_lidar/rtk_attitude.py`：GNHPR 候选约定与 SO(3) 插值。 -- `imu_lidar/rtk_imu_rotation.py`：时间审计、旋转手眼、偏置、协方差、LOO。 -- `imu_lidar/rtk_imu_translation.py`：轻量全 IMU 预积分、杆臂线性因子系统、协方差、LOO。 -- `imu_lidar/rtk_imu_replay.py`：清单加载、端到端运行、结构化结果发布。 -- `tools/run_rtk_imu_calibration.py`：命令行入口，支持会话/批次筛选和 rotation-only。 -- `tests/test_rtk_imu_calibration.py`：ENU、heading 方向、HPR 时刻、预积分等价性测试。 - -公共 IMU 预积分的端点插值已从每小段扫描完整数组，改为当前相邻样本内的等价局部插值；公共 IMU CSV 加载器也改为先校验表头、再按列加载。完整项目测试通过。 - -## 当前结果（8 会话，2026-08-21） - -结果目录：`artifacts/rtk_imu_calibration_v1/all_sessions`。 +统一输出约定： ```text -RPY(R_RTK_IMU) = [0.102824, -0.145072, -1.432698] deg -rotation RMS / median / P95 = 1.627683 / 0.569594 / 3.090395 deg -rotation std = [0.234013, 0.200486, 2.314692] deg -max conditional rotation LOO = 5.144191 deg（去掉 priority_175910_180530） - -t_RTK_IMU = [0.771094, 0.569302, -21.073037] m -translation std = [0.367658, 0.367640, 2.960859] m -position RMS XYZ = [0.002606, 0.002617, 0.000871] m -velocity RMS XYZ = [1.034408, 1.001846, 0.081893] m/s -max translation LOO Z = 1.774476 m（去掉 loop_194223_195003） +p_RTK = R_RTK_IMU p_IMU + t_RTK_IMU ``` -预期 GNHPR 符号候选胜出，预筛 RMS 1.6313°；pitch 反号为 1.7376°，heading 反号候选为 7.2633°/17.7163°。heading 方向基本可判定，但固定 RTK 轴定义仍未判定。 +RTK 车固坐标定义： -平移明显不具机械真实性。很小的位置残差来自节点速度自由度和鲁棒权重吸收，不能抵消约 1 m/s 的水平速度残差、米级标准差与巨大 LOO 变化。该候选禁止写入配置。 +- +X：ANT1（主天线、左侧）→ ANT2（从天线、右侧），指向车右。 +- +Y：车辆前进方向，与 IMU +Y 同向。 +- +Z：车辆上方；静止重力数据支持 IMU +Z 向上。 +- 该完整三轴定义包含机械安装先验；GNHPR 实际直接观测的只有 +X 基线。 -逐日期旋转：0808 为 `[-0.232660, -0.039716, -1.563562]°`、RMS 2.121957°；0815 为 `[0.188773, -0.139741, -0.582331]°`、RMS 0.942835°。两批 yaw 相差约 0.98°，但 yaw 自身标准差约 3.60°/2.57°，不能据此声称安装发生变化。 +位置参考点： -## 当前阻断项与下一步 +- GGA 为 ANT1/主天线相位中心。 +- 相位中心离地高度为 1.916499878 m。 -1. RTK `frame_definition` 和 `reference_point` 为空。必须从协议、天线安装方向和原 LiDAR–RTK 手眼结果确认 heading 物理轴、pitch/roll 旋转轴及 GGA 相位中心。 -2. 残余时间候选约 +0.04 s，但峰宽且次峰接近，当前按门限固定为 0 s。需要独立高动态数据或同步边沿确认。 -3. `priority_175910_180530` 的 conditional LOO 牵引达到 5.14°、自身 RMS 3.10°，应检查其 HPR 突跳、IMU 丢帧、动态挠曲和安装状态。 -4. 坡道会话 RMS 2.39°，说明 pitch 数据或姿态轴/符号仍可能不完整。 -5. 平移水平速度因子 RMS 约 1 m/s、Z 杆臂漂到约 21 m。应先冻结平移，完成 RTK 帧和旋转验证，再检查加速度轴、重力符号、高程基准与噪声模型。 -6. 只有旋转、时间和 RTK 帧定义全部通过后，才重新运行平移并做严格的全 nuisance LOO/留出验证。 +## 求解链路 + +### R0：协议、数据质量和时间 + +1. 校验 NMEA checksum。 +2. GGA 位置只接收 Q=4 固定解。 +3. GNHPR 姿态只接收 Q=4 固定解；Q=5 浮点解仅进入诊断统计，不参与标定。 +4. 保留 GNHPR 自己的卫星数、差分龄期和基站号，不能再用 GGA 卫星数替代 HPR 质量。 +5. 姿态使用 `hpr_measurement_utc_s` 映射后的 IMU 设备时间。 +6. 遇到 HPR 无效、浮点、时间间隔异常或基线跳变时切断连续段，运动对不得跨断点。 + +### T0：残余时间偏移 + +只使用可观测的有符号 heading 角速度与 IMU `gyro_z` 做相关扫描。角速度模长会混入不可观的绕基线旋转，不再作为时间审计依据。 + +时间偏移只用于初始化/诊断。只有相关峰足够高、与次峰分离且峰宽足够窄时才应用，否则保持 0 s。 + +### R1：双天线基线一致性 + +对每个连续固定解片段构造 0.75 s、1.5 s、3.0 s 的相对运动。以安装先验 `u_IMU=[1,0,0]` 检验： + +```text +angle(u_RTK(t0), u_RTK(t1)) + ≈ angle(u_IMU, ΔR_IMU(t0,t1) u_IMU) +``` + +该标量约束不虚构 RTK 的前向轴和上向轴。输出整体、逐会话 RMS/P95、分轴诊断及最坏时间区间。所有会话的总权重归一，避免高激励或样本更多的会话支配结果。 + +### R2：旧式完整 HPR 诊断 + +为兼容历史输出，将基线补成零 roll 的数学坐标架，再运行完整手眼。这个结果仅用于暴露符号错误、异常会话和旧结果变化，不能作为可交付外参。 + +LOO 删除一个会话后，会重新优化剩余会话的陀螺零偏，不再固定全量数据的 nuisance 参数。 + +### T1：平移 + +只有完整三自由度旋转“可观且通过”时，才允许进入杆臂和平移求解。当前条件不满足，因此： + +- 不运行平移优化； +- 不输出 `translation_result.json`； +- `t_RTK_IMU_m` 和 `T_RTK_IMU` 为 null； +- 历史巨大 Z、米级不确定度和约 1 m/s 速度残差不再消耗优化时间。 + +## 当前 8 会话结果 + +结果目录：`artifacts/rtk_imu_calibration_v2/all_sessions`。 + +```text +可观测基线一致性： +RMS / median / P95 = 0.593665 / 0.146040 / 1.212972 deg +priority_175910 RMS / P95 = 1.316292 / 2.472570 deg +slope RMS / P95 = 1.264514 / 2.475899 deg +最坏区间 = priority_175910，约 5.084 deg +baseline gate = passed + +时间偏移： +全局候选 = +0.005 s +峰值相关 = 0.909019 +近峰宽度 = [-0.110, +0.170] s +实际应用 = 0 s + +旧式完整 HPR 诊断： +RPY = [-0.132765, -0.297433, -1.558358] deg +RMS / P95 = 1.172397 / 2.404977 deg +std = [0.615416, 0.437419, 3.687145] deg +priority_175910 re-optimized LOO = 1.698555 deg +max re-optimized LOO = 1.699427 deg（删除 slope） +legacy numeric gate = failed +full attitude observable = false + +平移： +frozen / not run +``` + +旧结果中的 `priority_175910` 条件 LOO 为 5.14°。严格剔除 Q5、会话等权、断段保护以及 LOO 重估零偏后，该会话完整诊断 LOO 降为 1.699°。它和坡道会话仍是主要异常源，但现在异常集中在基线仰角通道，而不是 heading：这更符合原始 HPR 中 Q5、低卫星数和 pitch 大幅波动的事实。 + +## 已排除或仍存在的漏洞 + +- 已修复：heading 误当车前方向。 +- 已修复：GNHPR pitch 误当纵向车体 pitch。 +- 已修复：不可观 roll 注入完整姿态。 +- 已修复：Q5 浮点 HPR 进入标定。 +- 已修复：HPR 质量字段在导出时丢失。 +- 已修复：相对运动跨越无效段或跳变。 +- 已修复：样本多的会话权重过大。 +- 已修复：LOO 固定全量会话零偏。 +- 已修复：时间相关使用三轴角速度模长。 +- 已修复：旋转未通过仍继续优化平移。 +- 仍存在：当前运动不能稳定地从 GGA 速度补全车前/车上方向。 +- 仍存在：基线绕轴自由度没有独立传感器观测。 +- 仍存在：`priority_175910` 和坡道的基线仰角存在局部异常。 + +## 如何得到可交付的完整旋转 + +按优先级建议： + +1. 采一组专用数据：空旷区域、全程 RTK fixed、较长直线加减速、左右转、坡道上下行，保留原始 GGA/GNHPR/IMU 时间和全部质量字段。 +2. 用高质量前向速度补第二根轴。只在速度足够高、航向变化平缓的区间用 GGA course，并在同一优化中建模 ANT1 杆臂、非完整车辆侧向速度约束和时间偏移。 +3. 用静止重力补上向轴时，必须把“地面水平/车辆静止”写成显式先验，并将结果标记为安装先验约束解，而不是双天线数据独立解。 +4. 若能取得 G90 内部融合后的完整 INS 姿态、第三天线、轮速/转角或可靠车体姿态源，优先作为第二独立方向。 +5. 完整旋转通过留出验证后，才恢复平移；平移应同时估计杆臂、速度、加计偏置，并检查垂向高程基准。 + +当前数据上尝试用平滑 GGA 速度直接补全姿态，结果随平滑窗口明显变化，受低速、转弯杆臂和高程差分噪声影响，不能进入正式结果。 + +## 与主流开源方法的对应 + +- Kalibr：角速度相关适合作为时间偏移初始化，不应在宽峰时强行采用候选值。 +- iKalibr：采用连续时间轨迹联合估计时空参数，并强调充分激励；适合后续专用数据。 +- MINS：异步测量插值并把传感器外参、时间和 nuisance 状态一起估计。 +- GICI-LIB：因子图中显式进行 GNSS/INS 初始化、质量控制和异常值处理。 + +本项目暂不直接引入这些大型框架，而是吸收其原则：先保证物理可观测性和数据质量，再进行联合优化；不能用自由状态吸收错误模型。 + +参考： + +- Unicore N4 Reference Commands Manual +- Unicore UM982 User Manual +- https://github.com/Unsigned-Long/iKalibr +- https://github.com/ethz-asl/kalibr +- https://github.com/rpng/MINS +- https://github.com/chichengcn/gici-open + +## 代码入口 + +- `imu_lidar/rtk_attitude.py`：GNHPR 基线语义及零 roll 数学补全。 +- `imu_lidar/rtk_io.py`：RTK CSV、Q4/Q5 和 checksum 质量门禁。 +- `imu_lidar/rtk_imu_rotation.py`：时间审计、连续段、基线审计、旧式诊断和重优化 LOO。 +- `imu_lidar/rtk_imu_replay.py`：坐标定义、参考点、平移冻结和 JSON 输出。 +- `tools/rscap_v2/g90_rtk.py`：原始 GNHPR 解析及质量字段。 +- `tools/export_g90_rtk_to_sessions.py`：GNHPR 质量字段导出。 +- `tools/run_rtk_imu_calibration.py`：端到端命令行入口。 +- `tests/test_rtk_imu_calibration.py`：轴定义、Q4/Q5、时间和预积分回归测试。 diff --git a/docs/rtk_imu_engineering_6dof.md b/docs/rtk_imu_engineering_6dof.md new file mode 100644 index 0000000..08f587b --- /dev/null +++ b/docs/rtk_imu_engineering_6dof.md @@ -0,0 +1,108 @@ +# RTK–IMU Engineering 6DoF 分支 + +该分支与 V3 `data_only` 严格链路并列，不改变其门禁结论： + +```text +data_only_6dof_accepted = false +``` + +工程分支固定使用真实水平静止场地得到的 R2G 旋转，默认参考值为 +`RPY=[0.4543066, -0.0026392, 0.0122384] deg`，输出始终记录： + +```text +rotation_source = R2G_gravity_level_prior +translation_conditional_on_rotation = true +``` + +## 条件模型 + +估计量是 `l_I = p_ANT1^I`。每个连续质量段使用 HI13 设备时间、raw gyro/acc 动态预积分，融合 RTK Fixed 的 GGA/BESTNAVA 位置、BESTNAVA Doppler velocity 和 Q4 GNHPR 的 ANT1→ANT2 基线。逐段 nuisance state 包含初始姿态、IMU 原点初始位置、速度、gyro bias 和 acc bias。HI13 absolute quaternion/RPY（尤其 absolute yaw）不进入机械外参因子；R2V 仅作诊断。 + +静止相关因子分为两类： + +- `gravity_candidate`：连续约 1.5 s 的低角速度、gyro/acc 方差稳定且加速度模长接近重力；它不能等价为静止。 +- `zupt_static`：在 gravity candidate 基础上，必须同时有至少约 1 s 的连续 BESTNAVA Doppler 速度接近 0。高速或匀速直线不会加入 ZUPT。 + +连续段复用 R0 质量断点：checksum 无效、定位非 Fixed、GNHPR 非 Q4、设备时间回跳、HPR 测量间隙、baseline jump、位置测量间隙或 IMU gap 都会切段，禁止跨断点预积分。 断后片段还必须至少包含 6 个求解节点且持续不少于 5 s；更短的局部欠约束微段不会跨断点拼接，而是直接不进入杆臂优化。 + +## 高程与残差口径 + +- GGA 只约束 XY；GGA MSL altitude 不定义也不参与 ENU-Z。 +- ENU-Z reference 只来自有效 Fixed BESTNAVA altitude；BESTNAVA 才约束 XYZ。 +- 输出分别为 `gga_xy_residual`、`bestnava_xyz_residual` 和 `doppler_velocity_residual`。未进入 Z factor 的 GGA 高度不进入垂向残差统计。 + +## 可观性与验收 + +杆臂可观性不再使用全状态最小奇异向量。状态分为杆臂 `l` 与 nuisance state，对优化 Hessian 计算 Schur complement： + +```text +H_l_marg = H_ll - H_ln pinv(H_nn) H_nl +``` + +只对该 3×3 marginal lever information 做 SVD，并输出： + +- `l_I_marginal_covariance_m2`、`l_I_std_m` +- `lever_information_singular_values` +- `lever_information_condition_number` +- `lever_precision_rank` +- `weakest_lever_direction_I` + +门禁分为两层： + +- `solver_health_gates` 只判断优化是否收敛、数值是否有限且残差未发散。 +- `engineering_acceptance_gates` 使用更严格的杆臂 marginal std/information/rank/condition、BESTNAVA XYZ、GGA XY、Doppler velocity、LOO、bootstrap、旋转敏感性和可选手量一致性。 + +任一核心门禁失败时，`engineering_6dof_accepted=false`。没有完整执行 bootstrap 和 18 组旋转敏感性时，两项门禁明确为 false，不会把阶段性 base/LOO 结果误标为正式放行。 + +Bootstrap 按 session 有放回抽样，并保留重复 session 的 multiplicity；重复抽中的 session 会重复贡献其全部连续段。 + +## 机械杆臂软先验与双解输出 + +`--manual-l-i-m` 仅在同时提供 `--manual-l-i-std-m` 或完整 +`--manual-l-i-covariance-m2` 时才成为白化高斯软因子。求解器始终先运行无先验 +`free_solution`，再运行 `prior_constrained_solution`；输出还包含机械参考及两者到 +参考的差值。当前活动 engineering 解为 prior-constrained 解（若启用），但 acceptance +额外要求 free-solve 也与机械参考一致，软先验不能掩盖不可观或数据矛盾。 + +示例（数值需使用实际机械测量的 1σ，不可把示例值当作默认）： + +```powershell +--manual-l-i-m -0.45072 -0.25682 0.73208 ` +--manual-l-i-std-m +``` +## 坐标转换 + +```text +T_RTK_IMU: p_RTK = R_RTK_IMU p_IMU - R_RTK_IMU l_I +T_IMU_RTK: p_IMU = R_RTK_IMU^T p_RTK + l_I +``` + +RTK 原点是 ANT1，因此 `T_IMU_RTK.translation == l_I`，两矩阵必须互逆。 + +## 分阶段运行 + +第一阶段默认只运行 base fit、marginal observability、residual audit 和 LOO： + +```powershell +python tools/run_rtk_imu_engineering_6dof.py ` + --manifest D:\data\rtk_imu_unified_v3\manifest.json ` + --output artifacts/rtk_imu_calibration_v3/engineering_6dof_base_loo.json ` + --session ` + --session ` + --session +``` + +只有 base/LOO 合理后，才显式启动全量验证： + +```powershell +python tools/run_rtk_imu_engineering_6dof.py ` + --manifest D:\data\rtk_imu_unified_v3\manifest.json ` + --output artifacts/rtk_imu_calibration_v3/engineering_6dof_full.json ` + --session ` + --session ` + --session ` + --run-bootstrap --bootstrap-repetitions 40 --bootstrap-seed 0 ` + --run-rotation-sensitivity +``` + +正式会话应覆盖直行加减速、左右转、坡道和明确水平静止段。不得为了运行时间降低验收门禁或把未完成验证标为成功。 \ No newline at end of file diff --git a/docs/rtk_imu_multisource_v3.md b/docs/rtk_imu_multisource_v3.md new file mode 100644 index 0000000..0e3a061 --- /dev/null +++ b/docs/rtk_imu_multisource_v3.md @@ -0,0 +1,175 @@ +# RTK–IMU 多源原始数据与旋转标定 V3 + +## 可行性结论 + +重构方向正确且必要。原始捕获中确实存在旧导出链路忽略的 BESTNAVA、 +PVTSLNA 和 HI13 姿态/四元数。它们可以补充速度、质量、绝对姿态和静止 +重力约束，但本批数据尚不能通过完整旋转门禁： + +- BESTNAVA 约 0.8–1 Hz，严格高速样本很少。 +- 当前动态主要是平面 yaw，R1b 的两个倾斜自由度仍弱可观。 +- HI13 四元数与 GNSS 真北存在稳定但非机械的 yaw 偏差，可能来自磁偏角或 + 内部导航融合。 +- 两个明确水平静止会话的 R2G 很稳定，但它是水平/重力先验约束解。 + +因此，本次重构显著提高了诊断能力，也防止错误外参进入平移；它没有把信息 +不足包装成“标定成功”。 + +## 三批原始数据 + +扫描根目录： + +- 0808：D:\data\raw_serial_capture_v2，仅选 20260808。 +- 0815：D:\data\0815\raw_serial_capture_v2；目录实际包含 20260812–14。 +- 0819：D:\data\0819\raw_serial_capture_v2。 + +按文件名捕获开始时间在 1.5 s 内一一配对： + +| 批次 | G90/HI13 配对 | GNHPR | BESTNAVA | PVTSLNA | Q4 HPR | Fixed Doppler | +|---|---:|---:|---:|---:|---:|---:| +| 0808 | 18 | 46,180 | 8,291 | 5,993 | 45,606 | 8,128 | +| 0815 | 28 | 96,241 | 12,660 | 9,638 | 27,045 | 3,385 | +| 0819 | 9 | 15,717 | 2,029 | 1,427 | 14,976 | 1,930 | + +共完成 55 组统一导出。0815 有一组 G90 没有匹配 HI13，manifest 将其列为 +unmatched。发现一条 NMEA 时间为 0913A：保留原始行，设备时间留空并隔离， +没有回退到 host receive time。 + +速度不等于高速激励。增加速度 ≥1.5 m/s、水平速度标准差 ≤0.25 m/s 后， +三个批次分别只剩 44、8、0 条候选。 + +## 统一导出 + +输出根目录：D:\data\rtk_imu_unified_v3。 + +每组捕获： + +- imu.npz：HI13 system_time、gyro、accel、RPY、WXYZ quaternion、磁场、 + PPS stamp、温度、气压和 host receive UTC。 +- rtk.csv：每条原生异步 GGA/GNHPR/BESTNAVA/PVTSLNA 单独成行，不再把 + HPR/BEST/PVT 最近邻挂到 GGA。 +- export_summary.json：原始捕获摘要、报文数量、四元数范数和设备到 host + 的仿射时钟诊断。 +- manifest.json：全部会话、批次、目录和 unmatched 记录。 + +时间规则： + +1. HI13 system_time 是 IMU 主时间轴。 +2. BESTNAVA/PVTSLNA 使用 GNSS week/TOW 和 leap seconds。 +3. GGA/GNHPR 使用报文自己的 UTC time-of-day。 +4. RTK GNSS 测量时刻通过 HI13 device→host 仿射模型映射进 HI13 设备时钟。 +5. host receive time 只用于跨时钟桥接、延迟和抖动诊断，绝不替代采样时刻。 +6. checksum 无效、时间畸形或非固定解记录保留，但不进入求解。 + +## 新旋转链路 + +### R1b：基线在 IMU 中的 2DoF 方向 + +对连续 Q4 GNHPR 基线和 HI13 陀螺相对旋转使用不变量： + + angle(b_ENU(t0), b_ENU(t1)) + = angle(b_IMU, DeltaR_IMU b_IMU) + +估计 b_IMU 的两个倾斜自由度及逐会话陀螺零偏。安装信息只用于选择 +X +半球，并施加明确记录的弱 20° 先验。按会话等权，输出残差、协方差和信息 +奇异值。 + +### R2V：基线 + Doppler velocity + +筛选条件： + +- BESTNAVA position 为 SOL_COMPUTED/NARROW_INT； +- velocity 为 SOL_COMPUTED/DOPPLER_VELOCITY； +- checksum 有效； +- 水平速度 ≥1.5 m/s、速度标准差 ≤0.25 m/s； +- |IMU gyro_z| ≤3°/s； +- 速度方向与横向基线接近正交； +- GNHPR Q4，并有时间邻近的合法 HI13 quaternion。 + +基线给车右，Doppler velocity 给车前，叉积给车上。HI13 quaternion 的 +body/world 和 ENU/NED 候选全部评分，只保留残差最小者。该方法会把 HI13 +导航 yaw 偏差带入候选，因此必须和 R2G 交叉验证。 + +### R2G：基线 + 水平静止重力 + +只允许调用方明确标记的水平静止会话。本次使用： + +- 0819_20260819_072130（flat_static_hdg207） +- 0819_20260819_073045（flat_static_hdg082） + +每 10 s 分块，要求 gyro norm ≤0.35°/s，且加速度模长距标准重力不超过 +0.15 m/s²。R1b 提供车右，加速度中值提供车上，叉积得到车前。 + +### R3：稳定性与正式门禁 + +R2V/R2G 都输出： + +- 样本和会话数量； +- SO(3) RMS/P95； +- 旋转向量样本标准差和均值协方差； +- leave-one-session； +- 10 样本或 10 s block-out。 + +只有 R1b 可观、R2V 和 R2G 各自稳定、二者差异 ≤2° 时，才将 +translation_unlocked 设为 true。旧 GNHPR 三轴手眼不参与正式门禁。 + +## 首轮实测 + +R1b： + + b_IMU = [0.999999976, -0.000213959, -0.000044368] + tilt_yz = [-0.0123, -0.0025] deg + pairs = 2091 + RMS / P95 = 0.8086 / 1.7105 deg + information singular values = [1.176e-2, 8.476e-4] + std = [27.77, 7.47] deg + gate = failed + +点估计接近 +X 是安装先验与名义轴一致的结果；巨大协方差说明不能宣称 +数据独立估出了这两个小角。 + +R2V： + + qualifying samples / sessions = 41 / 2 + selected quaternion convention = HI13_q_body_to_ENU + RPY = [0.7532, 0.0708, -9.2944] deg + RMS / P95 = 1.8436 / 3.4975 deg + max leave-one-session = 3.4519 deg + gate = failed + +R2G： + + level-static blocks / sessions = 53 / 2 + RPY = [0.4543, -0.0026, 0.0122] deg + RMS / P95 = 0.2542 / 0.2732 deg + max leave-one-session = 0.2688 deg + gate = passed (level/gravity-prior constrained) + +R2V 与 R2G 的 SO(3) 差异为 9.3119°。R3 最终： + + full_rotation_accepted = false + translation_unlocked = false + +## 下一轮数据要求 + +1. BESTNAVA 改为至少 10 Hz，并确认 Doppler velocity 与 GNHPR 使用同一 + GNSS week/TOW 输出周期。 +2. 每个日期都录制多段 ≥3 m/s、持续 20–30 s 的正向直线；包含不同方位， + 避免单一磁环境和单一会话支配。 +3. 为 R1b 增加可控的 roll/pitch 激励；只有平面 yaw 无法稳定估出横向 + 基线的两个微小倾斜角。 +4. 每个日期至少录两种车头方位的明确水平静止段，检验 HI13 重力和绝对 + quaternion 的跨日期稳定性。 +5. 若 HI13 quaternion 的 yaw 来自磁融合，应获取其导航坐标定义、磁偏角 + 设置和融合状态；否则 R2V 只使用其 roll/pitch，yaw 由 GNSS 基线和速度 + 决定。 +6. 上述门禁通过前继续冻结杆臂和平移。 + +## 实现入口 + +- tools/rscap_v2/g90_rtk.py：GGA/GNHPR/BESTNAVA/PVTSLNA 和双 checksum。 +- tools/rscap_v2/hi13_imu.py：HI91 system_time、惯性、姿态和四元数。 +- tools/export_rtk_imu_unified.py：55 组配对、原生异步导出和断点续导。 +- imu_lidar/rtk_imu_multisource.py：R1b/R2V/R2G/R3。 +- tools/run_rtk_imu_multisource.py：多源旋转命令行入口。 +- artifacts/rtk_imu_calibration_v3/multisource_result.json：首轮 R3 结果。 diff --git a/imu_lidar/rtk_attitude.py b/imu_lidar/rtk_attitude.py index ce299ef..4308479 100644 --- a/imu_lidar/rtk_attitude.py +++ b/imu_lidar/rtk_attitude.py @@ -1,4 +1,4 @@ -"""GNHPR attitude conventions and SO(3) interpolation.""" +"""GNHPR dual-antenna baseline conventions and SO(3) interpolation.""" from __future__ import annotations @@ -9,12 +9,14 @@ from scipy.spatial.transform import Rotation, Slerp @dataclass(frozen=True) class GnhprConvention: - """Interpretation of GNHPR angles as ``R_ENU_RTK``. + """Interpretation of the GNHPR ANT1-to-ANT2 baseline. - Heading is normally clockwise from north. With ENU and an x-forward RTK - frame this becomes yaw ``90 deg - heading``. Aircraft-positive pitch is - nose-up, which is the negative mathematical Y rotation in an FLU frame. - Alternative signs are retained for empirical protocol validation. + Heading is clockwise from north. In this vehicle the RTK ``+X`` axis is + the baseline from the main/left antenna (ANT1) to the secondary/right + antenna (ANT2), so it points to vehicle right rather than vehicle forward. + GNHPR pitch is the elevation of that baseline. A two-antenna receiver + cannot observe rotation about the baseline; the reported roll is therefore + not treated as a third independent attitude measurement. """ name: str @@ -23,7 +25,7 @@ class GnhprConvention: roll_sign: float = 1.0 -EXPECTED_GNHPR = GnhprConvention("north_cw__pitch_nose_up__roll_right_down") +EXPECTED_GNHPR = GnhprConvention("ant1_to_ant2__north_cw__elevation_up") GNHPR_CANDIDATES = ( EXPECTED_GNHPR, GnhprConvention("north_cw__pitch_opposite", -1.0, 1.0, 1.0), @@ -38,7 +40,12 @@ def gnhpr_to_rotation_enu_rtk( roll_deg: np.ndarray, convention: GnhprConvention = EXPECTED_GNHPR, ) -> np.ndarray: - """Build body-to-ENU matrices with an extrinsic Z-Y-X Euler sequence.""" + """Build a zero-roll mathematical completion of the baseline frame. + + Only the first column (the ANT1-to-ANT2 unit vector) is physically observed + by GNHPR. The remaining columns are a convenient gauge completion and must + not be used as a measured full vehicle attitude. + """ heading = np.asarray(heading_deg, dtype=float).reshape(-1) pitch = np.asarray(pitch_deg, dtype=float).reshape(-1) @@ -47,11 +54,36 @@ def gnhpr_to_rotation_enu_rtk( raise ValueError("heading, pitch and roll must have equal length") yaw_rad = np.deg2rad(90.0 + convention.heading_sign * heading) pitch_rad = np.deg2rad(convention.pitch_sign * pitch) - roll_rad = np.deg2rad(convention.roll_sign * roll) + # A dual-antenna baseline has no independent roll observation. Keep the + # argument for wire-format compatibility, but never inject it into SO(3). + roll_rad = np.zeros_like(roll) angles = np.column_stack([yaw_rad, pitch_rad, roll_rad]) return Rotation.from_euler('ZYX', angles).as_matrix() +def gnhpr_to_baseline_enu( + heading_deg: np.ndarray, + pitch_deg: np.ndarray, + convention: GnhprConvention = EXPECTED_GNHPR, +) -> np.ndarray: + """Return ANT1-to-ANT2 unit vectors expressed in ENU. + + The result is independent of the unobservable GNHPR roll field. + """ + + heading = np.asarray(heading_deg, dtype=float).reshape(-1) + pitch = np.asarray(pitch_deg, dtype=float).reshape(-1) + if heading.size != pitch.size: + raise ValueError("heading and pitch must have equal length") + azimuth = np.deg2rad(heading) + elevation = np.deg2rad(-convention.pitch_sign * pitch) + horizontal = np.cos(elevation) + east = np.sin(azimuth) * horizontal + north = np.cos(azimuth) * horizontal + up = np.sin(elevation) + return np.column_stack([east, north, up]) + + def interpolate_rotations( source_t_s: np.ndarray, rotations: np.ndarray, diff --git a/imu_lidar/rtk_imu_engineering.py b/imu_lidar/rtk_imu_engineering.py new file mode 100644 index 0000000..a190842 --- /dev/null +++ b/imu_lidar/rtk_imu_engineering.py @@ -0,0 +1,1208 @@ +"""Conditional engineering RTK--IMU 6DoF calibration. + +The data-only V3 gates remain unchanged. This branch estimates the ANT1 lever +arm only after an R2G gravity/level rotation is explicitly fixed. HI13 +absolute attitude and quaternion yaw are never used as mechanical constraints. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Iterable, Sequence + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +from .geometry import make_transform, so3_exp +from .imu_preintegration import apply_bias_correction_imu, preintegrate_imu +from .rtk_attitude import gnhpr_to_baseline_enu +from .rtk_imu_multisource import UnifiedSession, _f, _nearest_index, _truth + +G0 = 9.80665 +G_ENU = np.array([0.0, 0.0, -G0]) +EARTH_RADIUS_M = 6_378_137.0 +NUISANCE_DOF_PER_SEGMENT = 15 +LEGACY_DETERMINISTIC_SEGMENT_MODEL_DEPRECATED = True +LEGACY_DEPRECATION_REASON = ( + 'multi-horizon audit: 20 s position/velocity P95 grew 259.8x/13.4x ' + 'relative to 1 s re-anchored propagation' +) +MIN_SEGMENT_DURATION_S = 5.0 +MIN_SEGMENT_NODE_COUNT = 6 +DEFAULT_MANUAL_L_I_M = np.array([-0.45072, -0.25682, 0.73208]) +DEFAULT_MANUAL_L_I_COVARIANCE_M2 = np.diag([4e-4, 4e-4, 9e-4]) +FREE_MANUAL_MAHALANOBIS_99_DF3 = 11.345 + + +@dataclass(frozen=True) +class ResidualAudit: + count: int + axis_rms: np.ndarray + axis_p95_abs: np.ndarray + vector_rms: float + vector_p95: float + + +@dataclass(frozen=True) +class LeverFitSummary: + l_I_m: np.ndarray + l_I_marginal_covariance_m2: np.ndarray + l_I_std_m: np.ndarray + lever_marginal_information: np.ndarray + lever_information_singular_values: np.ndarray + lever_information_condition_number: float + lever_precision_rank: int + weakest_lever_direction_I: np.ndarray + gga_xy_residual: ResidualAudit + bestnava_xyz_residual: ResidualAudit + doppler_velocity_residual: ResidualAudit + optimizer_converged: bool + + +@dataclass(frozen=True) +class FixedLeverResidualSummary: + l_I_m: np.ndarray + gga_xy_residual: ResidualAudit + bestnava_xyz_residual: ResidualAudit + doppler_velocity_residual: ResidualAudit + optimizer_converged: bool + + +@dataclass(frozen=True) +class Engineering6DofResult: + rotation_source: str + translation_conditional_on_rotation: bool + R_RTK_IMU: np.ndarray + l_I_m: np.ndarray | None + l_I_marginal_covariance_m2: np.ndarray + l_I_std_m: np.ndarray + lever_marginal_information: np.ndarray + lever_information_singular_values: np.ndarray + lever_information_condition_number: float + lever_precision_rank: int + weakest_lever_direction_I: np.ndarray + gga_xy_residual: ResidualAudit + bestnava_xyz_residual: ResidualAudit + doppler_velocity_residual: ResidualAudit + session_count: int + segment_count: int + gga_factor_count: int + bestnava_factor_count: int + velocity_factor_count: int + gravity_candidate_factor_count: int + zupt_static_factor_count: int + leave_one_session_out_l_I_m: dict[str, np.ndarray] + leave_one_session_out_delta_m: dict[str, float] + bootstrap_l_I_mean_m: np.ndarray + bootstrap_l_I_std_m: np.ndarray + bootstrap_success_count: int + bootstrap_requested_count: int + rotation_sensitivity_delta_l_I_m: dict[str, np.ndarray] + rotation_sensitivity_max_delta_m: float + manual_l_I_m: np.ndarray | None + manual_l_I_delta_m: np.ndarray | None + solver_health_gates: dict[str, bool] + solver_health_passed: bool + engineering_acceptance_gates: dict[str, bool] + T_RTK_IMU: np.ndarray | None + T_IMU_RTK: np.ndarray | None + engineering_6dof_accepted: bool + blockers: tuple[str, ...] + notes: tuple[str, ...] + translation_prior_applied: bool + lever_prior_covariance_m2: np.ndarray | None + mechanical_reference_l_I_m: np.ndarray | None + free_solution: LeverFitSummary | None + prior_constrained_solution: LeverFitSummary | None + free_to_mechanical_delta_m: np.ndarray | None + prior_to_mechanical_delta_m: np.ndarray | None + free_translation_observable: bool + data_only_translation_accepted: bool + free_manual_mahalanobis_d2: float | None + free_translation_observability_gates: dict[str, bool] + posterior_to_prior_covariance_ratio: np.ndarray | None + free_to_prior_residual_delta: dict[str, float] | None + mechanical_reference_solution: FixedLeverResidualSummary | None + residual_comparison: dict[str, float] | None + + +@dataclass(frozen=True) +class _HprSeries: + t_s: np.ndarray + baseline_enu: np.ndarray + valid: np.ndarray + valid_indices: np.ndarray + + +@dataclass(frozen=True) +class _Node: + t_s: float + source: str + p_enu_m: np.ndarray + position_mask: np.ndarray + baseline_enu: np.ndarray + hpr_index: int + hpr_factor_valid: bool + hpr_factor_method: str + hpr_support_gap_s: float + hpr_angular_sigma_rad: float + position_time_discontinuity_before: bool + gyro_rad_s: np.ndarray + accel_m_s2: np.ndarray + velocity_enu_m_s: np.ndarray | None + gravity_candidate: bool + zupt_static: bool + continuity_id: int + + +@dataclass(frozen=True) +class _Segment: + segment_id: str + session_id: str + nodes: tuple[_Node, ...] + preintegrations: tuple[object, ...] + R_WRTK_initial: np.ndarray + + +def _empty_audit(dimension: int) -> ResidualAudit: + return ResidualAudit(0, np.full(dimension, np.nan), np.full(dimension, np.nan), np.nan, np.nan) + + +def _audit(values: Sequence[np.ndarray], dimension: int) -> ResidualAudit: + if not values: + return _empty_audit(dimension) + array = np.asarray(values, dtype=float).reshape(-1, dimension) + norm = np.linalg.norm(array, axis=1) + return ResidualAudit( + count=array.shape[0], + axis_rms=np.sqrt(np.mean(array**2, axis=0)), + axis_p95_abs=np.percentile(np.abs(array), 95.0, axis=0), + vector_rms=float(np.sqrt(np.mean(norm**2))), + vector_p95=float(np.percentile(norm, 95.0)), + ) + + +def _world_rtk(right_enu: np.ndarray) -> np.ndarray: + right = np.asarray(right_enu, dtype=float).reshape(3).copy() + right /= np.linalg.norm(right) + up = np.array([0.0, 0.0, 1.0]) + up -= right * np.dot(up, right) + up /= np.linalg.norm(up) + forward = np.cross(up, right) + forward /= np.linalg.norm(forward) + return np.column_stack([right, forward, up]) + + +def _all_hpr(session: UnifiedSession) -> _HprSeries: + annotated: list[tuple[dict[str, str], bool]] = [] + last_device_t = -np.inf + for row in session.rtk_by_type.get("GNHPR", []): + value_t = _f(row, "t_device_s") + monotonic = bool(np.isfinite(value_t) and value_t > last_device_t) + if not monotonic and annotated: + previous_row, _ = annotated[-1] + annotated[-1] = (previous_row, False) + if np.isfinite(value_t): + last_device_t = value_t + annotated.append((row, monotonic)) + annotated.sort(key=lambda item: _f(item[0], "t_device_s")) + t, baseline, valid = [], [], [] + for row, monotonic in annotated: + value_t = _f(row, "t_device_s") + heading, pitch = _f(row, "heading_deg"), _f(row, "pitch_deg") + ok = bool( + monotonic and np.isfinite(value_t) and np.isfinite(heading) and np.isfinite(pitch) + and _truth(row, "checksum_valid") and int(_f(row, "heading_quality", -1)) == 4 + ) + t.append(value_t) + if np.isfinite(heading) and np.isfinite(pitch): + vector = gnhpr_to_baseline_enu(np.array([heading]), np.array([pitch]))[0] + else: + vector = np.full(3, np.nan) + baseline.append(vector) + valid.append(ok) + valid_array = np.asarray(valid, dtype=bool) + return _HprSeries(np.asarray(t), np.asarray(baseline).reshape(-1, 3), valid_array, + np.flatnonzero(valid_array)) + + +# HPR is an optional baseline-direction observation. These limits decide only +# whether an HPR factor is usable; they never define IMU trajectory continuity. +HPR_NEAREST_Q4_TOLERANCE_S = 0.12 +HPR_BRIDGE_MAX_GAP_S = 0.50 # data-backed: 0.5 s dropout max error < 0.035 rad; 0.6 s does not +HPR_ISOLATED_OUTLIER_DEG = 30.0 +HPR_DIRECT_ANGULAR_SIGMA_RAD = 0.035 +# Conservative envelope of the three-session artificial-dropout interpolation +# P95 errors (deg), forced non-decreasing before covariance composition. +HPR_BRIDGE_ERROR_P95_DEG = np.array([ + [0.00, 0.000], [0.20, 0.899], [0.30, 0.903], + [0.40, 0.928], [0.50, 0.928], +], dtype=float) + + +def _hpr_angular_sigma_rad(method: str, support_gap_s: float) -> float: + """Per-factor angular sigma; bridge uncertainty is never treated as direct HPR.""" + if method == "nearest_q4": + return HPR_DIRECT_ANGULAR_SIGMA_RAD + if method != "bracket_interpolation" or not np.isfinite(support_gap_s): + return np.inf + if support_gap_s <= 0.0 or support_gap_s > HPR_BRIDGE_MAX_GAP_S: + return np.inf + bridge_error_deg = float(np.interp(support_gap_s, HPR_BRIDGE_ERROR_P95_DEG[:, 0], + HPR_BRIDGE_ERROR_P95_DEG[:, 1])) + # Treat the empirical P95 envelope itself as an additional conservative + # 1-sigma-equivalent term rather than granting bridge direct-HPR weight. + return float(np.hypot(HPR_DIRECT_ANGULAR_SIGMA_RAD, np.deg2rad(bridge_error_deg))) + + +def _unit_vector(vector: np.ndarray) -> np.ndarray: + value = np.asarray(vector, dtype=float).reshape(3) + norm = float(np.linalg.norm(value)) + return value / norm if np.isfinite(norm) and norm > 0.0 else np.full(3, np.nan) + + +def _baseline_angle_deg(left: np.ndarray, right: np.ndarray) -> float: + a, b = _unit_vector(left), _unit_vector(right) + if not (np.all(np.isfinite(a)) and np.all(np.isfinite(b))): + return np.nan + return float(np.degrees(np.arccos(np.clip(np.dot(a, b), -1.0, 1.0)))) + + +def _interpolate_baseline(left: np.ndarray, right: np.ndarray, fraction: float) -> np.ndarray: + # Normalized linear interpolation is stable for the sub-second bridge range. + return _unit_vector((1.0 - fraction) * _unit_vector(left) + fraction * _unit_vector(right)) + + +def _hpr_factor_observation(series: _HprSeries, t_s: float) -> tuple[np.ndarray, int, bool, str, float]: + """Return optional Q4 baseline support without changing trajectory continuity.""" + valid_indices = series.valid_indices + if valid_indices.size == 0 or not np.isfinite(t_s): + return np.full(3, np.nan), -1, False, "unavailable", np.nan + nearest_local = int(np.argmin(np.abs(series.t_s[valid_indices] - t_s))) + nearest = int(valid_indices[nearest_local]) + nearest_dt = float(abs(series.t_s[nearest] - t_s)) + if nearest_dt <= HPR_NEAREST_Q4_TOLERANCE_S: + # A single gross direction spike is a rejected factor, not a trajectory cut. + nearest_position = int(np.searchsorted(valid_indices, nearest)) + if 0 < nearest_position < valid_indices.size - 1: + left, right = int(valid_indices[nearest_position - 1]), int(valid_indices[nearest_position + 1]) + gap = float(series.t_s[right] - series.t_s[left]) + if 0.0 < gap <= 2.0 * HPR_BRIDGE_MAX_GAP_S: + expected = _interpolate_baseline( + series.baseline_enu[left], series.baseline_enu[right], + float((series.t_s[nearest] - series.t_s[left]) / gap), + ) + if _baseline_angle_deg(series.baseline_enu[nearest], expected) > HPR_ISOLATED_OUTLIER_DEG: + return np.full(3, np.nan), nearest, False, "isolated_outlier", gap + return series.baseline_enu[nearest], nearest, True, "nearest_q4", nearest_dt + right_local = int(np.searchsorted(series.t_s[valid_indices], t_s, side="right")) + if 0 < right_local < valid_indices.size: + left, right = int(valid_indices[right_local - 1]), int(valid_indices[right_local]) + gap = float(series.t_s[right] - series.t_s[left]) + if 0.0 < gap <= HPR_BRIDGE_MAX_GAP_S: + fraction = float((t_s - series.t_s[left]) / gap) + return (_interpolate_baseline(series.baseline_enu[left], series.baseline_enu[right], fraction), + -1, True, "bracket_interpolation", gap) + return np.full(3, np.nan), -1, False, "unavailable", np.nan + + +def _node_interval_threshold_s(period_s: float) -> float: + return max(1.5, 1.75 * float(period_s)) + + +def _trajectory_continuity_reasons(session: UnifiedSession, t0: float, t1: float, + period_s: float) -> tuple[str, ...]: + """Structural R0 continuity: device time and IMU preintegration coverage only.""" + dt = float(t1 - t0) + threshold = _node_interval_threshold_s(period_s) + reasons: list[str] = [] + if not np.isfinite(dt) or dt <= 0.0: + reasons.append("measurement_time_nonmonotonic") + elif dt > threshold: + reasons.append("measurement_gap") + imu_t = session.imu.t_s + if (imu_t.size < 2 or t0 < imu_t[0] or t1 > imu_t[-1] + or not _imu_continuous(session, t0, t1)): + reasons.append("imu_device_time_gap_or_preintegration_uncovered") + return tuple(reasons) + + +def _hpr_chain_continuous(series: _HprSeries, left: int, right: int) -> bool: + """Legacy diagnostic only; never use this to split R0 trajectories.""" + if left < 0 or right < left or right >= series.t_s.size: + return False + if not np.all(series.valid[left:right + 1]): + return False + if left == right: + return True + dt = np.diff(series.t_s[left:right + 1]) + return bool(np.all(np.isfinite(dt)) and np.all(dt >= 0.03) and np.all(dt <= 0.25)) + +def _all_position_rows(session: UnifiedSession) -> list[tuple[dict[str, str], str]]: + rows = [(row, "BESTNAVA") for row in session.rtk_by_type.get("BESTNAVA", [])] + rows.extend((row, "GGA") for row in session.rtk_by_type.get("GGA", [])) + return sorted(rows, key=lambda item: (_f(item[0], "t_device_s"), 0 if item[1] == "BESTNAVA" else 1)) + + +def _position_valid(row: dict[str, str], source: str) -> bool: + fixed = _truth(row, "position_fixed") if source == "BESTNAVA" else int(_f(row, "fix_quality", -1)) == 4 + required = (_f(row, "t_device_s"), _f(row, "lat_deg"), _f(row, "lon_deg")) + if source == "BESTNAVA": + required += (_f(row, "altitude_m"),) + return bool(_truth(row, "checksum_valid") and fixed and all(np.isfinite(value) for value in required)) + + +def _height_reference(sessions: Sequence[UnifiedSession]) -> tuple[float, float, float, bool] | None: + for session in sessions: + for row in session.rtk_by_type.get("BESTNAVA", []): + if _position_valid(row, "BESTNAVA"): + return _f(row, "lat_deg"), _f(row, "lon_deg"), _f(row, "altitude_m"), True + for session in sessions: + for row in session.rtk_by_type.get("GGA", []): + if _position_valid(row, "GGA"): + return _f(row, "lat_deg"), _f(row, "lon_deg"), 0.0, False + return None + + +def _enu(row: dict[str, str], source: str, reference: tuple[float, float, float, bool]) -> tuple[np.ndarray, np.ndarray]: + lat0, lon0, alt0, has_best_height = reference + lat, lon = _f(row, "lat_deg"), _f(row, "lon_deg") + north = np.deg2rad(lat - lat0) * EARTH_RADIUS_M + east = np.deg2rad(lon - lon0) * EARTH_RADIUS_M * np.cos(np.deg2rad(lat0)) + if source == "BESTNAVA" and has_best_height: + return np.array([east, north, _f(row, "altitude_m") - alt0]), np.ones(3, dtype=bool) + # Never let a GGA MSL altitude define or constrain the BESTNAVA Z datum. + return np.array([east, north, 0.0]), np.array([True, True, False]) + + +def _best_velocity_series(session: UnifiedSession) -> tuple[np.ndarray, np.ndarray]: + times, vectors = [], [] + for row in session.rtk_by_type.get("BESTNAVA", []): + value = np.array([_f(row, "velocity_east_m_s"), _f(row, "velocity_north_m_s"), + _f(row, "vertical_speed_m_s")]) + if (_truth(row, "checksum_valid") and _truth(row, "position_fixed") + and _truth(row, "doppler_velocity_valid") and np.all(np.isfinite(value))): + times.append(_f(row, "t_device_s")) + vectors.append(value) + order = np.argsort(times) + return np.asarray(times)[order], np.asarray(vectors).reshape(-1, 3)[order] + + +def _motion_flags(session: UnifiedSession, t_s: float, velocity_t: np.ndarray, + velocity: np.ndarray, window_s: float = 1.5) -> tuple[bool, bool]: + """Classify gravity/ZUPT in a local device-time slice, never a host-time window.""" + half = 0.5 * window_s + imu_left = int(np.searchsorted(session.imu.t_s, t_s - half, side="left")) + imu_right = int(np.searchsorted(session.imu.t_s, t_s + half, side="right")) + imu_t = session.imu.t_s[imu_left:imu_right] + if imu_t.size < 20 or imu_t[-1] - imu_t[0] < 1.2: + return False, False + gyro = session.imu.gyro_rad_s[imu_left:imu_right] + accel = session.imu.acc_m_s2[imu_left:imu_right] + gravity_candidate = bool( + np.linalg.norm(np.mean(gyro, axis=0)) <= np.deg2rad(0.35) + and np.max(np.std(gyro, axis=0)) <= np.deg2rad(0.10) + and abs(float(np.mean(np.linalg.norm(accel, axis=1))) - G0) <= 0.15 + and float(np.std(np.linalg.norm(accel, axis=1))) <= 0.06 + and np.max(np.std(accel, axis=0)) <= 0.10 + ) + if not gravity_candidate or velocity_t.size == 0: + return gravity_candidate, False + velocity_left = int(np.searchsorted(velocity_t, t_s - half, side="left")) + velocity_right = int(np.searchsorted(velocity_t, t_s + half, side="right")) + local_t, local_v = velocity_t[velocity_left:velocity_right], velocity[velocity_left:velocity_right] + if local_t.size < 3 or local_t[-1] - local_t[0] < 1.0 or np.any(np.diff(local_t) > 0.75): + return gravity_candidate, False + speed = np.linalg.norm(local_v, axis=1) + zupt = bool(np.max(speed) <= 0.12 and np.median(speed) <= 0.05) + return gravity_candidate, zupt + +def _imu_continuous(session: UnifiedSession, t0: float, t1: float) -> bool: + left = max(int(np.searchsorted(session.imu.t_s, t0, side="right")) - 1, 0) + right = min(int(np.searchsorted(session.imu.t_s, t1, side="left")) + 1, session.imu.t_s.size) + local = session.imu.t_s[left:right] + return bool(local.size >= 2 and np.all(np.diff(local) > 0.0) and np.all(np.diff(local) <= 0.05)) + + +def _source_nodes(session: UnifiedSession, reference: tuple[float, float, float, bool], + period_s: float, source: str) -> list[_Node]: + """Build one position source's nodes; HPR only annotates optional factors.""" + hpr = _all_hpr(session) + velocity_t, velocity_values = _best_velocity_series(session) + bad_position_rows: set[int] = set() + last_device_t = -np.inf + previous_row: dict[str, str] | None = None + for raw_row in session.rtk_by_type.get(source, []): + raw_t = _f(raw_row, "t_device_s") + if not np.isfinite(raw_t) or raw_t <= last_device_t: + bad_position_rows.add(id(raw_row)) + if previous_row is not None: + bad_position_rows.add(id(previous_row)) + if np.isfinite(raw_t): + last_device_t = raw_t + previous_row = raw_row + + result: list[_Node] = [] + continuity_id = 0 + last_selected: _Node | None = None + pending_position_time_break = False + # Tolerate normal timestamp jitter: the old `next_t=t+period` skipped a + # nominal 1 Hz sample whenever it arrived microscopically early. + min_selected_spacing_s = 0.75 * float(period_s) + rows = sorted(session.rtk_by_type.get(source, []), key=lambda row: _f(row, "t_device_s")) + for row in rows: + t = _f(row, "t_device_s") + if id(row) in bad_position_rows: + # Preserve a raw device-time discontinuity after sorting/dropping + # the malformed rows: it is a structural trajectory break. + pending_position_time_break = True + continuity_id += 1 + last_selected = None + continue + if not _position_valid(row, source): + # A rejected position factor alone is not allowed to create an + # HPR-like artificial trajectory split. + continue + if last_selected is not None and t - last_selected.t_s < min_selected_spacing_s: + continue + imu_index = _nearest_index(session.imu.t_s, t, 0.03) + if imu_index is None: + continuity_id += 1 + last_selected = None + continue + p_enu, mask = _enu(row, source, reference) + row_velocity = None + if source == "BESTNAVA" and _truth(row, "doppler_velocity_valid"): + candidate = np.array([_f(row, "velocity_east_m_s"), _f(row, "velocity_north_m_s"), + _f(row, "vertical_speed_m_s")]) + if np.all(np.isfinite(candidate)): + row_velocity = candidate + gravity_candidate, zupt_static = _motion_flags(session, t, velocity_t, velocity_values) + baseline, hpr_index, hpr_valid, hpr_method, hpr_gap = _hpr_factor_observation(hpr, t) + node = _Node( + t_s=float(t), source=source, p_enu_m=p_enu, position_mask=mask, + baseline_enu=baseline, hpr_index=hpr_index, + hpr_factor_valid=hpr_valid, hpr_factor_method=hpr_method, hpr_support_gap_s=hpr_gap, + hpr_angular_sigma_rad=_hpr_angular_sigma_rad(hpr_method, hpr_gap) if hpr_valid else np.inf, + position_time_discontinuity_before=pending_position_time_break, + gyro_rad_s=session.imu.gyro_rad_s[imu_index], + accel_m_s2=session.imu.acc_m_s2[imu_index], velocity_enu_m_s=row_velocity, + gravity_candidate=gravity_candidate, zupt_static=zupt_static, + continuity_id=continuity_id, + ) + if last_selected is not None: + if _trajectory_continuity_reasons(session, last_selected.t_s, node.t_s, period_s): + continuity_id += 1 + node = _Node(**{**node.__dict__, "continuity_id": continuity_id}) + result.append(node) + last_selected = node + pending_position_time_break = False + return result + + +def _nodes(session: UnifiedSession, reference: tuple[float, float, float, bool], + period_s: float) -> list[_Node]: + """Prefer BESTNAVA; let GGA fill only intervals absent from BESTNAVA.""" + best = _source_nodes(session, reference, period_s, "BESTNAVA") + gga = _source_nodes(session, reference, period_s, "GGA") + if best: + best_t = np.asarray([node.t_s for node in best]) + keep_gga: list[_Node] = [] + for node in gga: + right = int(np.searchsorted(best_t, node.t_s)) + candidates = best_t[max(right - 1, 0):min(right + 1, best_t.size)] + if candidates.size == 0 or np.min(np.abs(candidates - node.t_s)) > period_s: + keep_gga.append(node) + else: + keep_gga = gga + merged = sorted([*best, *keep_gga], key=lambda node: (node.t_s, 0 if node.source == "BESTNAVA" else 1)) + result: list[_Node] = [] + continuity_id = 0 + previous: _Node | None = None + for node in merged: + if previous is not None: + # Per-source continuity ids cannot be compared across BEST/GGA: + # an alternate source may legitimately bridge a missing position row. + # The merged trajectory is continuous exactly when its device-time + # interval has usable IMU preintegration coverage. + if (node.position_time_discontinuity_before + or _trajectory_continuity_reasons(session, previous.t_s, node.t_s, period_s)): + continuity_id += 1 + node = _Node(**{**node.__dict__, "continuity_id": continuity_id}) + result.append(node) + previous = node + return result +def _segments(sessions: Iterable[UnifiedSession], period_s: float) -> list[_Segment]: + sessions = list(sessions) + reference = _height_reference(sessions) + if reference is None: + return [] + result: list[_Segment] = [] + for session in sessions: + nodes = _nodes(session, reference, period_s) + if not nodes: + continue + start = 0 + qualifying_index = 0 + for end in range(1, len(nodes) + 1): + split = end == len(nodes) or nodes[end].continuity_id != nodes[end - 1].continuity_id + if not split: + continue + run, start = nodes[start:end], end + if (len(run) < MIN_SEGMENT_NODE_COUNT + or run[-1].t_s - run[0].t_s < MIN_SEGMENT_DURATION_S): + continue + pre = tuple(preintegrate_imu( + session.imu.t_s, session.imu.gyro_rad_s, session.imu.acc_m_s2, + left.t_s, right.t_s, + ) for left, right in zip(run[:-1], run[1:])) + if any(item.duration_s <= 0.0 for item in pre): + continue + initial_hpr = next((node for node in run if node.hpr_factor_valid), None) + if initial_hpr is None: + continue + result.append(_Segment( + f"{session.session_id}:{qualifying_index:02d}", session.session_id, + tuple(run), pre, _world_rtk(initial_hpr.baseline_enu) + )) + qualifying_index += 1 + return result + + +def _initial_parameters(segments: Sequence[_Segment]) -> np.ndarray: + x = np.zeros(3 + NUISANCE_DOF_PER_SEGMENT * len(segments)) + for index, segment in enumerate(segments): + offset = 3 + NUISANCE_DOF_PER_SEGMENT * index + x[offset + 3:offset + 6] = segment.nodes[0].p_enu_m + return x + + +def _residual(x: np.ndarray, segments: Sequence[_Segment], R_RTK_IMU: np.ndarray, + details: dict[str, list[np.ndarray]] | None = None, + lever_prior_l_I_m: np.ndarray | None = None, + lever_prior_cholesky: np.ndarray | None = None) -> np.ndarray: + l_I, values = x[:3], [] + baseline_I = R_RTK_IMU.T[:, 0] + for index, segment in enumerate(segments): + offset = 3 + NUISANCE_DOF_PER_SEGMENT * index + dtheta = x[offset:offset + 3] + p_I = x[offset + 3:offset + 6].copy() + velocity = x[offset + 6:offset + 9].copy() + bg = x[offset + 9:offset + 12] + ba = x[offset + 12:offset + 15] + R_WI = segment.R_WRTK_initial @ R_RTK_IMU @ so3_exp(dtheta) + for node_index, node in enumerate(segment.nodes): + p_error = p_I + R_WI @ l_I - node.p_enu_m + if node.source == "GGA": + values.extend(p_error[:2] / 0.06) + if details is not None: + details["gga_xy"].append(p_error[:2]) + else: + values.extend(p_error / np.array([0.06, 0.06, 0.12])) + if details is not None: + details["bestnava_xyz"].append(p_error) + if node.hpr_factor_valid: + values.extend(np.cross(R_WI @ baseline_I, node.baseline_enu) / node.hpr_angular_sigma_rad) + if node.velocity_enu_m_s is not None: + v_error = velocity + R_WI @ np.cross(node.gyro_rad_s - bg, l_I) - node.velocity_enu_m_s + values.extend(v_error / np.array([0.15, 0.15, 0.30])) + if details is not None: + details["doppler_velocity"].append(v_error) + if node.gravity_candidate: + gravity_error = node.accel_m_s2 - ba - R_WI.T @ (-G_ENU) + values.extend(gravity_error / 0.12) + if details is not None: + details["gravity"].append(gravity_error) + if node.zupt_static: + values.extend(velocity / 0.10) + if details is not None: + details["zupt"].append(velocity.copy()) + if node_index == len(segment.preintegrations): + continue + pre = segment.preintegrations[node_index] + dR, dv, dp = apply_bias_correction_imu(pre, bg, ba) + dt = pre.duration_s + p_I = p_I + velocity * dt + 0.5 * G_ENU * dt * dt + R_WI @ dp + velocity = velocity + G_ENU * dt + R_WI @ dv + R_WI = R_WI @ dR + values.extend(dtheta / np.deg2rad(5.0)) + values.extend(bg / 0.02) + values.extend(ba / 0.50) + if lever_prior_l_I_m is not None and lever_prior_cholesky is not None: + values.extend(np.linalg.solve(lever_prior_cholesky, l_I - lever_prior_l_I_m)) + return np.asarray(values, dtype=float) + + +def _segment_residual_size(segment: _Segment) -> int: + size = 9 # dtheta, gyro-bias, and accel-bias priors + for node in segment.nodes: + size += 2 if node.source == "GGA" else 3 + size += 3 if node.hpr_factor_valid else 0 # optional ANT1-to-ANT2 direction + size += 3 if node.velocity_enu_m_s is not None else 0 + size += 3 if node.gravity_candidate else 0 + size += 3 if node.zupt_static else 0 + return size + + +def _dense_colored_jacobian(x: np.ndarray, segments: Sequence[_Segment], + R_RTK_IMU: np.ndarray, + lever_prior_l_I_m: np.ndarray | None = None, + lever_prior_cholesky: np.ndarray | None = None) -> np.ndarray: + """Finite-difference the block-separable Jacobian using 18 color groups.""" + base = _residual(x, segments, R_RTK_IMU, lever_prior_l_I_m=lever_prior_l_I_m, + lever_prior_cholesky=lever_prior_cholesky) + jacobian = np.zeros((base.size, x.size), dtype=float) + step_scale = np.sqrt(np.finfo(float).eps) + row_slices: list[slice] = [] + row = 0 + for segment in segments: + count = _segment_residual_size(segment) + row_slices.append(slice(row, row + count)) + row += count + for column in range(3): + step = step_scale * max(1.0, abs(float(x[column]))) + shifted = x.copy() + shifted[column] += step + jacobian[:, column] = (_residual( + shifted, segments, R_RTK_IMU, lever_prior_l_I_m=lever_prior_l_I_m, + lever_prior_cholesky=lever_prior_cholesky, + ) - base) / step + for local_column in range(NUISANCE_DOF_PER_SEGMENT): + shifted = x.copy() + steps = [] + for segment_index in range(len(segments)): + column = 3 + NUISANCE_DOF_PER_SEGMENT * segment_index + local_column + step = step_scale * max(1.0, abs(float(x[column]))) + shifted[column] += step + steps.append(step) + difference = _residual( + shifted, segments, R_RTK_IMU, lever_prior_l_I_m=lever_prior_l_I_m, + lever_prior_cholesky=lever_prior_cholesky, + ) - base + for segment_index, rows in enumerate(row_slices): + column = 3 + NUISANCE_DOF_PER_SEGMENT * segment_index + local_column + jacobian[rows, column] = difference[rows] / steps[segment_index] + return jacobian + + +def _fit_segments(segments: Sequence[_Segment], R_RTK_IMU: np.ndarray, + *, lever_prior_l_I_m: np.ndarray | None = None, + lever_prior_covariance_m2: np.ndarray | None = None, + max_nfev: int = 80): + details = {"gga_xy": [], "bestnava_xyz": [], "doppler_velocity": [], + "gravity": [], "zupt": []} + if not segments: + return None, np.zeros(0), details + if (lever_prior_l_I_m is None) != (lever_prior_covariance_m2 is None): + raise ValueError("lever prior mean and covariance must be provided together") + prior_l_I = None if lever_prior_l_I_m is None else np.asarray(lever_prior_l_I_m, dtype=float).reshape(3) + prior_cholesky = None + if lever_prior_covariance_m2 is not None: + covariance = np.asarray(lever_prior_covariance_m2, dtype=float).reshape(3, 3) + prior_cholesky = np.linalg.cholesky(covariance) + x0 = _initial_parameters(segments) + fit = least_squares( + lambda value: _residual(value, segments, R_RTK_IMU, lever_prior_l_I_m=prior_l_I, + lever_prior_cholesky=prior_cholesky), x0, + jac=lambda value: _dense_colored_jacobian( + value, segments, R_RTK_IMU, prior_l_I, prior_cholesky, + ), + loss="huber", f_scale=1.5, max_nfev=max_nfev, + ) + residual = _residual(fit.x, segments, R_RTK_IMU, details, prior_l_I, prior_cholesky) + return fit, residual, details + + +def _marginal_lever_information(jacobian: np.ndarray, residual: np.ndarray): + jacobian = np.asarray(jacobian, dtype=float) + hessian = jacobian.T @ jacobian + H_ll = hessian[:3, :3] + H_ln = hessian[:3, 3:] + H_nn = hessian[3:, 3:] + marginal = H_ll.copy() + nuisance_size = H_nn.shape[0] + if nuisance_size: + # Every engineering segment owns an independent 15-DoF nuisance block. + # Marginalize those blocks separately: one global relative pinv cutoff + # can otherwise discard valid modes from smaller-motion blocks and make + # full information disagree with the sum of category information. + if nuisance_size % NUISANCE_DOF_PER_SEGMENT == 0: + for start in range(0, nuisance_size, NUISANCE_DOF_PER_SEGMENT): + stop = start + NUISANCE_DOF_PER_SEGMENT + H_l_block = H_ln[:, start:stop] + H_block = H_nn[start:stop, start:stop] + marginal -= H_l_block @ np.linalg.pinv(H_block, rcond=1e-10) @ H_l_block.T + else: + marginal -= H_ln @ np.linalg.pinv(H_nn, rcond=1e-10) @ H_ln.T + marginal = 0.5 * (marginal + marginal.T) + _, singular, vh = np.linalg.svd(marginal) + threshold = max(float(singular[0]) * 1e-6, 1e-8) if singular.size else np.inf + rank = int(np.count_nonzero(singular > threshold)) + condition = float(singular[0] / singular[-1]) if singular[-1] > threshold else np.inf + weakest = vh[-1] if vh.size else np.full(3, np.nan) + dof = max(jacobian.shape[0] - jacobian.shape[1], 1) + variance = float(np.dot(residual, residual) / dof) + covariance = variance * np.linalg.pinv(marginal, rcond=1e-10) + return marginal, singular, condition, rank, weakest, covariance + + +def _fit_summary(fit, residual: np.ndarray, detail: dict[str, list[np.ndarray]]) -> LeverFitSummary | None: + if fit is None: + return None + marginal, singular, condition, rank, weak, covariance = _marginal_lever_information(fit.jac, residual) + std = np.sqrt(np.maximum(np.diag(covariance), 0.0)) + return LeverFitSummary( + l_I_m=fit.x[:3].copy(), + l_I_marginal_covariance_m2=covariance, + l_I_std_m=std, + lever_marginal_information=marginal, + lever_information_singular_values=singular, + lever_information_condition_number=condition, + lever_precision_rank=rank, + weakest_lever_direction_I=weak, + gga_xy_residual=_audit(detail["gga_xy"], 2), + bestnava_xyz_residual=_audit(detail["bestnava_xyz"], 3), + doppler_velocity_residual=_audit(detail["doppler_velocity"], 3), + optimizer_converged=bool(fit.success and np.all(np.isfinite(fit.x))), + ) +def _fit_fixed_lever_residual( + segments: Sequence[_Segment], R_RTK_IMU: np.ndarray, l_I_m: np.ndarray, + *, max_nfev: int = 80, +) -> FixedLeverResidualSummary | None: + """Profile nuisance states at a fixed mechanical lever arm for residual comparison.""" + if not segments: + return None + lever = np.asarray(l_I_m, dtype=float).reshape(3) + initial = _initial_parameters(segments)[3:] + def compose(nuisance: np.ndarray) -> np.ndarray: + return np.concatenate([lever, nuisance]) + fit = least_squares( + lambda nuisance: _residual(compose(nuisance), segments, R_RTK_IMU), + initial, loss="huber", f_scale=1.5, max_nfev=max_nfev, + ) + detail = {"gga_xy": [], "bestnava_xyz": [], "doppler_velocity": [], + "gravity": [], "zupt": []} + _residual(compose(fit.x), segments, R_RTK_IMU, detail) + return FixedLeverResidualSummary( + l_I_m=lever.copy(), + gga_xy_residual=_audit(detail["gga_xy"], 2), + bestnava_xyz_residual=_audit(detail["bestnava_xyz"], 3), + doppler_velocity_residual=_audit(detail["doppler_velocity"], 3), + optimizer_converged=bool(fit.success and np.all(np.isfinite(fit.x))), + ) + + +def _resample_segments_with_multiplicity(segments: Sequence[_Segment], + drawn_session_ids: Sequence[str]) -> list[_Segment]: + by_session = { + session_id: [segment for segment in segments if segment.session_id == session_id] + for session_id in set(drawn_session_ids) + } + return [segment for session_id in drawn_session_ids for segment in by_session.get(session_id, [])] + + +def _subset_value(segments: Sequence[_Segment], R_RTK_IMU: np.ndarray, + *, lever_prior_l_I_m: np.ndarray | None = None, + lever_prior_covariance_m2: np.ndarray | None = None, + max_nfev: int = 80) -> np.ndarray | None: + fit, _, _ = _fit_segments( + segments, R_RTK_IMU, lever_prior_l_I_m=lever_prior_l_I_m, + lever_prior_covariance_m2=lever_prior_covariance_m2, max_nfev=max_nfev, + ) + return None if fit is None else fit.x[:3] + + +def _solver_health_gates(fit, std: np.ndarray, rank: int, condition: float, + gga: ResidualAudit, best: ResidualAudit, + velocity: ResidualAudit) -> dict[str, bool]: + return { + "optimizer_converged_and_finite": bool( + fit is not None and fit.success and np.all(np.isfinite(fit.x)) + ), + "lever_std_below_0p5m": bool(np.all(np.isfinite(std)) and np.max(std) <= 0.50), + "lever_precision_rank_at_least_2": rank >= 2, + "lever_condition_below_1e10": bool(np.isfinite(condition) and condition <= 1e10), + "position_residual_not_diverged": bool( + max(gga.vector_p95 if gga.count else 0.0, best.vector_p95 if best.count else 0.0) <= 2.0 + ), + "velocity_residual_not_diverged": bool(velocity.count and velocity.vector_p95 <= 3.0), + } + + +def _engineering_gates(std: np.ndarray, singular: np.ndarray, rank: int, + condition: float, gga: ResidualAudit, best: ResidualAudit, + velocity: ResidualAudit, loo: dict[str, float], + bootstrap_std: np.ndarray, bootstrap_complete: bool, + sensitivity_max: float, sensitivity_complete: bool, + manual_delta: np.ndarray | None, + free_translation_observable: bool, + free_manual_mahalanobis_d2: float | None) -> dict[str, bool]: + return { + "lever_marginal_std": bool(np.all(np.isfinite(std)) and np.all(std <= np.array([0.15, 0.15, 0.20]))), + "lever_information_rank": rank == 3, + "lever_information_condition": bool(np.isfinite(condition) and condition <= 1e6), + "lever_min_information": bool(singular.size == 3 and singular[-1] >= 1e-3), + "bestnava_xyz_rms_p95": bool( + best.count >= 10 and np.all(best.axis_rms <= np.array([0.20, 0.20, 0.35])) + and np.all(best.axis_p95_abs <= np.array([0.40, 0.40, 0.70])) + ), + "gga_xy_rms_p95": bool( + gga.count == 0 or (np.all(gga.axis_rms <= 0.20) and np.all(gga.axis_p95_abs <= 0.40)) + ), + "doppler_velocity_rms_p95": bool( + velocity.count >= 10 and np.all(velocity.axis_rms <= np.array([0.30, 0.30, 0.50])) + and np.all(velocity.axis_p95_abs <= np.array([0.60, 0.60, 1.00])) + ), + "leave_one_session_out": bool(len(loo) >= 3 and max(loo.values(), default=np.inf) <= 0.20), + "bootstrap_complete_and_stable": bool( + bootstrap_complete and np.all(np.isfinite(bootstrap_std)) and np.max(bootstrap_std) <= 0.20 + ), + "rotation_sensitivity_complete_and_stable": bool( + sensitivity_complete and np.isfinite(sensitivity_max) and sensitivity_max <= 0.10 + ), + "manual_lever_consistency": bool( + manual_delta is None or np.linalg.norm(manual_delta) <= 0.30 + ), + "free_manual_mahalanobis_consistency": bool( + not free_translation_observable + or free_manual_mahalanobis_d2 is None + or free_manual_mahalanobis_d2 <= FREE_MANUAL_MAHALANOBIS_99_DF3 + ), + } + + + +def _free_translation_observability(summary: LeverFitSummary) -> dict[str, bool]: + return { + "free_optimizer_converged": summary.optimizer_converged, + "free_lever_marginal_std": bool( + np.all(np.isfinite(summary.l_I_std_m)) + and np.all(summary.l_I_std_m <= np.array([0.15, 0.15, 0.20])) + ), + "free_lever_information_rank": summary.lever_precision_rank == 3, + "free_lever_information_condition": bool( + np.isfinite(summary.lever_information_condition_number) + and summary.lever_information_condition_number <= 1e6 + ), + "free_min_information": bool( + summary.lever_information_singular_values.size == 3 + and summary.lever_information_singular_values[-1] >= 1e-3 + ), + } +def solve_engineering_6dof( + sessions: list[UnifiedSession], *, R_RTK_IMU: np.ndarray, + manual_l_I_m: np.ndarray | None = None, + manual_l_I_covariance_m2: np.ndarray | None = None, + sample_period_s: float = 0.5, + run_loo: bool = True, run_bootstrap: bool = False, + bootstrap_repetitions: int = 40, bootstrap_seed: int = 0, + run_rotation_sensitivity: bool = False, + selected_segment_ids: set[str] | None = None, +) -> Engineering6DofResult: + """Fit free and optionally mechanically-prior-constrained ``l_I=p_ANT1^I``.""" + R = np.asarray(R_RTK_IMU, dtype=float).reshape(3, 3) + manual = None if manual_l_I_m is None else np.asarray(manual_l_I_m, dtype=float).reshape(3) + prior_covariance = ( + None if manual_l_I_covariance_m2 is None + else np.asarray(manual_l_I_covariance_m2, dtype=float).reshape(3, 3) + ) + if (manual is None) != (prior_covariance is None): + raise ValueError("manual_l_I_m and manual_l_I_covariance_m2 must be provided together") + if prior_covariance is not None: + if not np.allclose(prior_covariance, prior_covariance.T, atol=1e-12): + raise ValueError("manual_l_I_covariance_m2 must be symmetric") + np.linalg.cholesky(prior_covariance) + + segments = _segments(sessions, sample_period_s) + if selected_segment_ids is not None: + selected = set(selected_segment_ids) + unknown = selected.difference(segment.segment_id for segment in segments) + if unknown: + raise ValueError(f"unknown selected_segment_ids: {sorted(unknown)}") + segments = [segment for segment in segments if segment.segment_id in selected] + free_fit, free_residual, free_detail = _fit_segments(segments, R) + free_solution = _fit_summary(free_fit, free_residual, free_detail) + nan3, nan33 = np.full(3, np.nan), np.full((3, 3), np.nan) + empty2, empty3 = _empty_audit(2), _empty_audit(3) + if free_solution is None: + return Engineering6DofResult( + rotation_source="R2G_gravity_level_prior", + translation_conditional_on_rotation=True, + R_RTK_IMU=R, + l_I_m=None, + l_I_marginal_covariance_m2=nan33, + l_I_std_m=nan3, + lever_marginal_information=nan33, + lever_information_singular_values=nan3, + lever_information_condition_number=np.inf, + lever_precision_rank=0, + weakest_lever_direction_I=nan3, + gga_xy_residual=empty2, + bestnava_xyz_residual=empty3, + doppler_velocity_residual=empty3, + session_count=0, + segment_count=0, + gga_factor_count=0, + bestnava_factor_count=0, + velocity_factor_count=0, + gravity_candidate_factor_count=0, + zupt_static_factor_count=0, + leave_one_session_out_l_I_m={}, + leave_one_session_out_delta_m={}, + bootstrap_l_I_mean_m=nan3, + bootstrap_l_I_std_m=nan3, + bootstrap_success_count=0, + bootstrap_requested_count=bootstrap_repetitions, + rotation_sensitivity_delta_l_I_m={}, + rotation_sensitivity_max_delta_m=np.inf, + manual_l_I_m=manual, + manual_l_I_delta_m=None, + solver_health_gates={"optimizer_converged_and_finite": False}, + solver_health_passed=False, + engineering_acceptance_gates={"base_fit_available": False}, + T_RTK_IMU=None, + T_IMU_RTK=None, + engineering_6dof_accepted=False, + blockers=("no continuous Fixed GNSS + Q4 GNHPR segment",), + notes=("data_only_6dof_accepted remains false",), + translation_prior_applied=False, + lever_prior_covariance_m2=prior_covariance, + mechanical_reference_l_I_m=manual, + free_solution=None, + prior_constrained_solution=None, + free_to_mechanical_delta_m=None, + prior_to_mechanical_delta_m=None, + free_translation_observable=False, + data_only_translation_accepted=False, + free_manual_mahalanobis_d2=None, + free_translation_observability_gates={"free_fit_available": False}, + posterior_to_prior_covariance_ratio=None, + free_to_prior_residual_delta=None, + mechanical_reference_solution=None, + residual_comparison=None, + ) + + prior_fit = prior_residual = prior_detail = None + prior_solution: LeverFitSummary | None = None + if manual is not None: + prior_fit, prior_residual, prior_detail = _fit_segments( + segments, R, lever_prior_l_I_m=manual, + lever_prior_covariance_m2=prior_covariance, + ) + prior_solution = _fit_summary(prior_fit, prior_residual, prior_detail) + mechanical_solution = ( + None if manual is None else _fit_fixed_lever_residual(segments, R, manual) + ) + active_fit = prior_fit if prior_solution is not None else free_fit + active_detail = prior_detail if prior_solution is not None else free_detail + active_solution = prior_solution if prior_solution is not None else free_solution + assert active_fit is not None and active_detail is not None and active_solution is not None + l_I = active_solution.l_I_m + ids = sorted({segment.session_id for segment in segments}) + loo_l: dict[str, np.ndarray] = {} + loo_delta: dict[str, float] = {} + if run_loo: + for session_id in ids: + kept = [segment for segment in segments if segment.session_id != session_id] + value = _subset_value( + kept, R, lever_prior_l_I_m=manual, + lever_prior_covariance_m2=prior_covariance, + ) + if value is not None: + loo_l[session_id] = value + loo_delta[session_id] = float(np.linalg.norm(value - l_I)) + rng = np.random.default_rng(bootstrap_seed) + bootstrap: list[np.ndarray] = [] + if run_bootstrap and len(ids) >= 2: + for _ in range(bootstrap_repetitions): + drawn = list(rng.choice(ids, size=len(ids), replace=True)) + sampled = _resample_segments_with_multiplicity(segments, drawn) + value = _subset_value( + sampled, R, lever_prior_l_I_m=manual, + lever_prior_covariance_m2=prior_covariance, + ) + if value is not None: + bootstrap.append(value) + bootstrap_array = np.asarray(bootstrap) + bootstrap_mean = np.mean(bootstrap_array, axis=0) if bootstrap else nan3 + bootstrap_std = np.std(bootstrap_array, axis=0, ddof=1) if len(bootstrap) > 1 else nan3 + bootstrap_complete = bool(run_bootstrap and len(bootstrap) == bootstrap_repetitions and bootstrap_repetitions >= 10) + sensitivity: dict[str, np.ndarray] = {} + if run_rotation_sensitivity: + for axis, name in enumerate("xyz"): + for angle in (0.2, 0.3, 0.5): + for sign in (-1.0, 1.0): + vector = np.zeros(3) + vector[axis] = np.deg2rad(sign * angle) + perturbed = Rotation.from_rotvec(vector).as_matrix() @ R + value = _subset_value( + segments, perturbed, lever_prior_l_I_m=manual, + lever_prior_covariance_m2=prior_covariance, + ) + if value is not None: + sensitivity[f"RTK_{name}_{sign * angle:+.1f}deg"] = value - l_I + sensitivity_max = max((float(np.linalg.norm(value)) for value in sensitivity.values()), default=np.inf) + sensitivity_complete = bool(run_rotation_sensitivity and len(sensitivity) == 18) + active_manual_delta = None if manual is None else l_I - manual + free_manual_delta = None if manual is None else free_solution.l_I_m - manual + prior_manual_delta = None if prior_solution is None or manual is None else prior_solution.l_I_m - manual + free_observability_gates = _free_translation_observability(free_solution) + free_translation_observable = all(free_observability_gates.values()) + free_manual_mahalanobis_d2: float | None = None + if manual is not None and free_translation_observable: + combined_covariance = free_solution.l_I_marginal_covariance_m2 + prior_covariance + try: + free_manual_mahalanobis_d2 = float( + free_manual_delta @ np.linalg.solve(combined_covariance, free_manual_delta) + ) + except np.linalg.LinAlgError: + free_manual_mahalanobis_d2 = np.inf + posterior_to_prior_covariance_ratio: np.ndarray | None = None + free_to_prior_residual_delta: dict[str, float] | None = None + residual_comparison: dict[str, float] | None = None + if prior_solution is not None and prior_covariance is not None: + # This ratio uses information matrices, not residual-scale covariance. + # Known factor sigmas already define the statistical scale of the prior. + posterior_covariance_by_information = np.linalg.pinv( + prior_solution.lever_marginal_information, rcond=1e-10 + ) + posterior_to_prior_covariance_ratio = np.diag( + posterior_covariance_by_information + ) / np.diag(prior_covariance) + free_to_prior_residual_delta = { + "bestnava_xyz_vector_rms_m": float( + prior_solution.bestnava_xyz_residual.vector_rms + - free_solution.bestnava_xyz_residual.vector_rms + ), + "bestnava_xyz_vector_p95_m": float( + prior_solution.bestnava_xyz_residual.vector_p95 + - free_solution.bestnava_xyz_residual.vector_p95 + ), + "doppler_velocity_vector_rms_m_s": float( + prior_solution.doppler_velocity_residual.vector_rms + - free_solution.doppler_velocity_residual.vector_rms + ), + "doppler_velocity_vector_p95_m_s": float( + prior_solution.doppler_velocity_residual.vector_p95 + - free_solution.doppler_velocity_residual.vector_p95 + ), + } + residual_comparison = dict(free_to_prior_residual_delta) + if mechanical_solution is not None: + for name, summary in (("mechanical", mechanical_solution), ("prior", prior_solution)): + residual_comparison[f"{name}_minus_free_bestnava_xyz_vector_rms_m"] = float( + summary.bestnava_xyz_residual.vector_rms + - free_solution.bestnava_xyz_residual.vector_rms + ) + residual_comparison[f"{name}_minus_free_bestnava_xyz_vector_p95_m"] = float( + summary.bestnava_xyz_residual.vector_p95 + - free_solution.bestnava_xyz_residual.vector_p95 + ) + residual_comparison[f"{name}_minus_free_doppler_velocity_vector_rms_m_s"] = float( + summary.doppler_velocity_residual.vector_rms + - free_solution.doppler_velocity_residual.vector_rms + ) + residual_comparison[f"{name}_minus_free_doppler_velocity_vector_p95_m_s"] = float( + summary.doppler_velocity_residual.vector_p95 + - free_solution.doppler_velocity_residual.vector_p95 + ) + residual_comparison["prior_minus_mechanical_bestnava_xyz_vector_rms_m"] = float( + prior_solution.bestnava_xyz_residual.vector_rms + - mechanical_solution.bestnava_xyz_residual.vector_rms + ) + residual_comparison["prior_minus_mechanical_doppler_velocity_vector_rms_m_s"] = float( + prior_solution.doppler_velocity_residual.vector_rms + - mechanical_solution.doppler_velocity_residual.vector_rms + ) + solver_gates = _solver_health_gates( + active_fit, active_solution.l_I_std_m, active_solution.lever_precision_rank, + active_solution.lever_information_condition_number, active_solution.gga_xy_residual, + active_solution.bestnava_xyz_residual, active_solution.doppler_velocity_residual, + ) + engineering_gates = _engineering_gates( + active_solution.l_I_std_m, active_solution.lever_information_singular_values, + active_solution.lever_precision_rank, active_solution.lever_information_condition_number, + active_solution.gga_xy_residual, active_solution.bestnava_xyz_residual, + active_solution.doppler_velocity_residual, loo_delta, bootstrap_std, bootstrap_complete, + sensitivity_max, sensitivity_complete, active_manual_delta, + free_translation_observable, free_manual_mahalanobis_d2, + ) + if manual is not None and prior_solution is None: + engineering_gates["prior_constrained_fit_available"] = False + engineering_gates["legacy_deterministic_segment_model_not_deprecated"] = ( + not LEGACY_DETERMINISTIC_SEGMENT_MODEL_DEPRECATED + ) + solver_health = all(solver_gates.values()) + accepted = bool(solver_health and all(engineering_gates.values())) + blockers = tuple( + [f"solver_health:{name}" for name, passed in solver_gates.items() if not passed] + + [f"engineering_acceptance:{name}" for name, passed in engineering_gates.items() if not passed] + ) + T_RTK_IMU = make_transform(-R @ l_I, R) + T_IMU_RTK = make_transform(l_I, R.T) + return Engineering6DofResult( + rotation_source="R2G_gravity_level_prior", + translation_conditional_on_rotation=True, + R_RTK_IMU=R, + l_I_m=l_I, + l_I_marginal_covariance_m2=active_solution.l_I_marginal_covariance_m2, + l_I_std_m=active_solution.l_I_std_m, + lever_marginal_information=active_solution.lever_marginal_information, + lever_information_singular_values=active_solution.lever_information_singular_values, + lever_information_condition_number=active_solution.lever_information_condition_number, + lever_precision_rank=active_solution.lever_precision_rank, + weakest_lever_direction_I=active_solution.weakest_lever_direction_I, + gga_xy_residual=active_solution.gga_xy_residual, + bestnava_xyz_residual=active_solution.bestnava_xyz_residual, + doppler_velocity_residual=active_solution.doppler_velocity_residual, + session_count=len(ids), + segment_count=len(segments), + gga_factor_count=active_solution.gga_xy_residual.count, + bestnava_factor_count=active_solution.bestnava_xyz_residual.count, + velocity_factor_count=active_solution.doppler_velocity_residual.count, + gravity_candidate_factor_count=len(active_detail["gravity"]), + zupt_static_factor_count=len(active_detail["zupt"]), + leave_one_session_out_l_I_m=loo_l, + leave_one_session_out_delta_m=loo_delta, + bootstrap_l_I_mean_m=bootstrap_mean, + bootstrap_l_I_std_m=bootstrap_std, + bootstrap_success_count=len(bootstrap), + bootstrap_requested_count=bootstrap_repetitions, + rotation_sensitivity_delta_l_I_m=sensitivity, + rotation_sensitivity_max_delta_m=sensitivity_max, + manual_l_I_m=manual, + manual_l_I_delta_m=active_manual_delta, + solver_health_gates=solver_gates, + solver_health_passed=solver_health, + engineering_acceptance_gates=engineering_gates, + T_RTK_IMU=T_RTK_IMU, + T_IMU_RTK=T_IMU_RTK, + engineering_6dof_accepted=accepted, + blockers=blockers, + notes=( + "data_only_6dof_accepted=false is intentionally unchanged", + "R2V and HI13 absolute quaternion yaw are diagnostics only", + "GGA contributes XY only; BESTNAVA defines and constrains the ENU Z datum", + "BESTNAVA is selected before GGA; GGA fills only BESTNAVA gaps", + "gravity_candidate is not ZUPT; ZUPT additionally requires continuous near-zero Doppler", + "bootstrap preserves repeated-session multiplicity", + "engineering acceptance remains false until bootstrap and all 18 rotation perturbations complete", + "T_RTK_IMU origin is ANT1; l_I=p_ANT1^I and T_IMU_RTK translation equals l_I", + ), + translation_prior_applied=prior_solution is not None, + lever_prior_covariance_m2=prior_covariance, + mechanical_reference_l_I_m=manual, + free_solution=free_solution, + prior_constrained_solution=prior_solution, + free_to_mechanical_delta_m=free_manual_delta, + prior_to_mechanical_delta_m=prior_manual_delta, + free_translation_observable=free_translation_observable, + data_only_translation_accepted=free_translation_observable, + free_manual_mahalanobis_d2=free_manual_mahalanobis_d2, + free_translation_observability_gates=free_observability_gates, + posterior_to_prior_covariance_ratio=posterior_to_prior_covariance_ratio, + free_to_prior_residual_delta=free_to_prior_residual_delta, + mechanical_reference_solution=mechanical_solution, + residual_comparison=residual_comparison, + ) diff --git a/imu_lidar/rtk_imu_multisource.py b/imu_lidar/rtk_imu_multisource.py new file mode 100644 index 0000000..016ce1c --- /dev/null +++ b/imu_lidar/rtk_imu_multisource.py @@ -0,0 +1,558 @@ +"""Observable RTK--IMU rotation stages using native asynchronous measurements.""" + +from __future__ import annotations + +import csv +import json +from dataclasses import asdict, dataclass +from pathlib import Path + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial.transform import Rotation + +from .contracts import ImuSeries +from .geometry import so3_exp, so3_log +from .imu_preintegration import apply_bias_jacobian_correction, preintegrate_gyro +from .rtk_attitude import gnhpr_to_baseline_enu + + +@dataclass(frozen=True) +class UnifiedSession: + session_id: str + batch_id: str + imu: ImuSeries + imu_rpy_deg: np.ndarray + imu_quaternion_wxyz: np.ndarray + imu_host_receive_utc_s: np.ndarray + rtk_by_type: dict[str, list[dict[str, str]]] + + +@dataclass(frozen=True) +class R1bResult: + baseline_axis_imu: np.ndarray + tilt_yz_deg: np.ndarray + pair_count: int + residual_rms_deg: float + residual_p95_deg: float + covariance_deg2: np.ndarray + std_deg: np.ndarray + information_singular_values: np.ndarray + per_session_rms_deg: dict[str, float] + gyro_bias_by_session_rad_s: dict[str, np.ndarray] + ok: bool + notes: tuple[str, ...] + + +@dataclass(frozen=True) +class CompletedRotationResult: + method: str + R_RTK_IMU: np.ndarray | None + rpy_deg: np.ndarray | None + sample_count: int + session_count: int + residual_rms_deg: float + residual_p95_deg: float + covariance_deg2: np.ndarray + std_deg: np.ndarray + per_session_rms_deg: dict[str, float] + leave_one_session_delta_deg: dict[str, float] + block_out_delta_deg: dict[str, float] + convention: str + ok: bool + notes: tuple[str, ...] + + +@dataclass(frozen=True) +class R3Result: + r1b: R1bResult + r2v: CompletedRotationResult + r2g: CompletedRotationResult + r2v_r2g_delta_deg: float + full_rotation_accepted: bool + translation_unlocked: bool + blockers: tuple[str, ...] + + +@dataclass(frozen=True) +class _BaselinePair: + session_index: int + session_id: str + world_angle_rad: float + delta_R: np.ndarray + J_bg: np.ndarray + + +def _f(row: dict[str, str], key: str, default: float = np.nan) -> float: + try: + return float(row.get(key, "")) + except (TypeError, ValueError): + return default + + +def _truth(row: dict[str, str], key: str) -> bool: + return str(row.get(key, "")).strip().lower() in {"1", "true", "yes"} + + +def load_unified_sessions( + manifest_path: Path | str, + *, + selected_session_ids: set[str] | None = None, +) -> list[UnifiedSession]: + manifest_source = Path(manifest_path) + manifest = json.loads(manifest_source.read_text(encoding="utf-8")) + sessions: list[UnifiedSession] = [] + for entry in manifest["sessions"]: + session_id = str(entry["session_id"]) + if selected_session_ids and session_id not in selected_session_ids: + continue + directory = Path(entry["directory"]) + if not directory.is_absolute(): + directory = manifest_source.parent / directory + with np.load(directory / "imu.npz") as payload: + t = np.asarray(payload["system_time_s"], dtype=float) + gyro = np.asarray(payload["gyro_rad_s"], dtype=float) + accel = np.asarray(payload["accel_m_s2"], dtype=float) + rpy = np.asarray(payload["rpy_deg"], dtype=float) + quaternion = np.asarray(payload["quaternion_wxyz"], dtype=float) + host = np.asarray(payload["host_receive_utc_s"], dtype=float) + by_type: dict[str, list[dict[str, str]]] = {} + with (directory / "rtk.csv").open("r", encoding="utf-8", newline="") as stream: + for row in csv.DictReader(stream): + by_type.setdefault(row["message_type"], []).append(row) + sessions.append( + UnifiedSession( + session_id=session_id, + batch_id=str(entry["batch_id"]), + imu=ImuSeries(t_s=t, gyro_rad_s=gyro, acc_m_s2=accel), + imu_rpy_deg=rpy, + imu_quaternion_wxyz=quaternion, + imu_host_receive_utc_s=host, + rtk_by_type=by_type, + ) + ) + return sessions + + +def _valid_hpr(session: UnifiedSession) -> tuple[np.ndarray, np.ndarray]: + rows = [ + row for row in session.rtk_by_type.get("GNHPR", []) + if _truth(row, "checksum_valid") and int(_f(row, "heading_quality", -1)) == 4 + ] + if not rows: + return np.zeros(0), np.zeros((0, 3)) + t = np.asarray([_f(row, "t_device_s") for row in rows]) + baseline = gnhpr_to_baseline_enu( + np.asarray([_f(row, "heading_deg") for row in rows]), + np.asarray([_f(row, "pitch_deg") for row in rows]), + ) + finite = np.isfinite(t) & np.all(np.isfinite(baseline), axis=1) + t, baseline = t[finite], baseline[finite] + order = np.argsort(t) + t, baseline = t[order], baseline[order] + unique, indices = np.unique(t, return_index=True) + return unique, baseline[indices] + + +def _baseline_pairs(sessions: list[UnifiedSession]) -> list[_BaselinePair]: + pairs: list[_BaselinePair] = [] + for session_index, session in enumerate(sessions): + t, baseline = _valid_hpr(session) + if t.size < 3: + continue + dt = np.diff(t) + jump = np.degrees( + np.arccos(np.clip(np.sum(baseline[:-1] * baseline[1:], axis=1), -1.0, 1.0)) + ) + continuous = (dt >= 0.03) & (dt <= 0.25) & (jump / np.maximum(dt, 1e-6) <= 45.0) + last_anchor = -np.inf + for index, t0 in enumerate(t[:-1]): + if t0 - last_anchor < 1.0: + continue + last_anchor = t0 + for duration in (0.75, 1.5, 3.0): + target = t0 + duration + end = int(np.searchsorted(t, target)) + candidates = [candidate for candidate in (end - 1, end) if index < candidate < t.size] + if not candidates: + continue + j = min(candidates, key=lambda candidate: abs(t[candidate] - target)) + if abs((t[j] - t0) - duration) > 0.12 or not np.all(continuous[index:j]): + continue + try: + pre = preintegrate_gyro( + session.imu.t_s, session.imu.gyro_rad_s, float(t0), float(t[j]) + ) + except ValueError: + continue + world_angle = float( + np.arccos(np.clip(np.dot(baseline[index], baseline[j]), -1.0, 1.0)) + ) + if np.degrees(world_angle) < 0.4: + continue + pairs.append( + _BaselinePair( + session_index=session_index, + session_id=session.session_id, + world_angle_rad=world_angle, + delta_R=pre.delta_R, + J_bg=pre.J_bg, + ) + ) + return pairs + + +def _axis_from_parameters(parameters: np.ndarray) -> np.ndarray: + axis = np.asarray([1.0, parameters[0], parameters[1]], dtype=float) + return axis / np.linalg.norm(axis) + + +def solve_r1b(sessions: list[UnifiedSession]) -> R1bResult: + pairs = _baseline_pairs(sessions) + if len(pairs) < 20: + raise ValueError("R1b needs at least 20 continuous baseline/gyro motion pairs") + session_count = len(sessions) + pair_counts = { + session.session_id: sum(pair.session_id == session.session_id for pair in pairs) + for session in sessions + } + active_sessions = sum(count > 0 for count in pair_counts.values()) + target_count = len(pairs) / max(active_sessions, 1) + + def residual(parameters: np.ndarray) -> np.ndarray: + axis = _axis_from_parameters(parameters[:2]) + biases = parameters[2:].reshape(session_count, 3) + values = [] + for pair in pairs: + corrected = apply_bias_jacobian_correction( + pair.delta_R, pair.J_bg, biases[pair.session_index] + ) + body_angle = np.arccos( + np.clip(np.dot(axis, corrected @ axis), -1.0, 1.0) + ) + weight = np.sqrt(target_count / pair_counts[pair.session_id]) + values.append(weight * (body_angle - pair.world_angle_rad)) + values.extend((biases / 0.01).reshape(-1)) + # Weak 20-degree installation prior selects the physically known +X hemisphere. + values.extend(np.asarray(parameters[:2]) / np.tan(np.deg2rad(20.0))) + return np.asarray(values) + + initial = np.zeros(2 + 3 * session_count) + optimum = least_squares( + residual, initial, loss="huber", f_scale=np.deg2rad(0.25), max_nfev=120 + ) + axis = _axis_from_parameters(optimum.x[:2]) + biases = optimum.x[2:].reshape(session_count, 3) + errors = [] + per_session_values: dict[str, list[float]] = {} + for pair in pairs: + corrected = apply_bias_jacobian_correction( + pair.delta_R, pair.J_bg, biases[pair.session_index] + ) + body_angle = np.arccos(np.clip(np.dot(axis, corrected @ axis), -1.0, 1.0)) + error = float(np.degrees(body_angle - pair.world_angle_rad)) + errors.append(error) + per_session_values.setdefault(pair.session_id, []).append(error) + errors_array = np.asarray(errors) + data_rows = len(pairs) + jacobian = optimum.jac[:data_rows, :2] + information = jacobian.T @ jacobian + residual_variance = float(np.mean(np.deg2rad(errors_array) ** 2)) + covariance = residual_variance * np.linalg.pinv(information, rcond=1e-12) + covariance_deg2 = np.degrees(1.0) ** 2 * covariance + std_deg = np.sqrt(np.maximum(np.diag(covariance_deg2), 0.0)) + singular = np.linalg.svd(information, compute_uv=False) + rms = float(np.sqrt(np.mean(errors_array**2))) + p95 = float(np.percentile(np.abs(errors_array), 95.0)) + return R1bResult( + baseline_axis_imu=axis, + tilt_yz_deg=np.degrees(np.arctan(optimum.x[:2])), + pair_count=len(pairs), + residual_rms_deg=rms, + residual_p95_deg=p95, + covariance_deg2=covariance_deg2, + std_deg=std_deg, + information_singular_values=singular, + per_session_rms_deg={ + key: float(np.sqrt(np.mean(np.asarray(value) ** 2))) + for key, value in per_session_values.items() + }, + gyro_bias_by_session_rad_s={ + session.session_id: biases[index] for index, session in enumerate(sessions) + }, + ok=bool(rms <= 1.0 and p95 <= 2.0 and np.min(singular) >= 1e-3), + notes=( + "2DoF ANT1-to-ANT2 direction; +X hemisphere selected by installation knowledge", + "weak 20 deg prior is reported and prevents sign/gauge branch switching", + ), + ) + + +def _rotation_mean(matrices: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + mean = Rotation.from_matrix(matrices).mean().as_matrix() + errors = np.asarray( + [np.degrees(np.linalg.norm(so3_log(mean.T @ matrix))) for matrix in matrices] + ) + return mean, errors + + +def _result_from_samples( + method: str, + samples: list[tuple[str, str, np.ndarray]], + convention: str, + notes: tuple[str, ...], +) -> CompletedRotationResult: + if not samples: + return CompletedRotationResult( + method, None, None, 0, 0, np.nan, np.nan, np.full((3, 3), np.nan), + np.full(3, np.nan), + {}, {}, {}, convention, False, notes + ("no qualifying samples",) + ) + matrices = np.asarray([item[2] for item in samples]) + mean, errors = _rotation_mean(matrices) + rotvec = np.asarray([so3_log(mean.T @ matrix) for matrix in matrices]) + rotvec_deg = np.degrees(rotvec) + std = np.std(rotvec_deg, axis=0, ddof=1) if len(samples) > 1 else np.full(3, np.nan) + covariance = ( + np.cov(rotvec_deg, rowvar=False, ddof=1) / len(samples) + if len(samples) > 1 else np.full((3, 3), np.nan) + ) + ids = sorted({item[0] for item in samples}) + per_session = {} + loo = {} + for session_id in ids: + selected = [item[2] for item in samples if item[0] == session_id] + _, local_errors = _rotation_mean(np.asarray(selected)) + per_session[session_id] = float(np.sqrt(np.mean(local_errors**2))) + kept = np.asarray([item[2] for item in samples if item[0] != session_id]) + if kept.size: + kept_mean, _ = _rotation_mean(kept) + loo[session_id] = float(np.degrees(np.linalg.norm(so3_log(mean.T @ kept_mean)))) + block_groups = sorted({(item[0], item[1]) for item in samples}) + block_out = {} + for session_id, block_id in block_groups: + kept = np.asarray( + [item[2] for item in samples if (item[0], item[1]) != (session_id, block_id)] + ) + if kept.size: + kept_mean, _ = _rotation_mean(kept) + block_out[f"{session_id}:{block_id}"] = float( + np.degrees(np.linalg.norm(so3_log(mean.T @ kept_mean))) + ) + rms = float(np.sqrt(np.mean(errors**2))) + p95 = float(np.percentile(errors, 95.0)) + max_loo = max(loo.values(), default=np.inf) + ok = bool( + len(samples) >= 10 and len(ids) >= 2 and rms <= 2.0 and p95 <= 3.0 + and np.nanmax(std) <= 1.0 and max_loo <= 1.0 + ) + return CompletedRotationResult( + method=method, + R_RTK_IMU=mean, + rpy_deg=Rotation.from_matrix(mean).as_euler("xyz", degrees=True), + sample_count=len(samples), + session_count=len(ids), + residual_rms_deg=rms, + residual_p95_deg=p95, + covariance_deg2=covariance, + std_deg=std, + per_session_rms_deg=per_session, + leave_one_session_delta_deg=loo, + block_out_delta_deg=block_out, + convention=convention, + ok=ok, + notes=notes, + ) + + +def solve_r2g( + sessions: list[UnifiedSession], + r1b: R1bResult, + *, + level_static_session_ids: set[str], + block_duration_s: float = 10.0, +) -> CompletedRotationResult: + samples: list[tuple[str, str, np.ndarray]] = [] + right = r1b.baseline_axis_imu + for session in sessions: + if session.session_id not in level_static_session_ids: + continue + gyro_norm = np.linalg.norm(session.imu.gyro_rad_s, axis=1) + accel_norm = np.linalg.norm(session.imu.acc_m_s2, axis=1) + valid = (gyro_norm <= np.deg2rad(0.35)) & (np.abs(accel_norm - 9.80665) <= 0.15) + block = np.floor( + (session.imu.t_s - session.imu.t_s[0]) / block_duration_s + ).astype(int) + for block_id in np.unique(block[valid]): + selected = valid & (block == block_id) + if np.count_nonzero(selected) < 200: + continue + up = np.median(session.imu.acc_m_s2[selected], axis=0) + up /= np.linalg.norm(up) + up -= right * np.dot(up, right) + if np.linalg.norm(up) < 0.9: + continue + up /= np.linalg.norm(up) + forward = np.cross(up, right) + forward /= np.linalg.norm(forward) + C_IMU_RTK = np.column_stack([right, forward, up]) + samples.append((session.session_id, str(int(block_id)), C_IMU_RTK.T)) + return _result_from_samples( + "R2G_baseline_plus_level_gravity", + samples, + "accelerometer specific-force points vehicle up on explicit level-static blocks", + ( + "only caller-declared level-static sessions are eligible", + "result is level/gravity-prior constrained, not dual-antenna-only", + ), + ) + + +def _nearest_index(t: np.ndarray, value: float, tolerance: float) -> int | None: + index = int(np.searchsorted(t, value)) + candidates = [item for item in (index - 1, index) if 0 <= item < t.size] + if not candidates: + return None + best = min(candidates, key=lambda item: abs(t[item] - value)) + return best if abs(t[best] - value) <= tolerance else None + + +def solve_r2v( + sessions: list[UnifiedSession], + *, + min_speed_m_s: float = 1.5, + max_yaw_rate_deg_s: float = 3.0, + max_baseline_course_error_deg: float = 15.0, +) -> CompletedRotationResult: + candidates: dict[str, list[tuple[str, str, np.ndarray]]] = { + "HI13_q_body_to_ENU": [], + "NED_to_ENU_times_HI13_q": [], + "HI13_q_inverse_as_body_to_ENU": [], + "NED_to_ENU_times_HI13_q_inverse": [], + } + NED_TO_ENU = np.asarray([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, -1.0]]) + for session in sessions: + hpr_t, hpr_baseline = _valid_hpr(session) + if hpr_t.size == 0: + continue + quaternion = session.imu_quaternion_wxyz + norm = np.linalg.norm(quaternion, axis=1) + valid_quaternion = np.isfinite(norm) & (np.abs(norm - 1.0) <= 0.02) + normalized = quaternion / np.maximum(norm[:, None], 1e-12) + imu_rotations = Rotation.from_quat(normalized[:, [1, 2, 3, 0]]).as_matrix() + for row_index, row in enumerate(session.rtk_by_type.get("BESTNAVA", [])): + if not ( + _truth(row, "checksum_valid") + and _truth(row, "position_fixed") + and _truth(row, "doppler_velocity_valid") + and _f(row, "horizontal_speed_m_s") >= min_speed_m_s + and _f(row, "horizontal_speed_std_m_s") <= 0.25 + ): + continue + t = _f(row, "t_device_s") + hpr_index = _nearest_index(hpr_t, t, 0.15) + imu_index = _nearest_index(session.imu.t_s, t, 0.03) + if hpr_index is None or imu_index is None or not valid_quaternion[imu_index]: + continue + if abs(np.degrees(session.imu.gyro_rad_s[imu_index, 2])) > max_yaw_rate_deg_s: + continue + right = hpr_baseline[hpr_index] + forward = np.asarray( + [_f(row, "velocity_east_m_s"), _f(row, "velocity_north_m_s"), 0.0] + ) + forward /= np.linalg.norm(forward) + course_error = np.degrees( + np.arcsin(np.clip(abs(np.dot(right, forward)), 0.0, 1.0)) + ) + if course_error > max_baseline_course_error_deg: + continue + forward -= right * np.dot(right, forward) + forward /= np.linalg.norm(forward) + up = np.cross(right, forward) + if up[2] < 0: + forward = -forward + up = -up + up /= np.linalg.norm(up) + R_ENU_RTK = np.column_stack([right, forward, up]) + q = imu_rotations[imu_index] + world_candidates = { + "HI13_q_body_to_ENU": q, + "NED_to_ENU_times_HI13_q": NED_TO_ENU @ q, + "HI13_q_inverse_as_body_to_ENU": q.T, + "NED_to_ENU_times_HI13_q_inverse": NED_TO_ENU @ q.T, + } + block_id = str(row_index // 10) + for name, R_ENU_IMU in world_candidates.items(): + candidates[name].append( + (session.session_id, block_id, R_ENU_RTK.T @ R_ENU_IMU) + ) + diagnostics = { + name: _result_from_samples( + "R2V_baseline_plus_doppler_velocity", + values, + name, + ( + "RTK fixed + Doppler velocity + speed + low-yaw + baseline/course gates", + "HI13 absolute quaternion may contain magnetic/navigation yaw bias", + ), + ) + for name, values in candidates.items() + } + finite = [result for result in diagnostics.values() if result.sample_count] + if not finite: + return diagnostics["HI13_q_body_to_ENU"] + return min(finite, key=lambda result: result.residual_rms_deg) + + +def solve_r3( + sessions: list[UnifiedSession], + *, + level_static_session_ids: set[str], +) -> R3Result: + dynamic_sessions = [ + session for session in sessions if session.session_id not in level_static_session_ids + ] + r1b = solve_r1b(dynamic_sessions) + r2v = solve_r2v(dynamic_sessions) + r2g = solve_r2g(sessions, r1b, level_static_session_ids=level_static_session_ids) + if r2v.R_RTK_IMU is None or r2g.R_RTK_IMU is None: + delta = np.nan + else: + delta = float( + np.degrees(np.linalg.norm(so3_log(r2v.R_RTK_IMU.T @ r2g.R_RTK_IMU))) + ) + blockers = [] + if not r1b.ok: + blockers.append("R1b baseline direction failed residual/observability gates") + if not r2v.ok: + blockers.append("R2V velocity-completed rotation failed stability gates") + if not r2g.ok: + blockers.append("R2G gravity-completed rotation failed stability gates") + if not np.isfinite(delta) or delta > 2.0: + blockers.append("R2V and R2G disagree by more than 2 deg") + accepted = not blockers + return R3Result( + r1b=r1b, + r2v=r2v, + r2g=r2g, + r2v_r2g_delta_deg=delta, + full_rotation_accepted=accepted, + translation_unlocked=accepted, + blockers=tuple(blockers), + ) + + +def result_to_jsonable(result: R3Result) -> dict: + def convert(value): + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, np.generic): + return value.item() + if hasattr(value, "__dataclass_fields__"): + return {key: convert(item) for key, item in asdict(value).items()} + if isinstance(value, dict): + return {str(key): convert(item) for key, item in value.items()} + if isinstance(value, (tuple, list)): + return [convert(item) for item in value] + return value + return convert(result) diff --git a/imu_lidar/rtk_imu_node_graph.py b/imu_lidar/rtk_imu_node_graph.py new file mode 100644 index 0000000..6c1ebe4 --- /dev/null +++ b/imu_lidar/rtk_imu_node_graph.py @@ -0,0 +1,529 @@ +'''Per-GNSS-node RTK/IMU state graph used after legacy propagation deprecation.''' +from __future__ import annotations +from dataclasses import dataclass +import numpy as np +from scipy.optimize import least_squares +from scipy.sparse import lil_matrix +from .geometry import so3_exp, so3_log +from .imu_preintegration import apply_bias_correction_imu, residual_whiten_matrix +from .rtk_imu_engineering import ( + G_ENU, HPR_DIRECT_ANGULAR_SIGMA_RAD, _Segment, _world_rtk) + +NODE_DOF = 15 +SIGMA_BG_RW = 1e-5 +SIGMA_BA_RW = 1e-3 + +@dataclass(frozen=True) +class NodeGraphProblem: + segment: _Segment + R_seed_WI: tuple[np.ndarray,...] + fixed_l_I_m: np.ndarray + R_RTK_IMU: np.ndarray + hpr_direct_angular_sigma_rad: float = HPR_DIRECT_ANGULAR_SIGMA_RAD + +@dataclass(frozen=True) +class NodeGraphResult: + success: bool + message: str + node_count: int + duration_s: float + fixed_l_I_m: np.ndarray + initial_cost: float + final_cost: float + cost_reduction: float + nfev: int + optimality: float + gradient_norm: float + residual_dimension: int + state_dimension: int + statistical_dof: int + total_nis: float + chi_square_per_dof: float + cost_per_dof: float + initial_residual_by_factor: dict[str,dict[str,float]] + final_residual_by_factor: dict[str,dict[str,float]] + final_position_residual_m: dict[str,object] + final_velocity_residual_m_s: dict[str,object] + max_bg_step_rad_s: float + max_ba_step_m_s2: float + preintegration_covariance_sigma: dict[str,dict[str,float]] + + +@dataclass(frozen=True) +class FreeLeverResult: + success: bool + message: str + initial_l_I_m: np.ndarray + final_l_I_m: np.ndarray + lever_step_norm_m: float + initial_cost: float + final_cost: float + nfev: int + optimality: float + chi_square_per_dof: float + position_residual_m: dict[str,object] + velocity_residual_m_s: dict[str,object] + residual_by_factor: dict[str,dict[str,float]] + lever_covariance_m2: np.ndarray + lever_information_singular_values: np.ndarray + lever_information_condition_number: float + lever_precision_rank: int + weakest_lever_direction_I: np.ndarray + +def _rotation(problem,index,x): + offset = NODE_DOF*index + return problem.R_seed_WI[index] @ so3_exp(x[offset:offset+3]) + +def initial_parameters(problem, lever_override=None): + nodes = problem.segment.nodes + x = np.zeros(NODE_DOF*len(nodes)) + lever = (problem.fixed_l_I_m if lever_override is None + else np.asarray(lever_override,dtype=float)) + previous_p, previous_v = np.zeros(3), np.zeros(3) + for index,node in enumerate(nodes): + offset = NODE_DOF*index + R = problem.R_seed_WI[index] + previous_p = node.p_enu_m-R@lever + if index and not node.position_mask[2]: + previous_p[2] = x[offset-NODE_DOF+5] + if node.velocity_enu_m_s is not None: + previous_v = node.velocity_enu_m_s-R@np.cross(node.gyro_rad_s,lever) + x[offset+3:offset+6] = previous_p + x[offset+6:offset+9] = previous_v + return x + +def _stats(values, effective_dof=None): + a = np.asarray(values,dtype=float).reshape(-1) + if not a.size: + return {'count':0,'dof':0,'rms':np.nan,'p50_abs':np.nan, + 'p95_abs':np.nan,'p99_abs':np.nan,'nis':np.nan, + 'chi_square_per_dof':np.nan} + nis = float(np.dot(a,a)) + dof = int(a.size if effective_dof is None else effective_dof) + return {'count':int(a.size),'dof':dof, + 'rms':float(np.sqrt(np.mean(a*a))), + 'p50_abs':float(np.percentile(np.abs(a),50.)), + 'p95_abs':float(np.percentile(np.abs(a),95.)), + 'p99_abs':float(np.percentile(np.abs(a),99.)), + 'nis':nis,'chi_square_per_dof':nis/max(dof,1)} + +def _hpr_sigma(problem, node): + old = float(node.hpr_angular_sigma_rad) + extra_var = max(old*old-HPR_DIRECT_ANGULAR_SIGMA_RAD**2, 0.) + return float(np.sqrt(problem.hpr_direct_angular_sigma_rad**2+extra_var)) + + +def _distribution(values): + a = np.asarray(values,dtype=float).reshape(-1) + if not a.size: + return {'count':0,'rms':np.nan,'p50':np.nan,'p95':np.nan,'p99':np.nan} + return {'count':len(a),'rms':float(np.sqrt(np.mean(a*a))), + 'p50':float(np.percentile(a,50.)),'p95':float(np.percentile(a,95.)), + 'p99':float(np.percentile(a,99.))} + +def _vector_stats(values): + a = np.asarray(values,dtype=float).reshape(-1,3) + if not a.size: + return {'count':0,'axis_rms':[np.nan]*3,'axis_p95_abs':[np.nan]*3, + 'vector_rms':np.nan,'vector_p95':np.nan} + norm = np.linalg.norm(a,axis=1) + return {'count':len(a),'axis_rms':np.sqrt(np.mean(a*a,axis=0)), + 'axis_p95_abs':np.percentile(np.abs(a),95.,axis=0), + 'vector_rms':float(np.sqrt(np.mean(norm*norm))), + 'vector_p95':float(np.percentile(norm,95.))} + +def residual(problem,x,details=None,dependencies=None,lever_override=None): + nodes, preints = problem.segment.nodes, problem.segment.preintegrations + lever = (problem.fixed_l_I_m if lever_override is None + else np.asarray(lever_override,dtype=float)) + baseline_I = problem.R_RTK_IMU.T[:,0] + values = [] + def add(value,label,node_indices,raw=None): + a = np.asarray(value,dtype=float).reshape(-1) + values.extend(a) + if dependencies is not None: + dependencies.extend([tuple(node_indices)]*len(a)) + if details is not None: + details.setdefault(label,[]).extend(a.tolist()) + if raw is not None: details.setdefault(label+'_physical',[]).append(np.asarray(raw)) + for index,node in enumerate(nodes): + offset = NODE_DOF*index + R = _rotation(problem,index,x) + p, v = x[offset+3:offset+6], x[offset+6:offset+9] + bg, ba = x[offset+9:offset+12], x[offset+12:offset+15] + p_error = p+R@lever-node.p_enu_m + if node.source == 'GGA': + add(p_error[:2]/.06,'gga_xy',(index,)) + if details is not None: details.setdefault('position_physical',[]).append( + np.array([p_error[0],p_error[1],np.nan])) + else: + add(p_error/np.array([.06,.06,.12]),'best_position',(index,),p_error) + if details is not None: details.setdefault('position_physical',[]).append(p_error) + if node.velocity_enu_m_s is not None: + v_error = v+R@np.cross(node.gyro_rad_s-bg,lever)-node.velocity_enu_m_s + add(v_error/np.array([.15,.15,.30]),'doppler',(index,),v_error) + if details is not None: details.setdefault('velocity_physical',[]).append(v_error) + if node.hpr_factor_valid: + hpr_error=np.cross(R@baseline_I,node.baseline_enu) + add(hpr_error/_hpr_sigma(problem,node),'hpr',(index,),hpr_error) + if node.gravity_candidate: + gravity = node.accel_m_s2-ba-R.T@(-G_ENU) + add(gravity/.12,'gravity',(index,)) + if index == len(nodes)-1: continue + right = index+1 + right_offset = NODE_DOF*right + Rj = _rotation(problem,right,x) + pj = x[right_offset+3:right_offset+6] + vj = x[right_offset+6:right_offset+9] + bgj = x[right_offset+9:right_offset+12] + baj = x[right_offset+12:right_offset+15] + pre = preints[index] + dR,dv,dp = apply_bias_correction_imu(pre,bg,ba) + dt = pre.duration_s + imu_error = np.concatenate([ + so3_log(dR.T@R.T@Rj), + R.T@(vj-v-G_ENU*dt)-dv, + R.T@(pj-p-v*dt-.5*G_ENU*dt*dt)-dp]) + add(residual_whiten_matrix(pre.cov)@imu_error,'imu_preintegration', + (index,right),imu_error) + add((bgj-bg)/(SIGMA_BG_RW*np.sqrt(dt)),'gyro_bias_random_walk',(index,right)) + add((baj-ba)/(SIGMA_BA_RW*np.sqrt(dt)),'accel_bias_random_walk',(index,right)) + add(x[:3]/np.deg2rad(5.),'initial_attitude_gauge',(0,)) + add(x[9:12]/.02,'initial_gyro_bias',(0,)) + add(x[12:15]/.5,'initial_accel_bias',(0,)) + return np.asarray(values) + +def jacobian_sparsity(problem,x): + dependencies = [] + base = residual(problem,x,dependencies=dependencies) + sparsity = lil_matrix((len(base),len(x)),dtype=int) + for row,node_indices in enumerate(dependencies): + for index in node_indices: + start = NODE_DOF*index + sparsity[row,start:start+NODE_DOF] = 1 + return sparsity.tocsr() + +def _factor_stats(details): + return {key:_stats(value,2*len(value)//3 if key=='hpr' else None) + for key,value in details.items() + if not key.endswith('_physical') and key not in ('position_physical','velocity_physical')} + + +def _effective_residual_dimension(details): + return sum(2*len(v)//3 if k=='hpr' else len(v) + for k,v in details.items() if not k.endswith('_physical') + and k not in ('position_physical','velocity_physical')) + +def _preintegration_covariance_stats(problem): + blocks = {'rotation_rad':[],'velocity_m_s':[],'position_m':[]} + for pre in problem.segment.preintegrations: + sigma = np.sqrt(np.maximum(np.diag(pre.cov),0.)) + blocks['rotation_rad'].extend(sigma[:3]) + blocks['velocity_m_s'].extend(sigma[3:6]) + blocks['position_m'].extend(sigma[6:9]) + return {key:_distribution(value) for key,value in blocks.items()} + +def build_problem(segment,R_RTK_IMU,fixed_l_I_m, + hpr_direct_angular_sigma_rad=HPR_DIRECT_ANGULAR_SIGMA_RAD): + seeds = [] + for index,node in enumerate(segment.nodes): + if node.hpr_factor_valid: + seeds.append(_world_rtk(node.baseline_enu)@R_RTK_IMU) + elif index: + seeds.append(seeds[-1]@segment.preintegrations[index-1].delta_R) + else: + seeds.append(segment.R_WRTK_initial@R_RTK_IMU) + return NodeGraphProblem(segment,tuple(seeds),np.asarray(fixed_l_I_m,dtype=float), + np.asarray(R_RTK_IMU,dtype=float), + float(hpr_direct_angular_sigma_rad)) + +def solve_fixed_lever(problem,max_nfev=30): + x0 = initial_parameters(problem) + initial_detail = {} + r0 = residual(problem,x0,initial_detail) + fit = least_squares( + lambda value:residual(problem,value),x0,jac='2-point', + jac_sparsity=jacobian_sparsity(problem,x0),method='trf', + tr_solver='lsmr',loss='linear',max_nfev=max_nfev, + x_scale='jac',ftol=1e-6,xtol=1e-6,gtol=1e-6) + final_detail = {} + rf = residual(problem,fit.x,final_detail) + statistical_dof = max(_effective_residual_dimension(final_detail)-len(fit.x),1) + bg = fit.x.reshape(-1,NODE_DOF)[:,9:12] + ba = fit.x.reshape(-1,NODE_DOF)[:,12:15] + bg_step = np.diff(bg,axis=0) + ba_step = np.diff(ba,axis=0) + return NodeGraphResult( + success=bool(fit.success),message=str(fit.message), + node_count=len(problem.segment.nodes), + duration_s=problem.segment.nodes[-1].t_s-problem.segment.nodes[0].t_s, + fixed_l_I_m=problem.fixed_l_I_m.copy(), + initial_cost=.5*float(np.dot(r0,r0)),final_cost=.5*float(np.dot(rf,rf)), + cost_reduction=.5*float(np.dot(r0,r0)-np.dot(rf,rf)), + nfev=int(fit.nfev),optimality=float(fit.optimality), + gradient_norm=float(np.linalg.norm(fit.grad)), + residual_dimension=len(rf),state_dimension=len(fit.x), + statistical_dof=statistical_dof,total_nis=float(np.dot(rf,rf)), + chi_square_per_dof=float(np.dot(rf,rf)/statistical_dof), + cost_per_dof=.5*float(np.dot(rf,rf)/statistical_dof), + initial_residual_by_factor=_factor_stats(initial_detail), + final_residual_by_factor=_factor_stats(final_detail), + final_position_residual_m=_vector_stats(final_detail.get('position_physical',[])), + final_velocity_residual_m_s=_vector_stats(final_detail.get('velocity_physical',[])), + max_bg_step_rad_s=float(np.max(np.linalg.norm(bg_step,axis=1))) if len(bg_step) else 0., + max_ba_step_m_s2=float(np.max(np.linalg.norm(ba_step,axis=1))) if len(ba_step) else 0., + preintegration_covariance_sigma=_preintegration_covariance_stats(problem)) + + +def fit_states_at_fixed_lever(problem,l_I_m,max_nfev=50,initial_state_values=None): + lever=np.asarray(l_I_m,dtype=float) + x0=(initial_parameters(problem,lever) if initial_state_values is None + else np.asarray(initial_state_values,dtype=float)) + r0=residual(problem,x0,lever_override=lever) + fit=least_squares( + lambda value:residual(problem,value,lever_override=lever),x0,jac='2-point', + jac_sparsity=jacobian_sparsity(problem,x0),method='trf',tr_solver='lsmr', + loss='linear',max_nfev=max_nfev,x_scale='jac', + ftol=1e-6,xtol=1e-6,gtol=1e-6) + return fit.x,{'success':bool(fit.success),'message':str(fit.message), + 'nfev':int(fit.nfev),'initial_cost':.5*float(r0@r0), + 'cost':float(fit.cost)} + + +def summarize_fixed_state_values(problems,state_values,l_I_m): + details={}; residuals=[] + for problem,value in zip(problems,state_values): + local={} + residuals.append(residual(problem,np.asarray(value),details=local, + lever_override=l_I_m)) + for key,items in local.items(): details.setdefault(key,[]).extend(items) + joined=np.concatenate(residuals) + state_dimension=sum(len(value) for value in state_values) + dof=max(_effective_residual_dimension(details)-state_dimension,1) + return {'cost':.5*float(np.dot(joined,joined)), + 'total_nis':float(np.dot(joined,joined)), + 'chi_square_per_dof':float(np.dot(joined,joined)/dof), + 'statistical_dof':dof,'residual_by_factor':_factor_stats(details), + 'best_position_physical_m':_vector_stats(details.get('best_position_physical',[])), + 'doppler_physical_m_s':_vector_stats(details.get('doppler_physical',[])), + 'hpr_physical_rad':_vector_stats(details.get('hpr_physical',[]))} + +def solve_fixed_lever_many(problems,max_nfev=30): + problems = tuple(problems) + sizes = [NODE_DOF*len(problem.segment.nodes) for problem in problems] + offsets = np.cumsum([0,*sizes]) + x0 = np.concatenate([initial_parameters(problem) for problem in problems]) + def evaluate(value,details=None): + chunks = [] + for index,problem in enumerate(problems): + local_details = {} if details is not None else None + chunks.append(residual(problem,value[offsets[index]:offsets[index+1]], + local_details)) + if details is not None: + for key,items in local_details.items(): + details.setdefault(key,[]).extend(items) + return np.concatenate(chunks) + initial_detail = {} + r0 = evaluate(x0,initial_detail) + sparsity = lil_matrix((len(r0),len(x0)),dtype=int) + row = 0 + for index,problem in enumerate(problems): + local_x = x0[offsets[index]:offsets[index+1]] + local = jacobian_sparsity(problem,local_x) + sparsity[row:row+local.shape[0],offsets[index]:offsets[index+1]] = local + row += local.shape[0] + fit = least_squares( + lambda value:evaluate(value),x0,jac='2-point',jac_sparsity=sparsity.tocsr(), + method='trf',tr_solver='lsmr',loss='linear',max_nfev=max_nfev, + x_scale='jac',ftol=1e-6,xtol=1e-6,gtol=1e-6) + final_detail = {} + rf = evaluate(fit.x,final_detail) + bg_steps, ba_steps = [], [] + for index,problem in enumerate(problems): + states = fit.x[offsets[index]:offsets[index+1]].reshape(-1,NODE_DOF) + bg_steps.extend(np.linalg.norm(np.diff(states[:,9:12],axis=0),axis=1)) + ba_steps.extend(np.linalg.norm(np.diff(states[:,12:15],axis=0),axis=1)) + covariance = {'rotation_rad':[],'velocity_m_s':[],'position_m':[]} + for problem in problems: + for pre in problem.segment.preintegrations: + sigma = np.sqrt(np.maximum(np.diag(pre.cov),0.)) + covariance['rotation_rad'].extend(sigma[:3]) + covariance['velocity_m_s'].extend(sigma[3:6]) + covariance['position_m'].extend(sigma[6:9]) + dof = max(_effective_residual_dimension(final_detail)-len(fit.x),1) + return NodeGraphResult( + success=bool(fit.success),message=str(fit.message), + node_count=sum(len(problem.segment.nodes) for problem in problems), + duration_s=sum(problem.segment.nodes[-1].t_s-problem.segment.nodes[0].t_s + for problem in problems), + fixed_l_I_m=problems[0].fixed_l_I_m.copy(), + initial_cost=.5*float(np.dot(r0,r0)),final_cost=.5*float(np.dot(rf,rf)), + cost_reduction=.5*float(np.dot(r0,r0)-np.dot(rf,rf)), + nfev=int(fit.nfev),optimality=float(fit.optimality), + gradient_norm=float(np.linalg.norm(fit.grad)), + residual_dimension=len(rf),state_dimension=len(fit.x), + statistical_dof=dof,total_nis=float(np.dot(rf,rf)), + chi_square_per_dof=float(np.dot(rf,rf)/dof), + cost_per_dof=.5*float(np.dot(rf,rf)/dof), + initial_residual_by_factor=_factor_stats(initial_detail), + final_residual_by_factor=_factor_stats(final_detail), + final_position_residual_m=_vector_stats(final_detail.get('position_physical',[])), + final_velocity_residual_m_s=_vector_stats(final_detail.get('velocity_physical',[])), + max_bg_step_rad_s=float(max(bg_steps,default=0.)), + max_ba_step_m_s2=float(max(ba_steps,default=0.)), + preintegration_covariance_sigma={key:_distribution(value) for key,value in covariance.items()}) + + +def _free_residual(problem,value,details=None): + return residual(problem,value[3:],details=details,lever_override=value[:3]) + + +def _free_sparsity(problem,value): + local = jacobian_sparsity(problem,value[3:]) + result = lil_matrix((local.shape[0],local.shape[1]+3),dtype=int) + result[:,:3] = 1 + result[:,3:] = local + return result.tocsr() + + +def _marginal_lever_information(jacobian): + J = jacobian.toarray() if hasattr(jacobian,'toarray') else np.asarray(jacobian) + H = J.T@J + Hll,Hln,Hnn = H[:3,:3],H[:3,3:],H[3:,3:] + marginal = Hll-Hln@np.linalg.pinv(Hnn,rcond=1e-10)@Hln.T + return .5*(marginal+marginal.T) + + +def _additive_marginal_lever_information(jacobian,row_offsets,state_offsets): + total=np.zeros((3,3)) + for index in range(len(row_offsets)-1): + rows=slice(row_offsets[index],row_offsets[index+1]) + columns=np.r_[0:3,state_offsets[index]:state_offsets[index+1]] + local=jacobian[rows,:][:,columns] + total+=_marginal_lever_information(local) + return .5*(total+total.T) + + +def linearized_lever_information(problem,l_I_m): + lever=np.asarray(l_I_m,dtype=float) + value=np.concatenate([lever,initial_parameters(problem,lever)]) + fit=least_squares(lambda x:_free_residual(problem,x),value,jac='2-point', + jac_sparsity=_free_sparsity(problem,value),method='trf',tr_solver='lsmr', + loss='linear',max_nfev=1,x_scale='jac') + information=_marginal_lever_information(fit.jac) + _,singular,Vt=np.linalg.svd(information) + covariance=np.linalg.pinv(information,rcond=1e-9) + return information,covariance,singular,Vt[-1] + + +def solve_free_lever(problem,initial_l_I_m,max_nfev=120): + initial_l = np.asarray(initial_l_I_m,dtype=float) + x0 = np.concatenate([initial_l,initial_parameters(problem,initial_l)]) + r0 = _free_residual(problem,x0) + fit = least_squares( + lambda value:_free_residual(problem,value),x0,jac='2-point', + jac_sparsity=_free_sparsity(problem,x0),method='trf',tr_solver='lsmr', + loss='linear',max_nfev=max_nfev,x_scale='jac', + ftol=1e-6,xtol=1e-6,gtol=1e-6) + detail = {} + rf = _free_residual(problem,fit.x,detail) + information = _marginal_lever_information(fit.jac) + _,singular_values,Vt = np.linalg.svd(information) + tolerance = max(singular_values[0]*1e-9,1e-10) + rank = int(np.sum(singular_values>tolerance)) + covariance = np.linalg.pinv(information,rcond=1e-9) + dof = max(_effective_residual_dimension(detail)-len(fit.x),1) + condition = (float(singular_values[0]/singular_values[-1]) + if singular_values[-1]>tolerance else np.inf) + return FreeLeverResult( + success=bool(fit.success),message=str(fit.message), + initial_l_I_m=initial_l,final_l_I_m=fit.x[:3].copy(), + lever_step_norm_m=float(np.linalg.norm(fit.x[:3]-initial_l)), + initial_cost=.5*float(np.dot(r0,r0)), + final_cost=.5*float(np.dot(rf,rf)),nfev=int(fit.nfev), + optimality=float(fit.optimality),chi_square_per_dof=float(np.dot(rf,rf)/dof), + position_residual_m=_vector_stats(detail.get('position_physical',[])), + velocity_residual_m_s=_vector_stats(detail.get('velocity_physical',[])), + residual_by_factor=_factor_stats(detail),lever_covariance_m2=covariance, + lever_information_singular_values=singular_values, + lever_information_condition_number=condition,lever_precision_rank=rank, + weakest_lever_direction_I=Vt[-1].copy()) + + +def solve_free_lever_many(problems,initial_l_I_m,max_nfev=120, + initial_state_values=None,lever_prior_mean_m=None, + lever_prior_covariance_m2=None,return_state_values=False): + problems=tuple(problems) + initial_l=np.asarray(initial_l_I_m,dtype=float) + sizes=[NODE_DOF*len(problem.segment.nodes) for problem in problems] + offsets=np.cumsum([3,*sizes]) + states=([initial_parameters(problem,initial_l) for problem in problems] + if initial_state_values is None else + [np.asarray(value,dtype=float) for value in initial_state_values]) + prior_mean=(None if lever_prior_mean_m is None else + np.asarray(lever_prior_mean_m,dtype=float)) + prior_cov=(None if lever_prior_covariance_m2 is None else + np.asarray(lever_prior_covariance_m2,dtype=float)) + prior_whitener=(None if prior_cov is None else + np.linalg.inv(np.linalg.cholesky(prior_cov))) + x0=np.concatenate([initial_l,*states]) + def evaluate(value,details=None): + chunks=[] + for index,problem in enumerate(problems): + local={} if details is not None else None + chunks.append(residual(problem,value[offsets[index]:offsets[index+1]], + details=local,lever_override=value[:3])) + if details is not None: + for key,items in local.items(): details.setdefault(key,[]).extend(items) + if prior_whitener is not None: + prior_error=prior_whitener@(value[:3]-prior_mean) + chunks.append(prior_error) + if details is not None: + details.setdefault('lever_prior',[]).extend(prior_error.tolist()) + return np.concatenate(chunks) + r0=evaluate(x0) + sparsity=lil_matrix((len(r0),len(x0)),dtype=int) + row=0; row_offsets=[0] + for index,problem in enumerate(problems): + local=jacobian_sparsity(problem,states[index]) + sparsity[row:row+local.shape[0],:3]=1 + sparsity[row:row+local.shape[0],offsets[index]:offsets[index+1]]=local + row+=local.shape[0] + row_offsets.append(row) + if prior_whitener is not None: + sparsity[row:row+3,:3]=1 + fit=least_squares( + lambda value:evaluate(value),x0,jac='2-point',jac_sparsity=sparsity.tocsr(), + method='trf',tr_solver='lsmr',loss='linear',max_nfev=max_nfev, + x_scale='jac',ftol=1e-6,xtol=1e-6,gtol=1e-6) + detail={} + rf=evaluate(fit.x,detail) + information=_additive_marginal_lever_information( + fit.jac,row_offsets,offsets) + if prior_cov is not None: + information+=np.linalg.inv(prior_cov) + _,singular_values,Vt=np.linalg.svd(information) + tolerance=max(singular_values[0]*1e-9,1e-10) + rank=int(np.sum(singular_values>tolerance)) + covariance=np.linalg.pinv(information,rcond=1e-9) + dof=max(_effective_residual_dimension(detail)-len(fit.x),1) + condition=(float(singular_values[0]/singular_values[-1]) + if singular_values[-1]>tolerance else np.inf) + result=FreeLeverResult( + success=bool(fit.success),message=str(fit.message), + initial_l_I_m=initial_l,final_l_I_m=fit.x[:3].copy(), + lever_step_norm_m=float(np.linalg.norm(fit.x[:3]-initial_l)), + initial_cost=.5*float(np.dot(r0,r0)),final_cost=.5*float(np.dot(rf,rf)), + nfev=int(fit.nfev),optimality=float(fit.optimality), + chi_square_per_dof=float(np.dot(rf,rf)/dof), + position_residual_m=_vector_stats(detail.get('position_physical',[])), + velocity_residual_m_s=_vector_stats(detail.get('velocity_physical',[])), + residual_by_factor=_factor_stats(detail),lever_covariance_m2=covariance, + lever_information_singular_values=singular_values, + lever_information_condition_number=condition,lever_precision_rank=rank, + weakest_lever_direction_I=Vt[-1].copy()) + if return_state_values: + states=[fit.x[offsets[i]:offsets[i+1]].copy() + for i in range(len(problems))] + return result,states + return result diff --git a/imu_lidar/rtk_imu_replay.py b/imu_lidar/rtk_imu_replay.py index b08b254..2fa96dd 100644 --- a/imu_lidar/rtk_imu_replay.py +++ b/imu_lidar/rtk_imu_replay.py @@ -16,6 +16,15 @@ from .rtk_imu_translation import TranslationCalibrationResult, solve_rtk_imu_tra from .rtk_io import load_rtk_csv +DEFAULT_RTK_FRAME_DEFINITION = ( + "right-handed vehicle-fixed frame: +X ANT1(main,left)->ANT2(secondary,right), " + "+Y vehicle forward/IMU +Y, +Z vehicle up/IMU +Z" +) +DEFAULT_RTK_REFERENCE_POINT = ( + "GGA ANT1/main-antenna phase center, 1.916499878 m above ground" +) + + @dataclass(frozen=True) class InventoryEntry: session_id: str @@ -82,6 +91,7 @@ def dataset_audit(sessions: list[RotationSession]) -> dict[str, Any]: rtk = session.rtk valid_position = rtk.position_valid valid_attitude = rtk.attitude_valid & valid_position + float_attitude = rtk.attitude_float & valid_position rows.append( { "session_id": session.session_id, @@ -90,6 +100,8 @@ def dataset_audit(sessions: list[RotationSession]) -> dict[str, Any]: "rtk_samples": int(rtk.t_s.size), "fixed_position_ratio": float(np.mean(valid_position)), "fixed_attitude_ratio": float(np.mean(valid_attitude)), + "float_attitude_ratio": float(np.mean(float_attitude)), + "checksum_valid_ratio": float(np.mean(rtk.checksum_valid)), "common_time_span_s": [ float(max(session.imu.t_s[0], rtk.t_s[0])), float(min(session.imu.t_s[-1], rtk.t_s[-1])), @@ -109,8 +121,8 @@ def run_calibration( rotation_only: bool = False, compute_loo: bool = True, knot_step_s: float = 2.0, - rtk_frame_definition: str = '', - rtk_reference_point: str = '', + rtk_frame_definition: str = DEFAULT_RTK_FRAME_DEFINITION, + rtk_reference_point: str = DEFAULT_RTK_REFERENCE_POINT, ) -> tuple[RotationCalibrationResult, TranslationCalibrationResult | None]: """Run calibration and publish human-readable JSON artifacts.""" @@ -118,7 +130,7 @@ def run_calibration( output.mkdir(parents=True, exist_ok=True) rotation = solve_rtk_imu_rotation(sessions, compute_loo=compute_loo) translation = None - if not rotation_only: + if not rotation_only and rotation.ok: translation = solve_rtk_imu_translation( sessions, rotation, @@ -144,9 +156,9 @@ def run_calibration( ) blockers = [] if not rotation.ok: - blockers.append('rotation quality gates failed') + blockers.append('full RTK-to-IMU rotation is not observable from the lateral dual-antenna baseline') if translation is None or not translation.ok: - blockers.append('translation quality gates failed or were not run') + blockers.append('translation is frozen until a full rotation is observable and accepted') if not rtk_frame_definition.strip(): blockers.append('RTK frame_definition is empty') if not rtk_reference_point.strip(): diff --git a/imu_lidar/rtk_imu_rotation.py b/imu_lidar/rtk_imu_rotation.py index 76e1b96..2a3fbe1 100644 --- a/imu_lidar/rtk_imu_rotation.py +++ b/imu_lidar/rtk_imu_rotation.py @@ -2,7 +2,7 @@ from __future__ import annotations -from dataclasses import dataclass +from dataclasses import dataclass, replace import numpy as np from scipy.optimize import least_squares @@ -31,6 +31,28 @@ class TimeOffsetAudit: second_best_correlation: float evaluated_samples: int reliable: bool + method: str + peak_width_s: tuple[float, float] + per_session_offset_s: dict[str, float] + per_session_peak_correlation: dict[str, float] + + +@dataclass(frozen=True) +class BaselineConsistencyAudit: + """Two-DOF audit using only the physically observed ANT1-to-ANT2 axis.""" + + baseline_axis_imu: np.ndarray + pair_count: int + residual_rms_deg: float + residual_median_deg: float + residual_p95_deg: float + per_session_rms_deg: dict[str, float] + per_session_p95_deg: dict[str, float] + per_session_axis_rms_deg: dict[str, np.ndarray] + gyro_bias_by_session_rad_s: dict[str, np.ndarray] + worst_pairs: tuple[dict[str, object], ...] + ok: bool + notes: tuple[str, ...] @dataclass(frozen=True) @@ -50,6 +72,10 @@ class RotationCalibrationResult: information_singular_values: np.ndarray per_session_rms_deg: dict[str, float] loo_delta_deg: dict[str, float] + baseline_consistency: BaselineConsistencyAudit + observable_rotation_dof: int + full_attitude_observable: bool + legacy_full_attitude_numeric_ok: bool ok: bool notes: tuple[str, ...] @@ -62,6 +88,8 @@ class _Pair: delta_R_zero_bias: np.ndarray J_bg: np.ndarray weight: float + t0_s: float + t1_s: float def _attitude_rows(rtk: RtkSeries, convention: GnhprConvention) -> tuple[np.ndarray, np.ndarray]: @@ -91,14 +119,25 @@ def _attitude_rows(rtk: RtkSeries, convention: GnhprConvention) -> tuple[np.ndar return unique_t, rotations -def _rtk_angular_speed(t: np.ndarray, rotations: np.ndarray) -> tuple[np.ndarray, np.ndarray]: +def _rtk_heading_rate(t: np.ndarray, rotations: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Return signed vehicle yaw rate from the ANT1-to-ANT2 azimuth. + + ANT1-to-ANT2 points vehicle-right, so its clockwise heading increases when + mathematical body yaw decreases. Only this signed heading channel is + compared with IMU gyro_z; rotation about the baseline is unobservable. + """ + dt = np.diff(t) - valid = (dt >= 0.03) & (dt <= 0.5) - midpoint = 0.5 * (t[:-1] + t[1:]) - speed = np.array( - [np.linalg.norm(so3_log(rotations[i].T @ rotations[i + 1])) for i in range(t.size - 1)] - ) / np.maximum(dt, 1e-6) - return midpoint[valid], speed[valid] + baseline = rotations[:, :, 0] + heading = np.unwrap(np.arctan2(baseline[:, 0], baseline[:, 1])) + rate = -np.diff(heading) / np.maximum(dt, 1e-6) + valid = ( + (dt >= 0.03) + & (dt <= 0.25) + & (np.abs(rate) >= np.deg2rad(0.5)) + & (np.abs(rate) <= np.deg2rad(30.0)) + ) + return 0.5 * (t[:-1] + t[1:])[valid], rate[valid] def _correlation(a: np.ndarray, b: np.ndarray) -> float: @@ -115,45 +154,102 @@ def audit_time_offset( search_half_width_s: float = 0.30, step_s: float = 0.005, ) -> TimeOffsetAudit: - """Estimate residual ``t_IMU - t_RTK`` from invariant angular-speed norms.""" + """Audit residual t_IMU - t_RTK from signed heading rate. + + A broad correlation peak remains diagnostic. It is never applied unless + both peak separation and peak width pass. + """ offsets = np.arange(-search_half_width_s, search_half_width_s + 0.5 * step_s, step_s) - session_series: list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = [] + session_series: list[tuple[str, np.ndarray, np.ndarray, np.ndarray, np.ndarray]] = [] total_samples = 0 for session in sessions: t_rtk, rotations = _attitude_rows(session.rtk, GNHPR_CANDIDATES[0]) - midpoint, rtk_speed = _rtk_angular_speed(t_rtk, rotations) - imu_speed = np.linalg.norm(session.imu.gyro_rad_s, axis=1) - motion = rtk_speed > np.deg2rad(0.5) - midpoint = midpoint[motion] - rtk_speed = rtk_speed[motion] + midpoint, rtk_rate = _rtk_heading_rate(t_rtk, rotations) + imu_rate = session.imu.gyro_rad_s[:, 2] if midpoint.size >= 20: - session_series.append((midpoint, rtk_speed, session.imu.t_s, imu_speed)) + session_series.append( + (session.session_id, midpoint, rtk_rate, session.imu.t_s, imu_rate) + ) total_samples += int(midpoint.size) if not session_series: - return TimeOffsetAudit(0.0, np.nan, np.nan, 0, False) + return TimeOffsetAudit( + 0.0, + np.nan, + np.nan, + 0, + False, + "signed_heading_rate_vs_imu_gyro_z", + (np.nan, np.nan), + {}, + {}, + ) scores = [] + per_session_scores: dict[str, list[float]] = { + session_id: [] for session_id, *_ in session_series + } for offset in offsets: per_session = [] - for midpoint, rtk_speed, imu_t, imu_speed in session_series: + for session_id, midpoint, rtk_rate, imu_t, imu_rate in session_series: query = midpoint + offset inside = (query >= imu_t[0]) & (query <= imu_t[-1]) if np.count_nonzero(inside) < 20: + per_session_scores[session_id].append(np.nan) continue - interpolated = np.interp(query[inside], imu_t, imu_speed) - value = _correlation(rtk_speed[inside], interpolated) + interpolated = np.interp(query[inside], imu_t, imu_rate) + value = _correlation(rtk_rate[inside], interpolated) + per_session_scores[session_id].append(value) if np.isfinite(value): per_session.append(value) scores.append(float(np.median(per_session)) if per_session else np.nan) values = np.asarray(scores, dtype=float) if not np.any(np.isfinite(values)): - return TimeOffsetAudit(0.0, np.nan, np.nan, total_samples, False) + return TimeOffsetAudit( + 0.0, + np.nan, + np.nan, + total_samples, + False, + "signed_heading_rate_vs_imu_gyro_z", + (np.nan, np.nan), + {}, + {}, + ) best_index = int(np.nanargmax(values)) exclusion = np.abs(offsets - offsets[best_index]) >= 0.03 second = float(np.nanmax(values[exclusion])) if np.any(np.isfinite(values[exclusion])) else np.nan peak = float(values[best_index]) - reliable = bool(peak >= 0.35 and (not np.isfinite(second) or peak - second >= 0.015)) - return TimeOffsetAudit(float(offsets[best_index]), peak, second, total_samples, reliable) + near_peak = np.flatnonzero(values >= peak - 0.005) + peak_width = ( + (float(offsets[near_peak[0]]), float(offsets[near_peak[-1]])) + if near_peak.size + else (np.nan, np.nan) + ) + per_session_offset = {} + per_session_peak = {} + for session_id, session_values in per_session_scores.items(): + array = np.asarray(session_values, dtype=float) + if np.any(np.isfinite(array)): + index = int(np.nanargmax(array)) + per_session_offset[session_id] = float(offsets[index]) + per_session_peak[session_id] = float(array[index]) + reliable = bool( + peak >= 0.5 + and (not np.isfinite(second) or peak - second >= 0.015) + and np.isfinite(peak_width[0]) + and peak_width[1] - peak_width[0] <= 0.03 + ) + return TimeOffsetAudit( + float(offsets[best_index]), + peak, + second, + total_samples, + reliable, + "signed_heading_rate_vs_imu_gyro_z", + peak_width, + per_session_offset, + per_session_peak, + ) def _nearest_index(times: np.ndarray, target: float) -> int: @@ -175,6 +271,17 @@ def _make_pairs( cache = {} if preintegration_cache is None else preintegration_cache for session_index, session in enumerate(sessions): t, rotations = _attitude_rows(session.rtk, convention) + dt = np.diff(t) + baseline = rotations[:, :, 0] + baseline_step = np.arccos( + np.clip(np.sum(baseline[:-1] * baseline[1:], axis=1), -1.0, 1.0) + ) + broken_edge = ( + (dt < 0.03) + | (dt > 0.25) + | (baseline_step / np.maximum(dt, 1e-6) > np.deg2rad(45.0)) + ) + broken_prefix = np.concatenate([[0], np.cumsum(broken_edge.astype(int))]) next_anchor = float(t[0]) for i in range(t.size - 1): if t[i] + 1e-9 < next_anchor: @@ -184,6 +291,8 @@ def _make_pairs( j = _nearest_index(t, float(t[i] + duration)) if j <= i or abs(float(t[j] - t[i]) - duration) > 0.18: continue + if broken_prefix[j] - broken_prefix[i] != 0: + continue imu_t0 = float(t[i] + time_offset_s) imu_t1 = float(t[j] + time_offset_s) if imu_t0 < session.imu.t_s[0] or imu_t1 > session.imu.t_s[-1]: @@ -212,9 +321,25 @@ def _make_pairs( delta_R_zero_bias=preint.delta_R, J_bg=preint.J_bg, weight=weight, + t0_s=float(t[i]), + t1_s=float(t[j]), ) ) - return pairs + # Equalize total influence per session. Pair count and excitation otherwise + # let long/high-motion sessions dominate the shared rotation. + totals = { + session.session_id: sum( + pair.weight for pair in pairs if pair.session_id == session.session_id + ) + for session in sessions + } + nonzero = [value for value in totals.values() if value > 0.0] + target = float(np.mean(nonzero)) if nonzero else 1.0 + return [ + replace(pair, weight=pair.weight * target / totals[pair.session_id]) + for pair in pairs + if totals[pair.session_id] > 0.0 + ] def _solve_core(sessions: list[RotationSession], pairs: list[_Pair]) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: @@ -290,6 +415,129 @@ def _solve_core(sessions: list[RotationSession], pairs: list[_Pair]) -> tuple[np return r_x, biases, np.asarray(errors), covariance +def _solve_baseline_consistency( + sessions: list[RotationSession], + pairs: list[_Pair], +) -> BaselineConsistencyAudit: + """Audit the two physically observable dual-antenna rotation DOFs. + + The confirmed ANT1-to-ANT2 axis is IMU +X. For every interval, the angle + swept by the GNSS baseline must equal the angle swept by IMU +X under gyro + preintegration. Rotation about +X cancels from this invariant and is not + falsely scored as an RTK attitude residual. + """ + + baseline_axis = np.array([1.0, 0.0, 0.0]) + session_count = len(sessions) + + def raw_errors(parameters: np.ndarray) -> np.ndarray: + biases = parameters.reshape(session_count, 3) + values = [] + for pair in pairs: + corrected = apply_bias_jacobian_correction( + pair.delta_R_zero_bias, + pair.J_bg, + biases[pair.session_index], + ) + observed = np.arccos(np.clip(pair.R_A[0, 0], -1.0, 1.0)) + predicted = np.arccos( + np.clip(baseline_axis @ corrected @ baseline_axis, -1.0, 1.0) + ) + values.append(predicted - observed) + return np.asarray(values) + + def residual(parameters: np.ndarray) -> np.ndarray: + errors = raw_errors(parameters) + weighted = errors * np.sqrt(np.asarray([pair.weight for pair in pairs])) + return np.concatenate([weighted, parameters / 0.003]) + + initial = np.zeros(3 * session_count) + opt = least_squares( + residual, + initial, + loss="huber", + f_scale=np.deg2rad(0.25), + max_nfev=60, + ) + biases = opt.x.reshape(session_count, 3) + errors_deg = np.degrees(raw_errors(opt.x)) + per_session_rms = {} + per_session_p95 = {} + per_session_axis_rms = {} + for session in sessions: + selection = np.asarray( + [pair.session_id == session.session_id for pair in pairs], dtype=bool + ) + values = errors_deg[selection] + per_session_rms[session.session_id] = ( + float(np.sqrt(np.mean(values**2))) if values.size else np.nan + ) + per_session_p95[session.session_id] = ( + float(np.percentile(np.abs(values), 95.0)) if values.size else np.nan + ) + axis_errors = [] + for pair in np.asarray(pairs, dtype=object)[selection]: + corrected = apply_bias_jacobian_correction( + pair.delta_R_zero_bias, + pair.J_bg, + biases[pair.session_index], + ) + axis_errors.append(np.degrees(so3_log(pair.R_A @ corrected.T))) + per_session_axis_rms[session.session_id] = ( + np.sqrt(np.mean(np.asarray(axis_errors) ** 2, axis=0)) + if axis_errors + else np.full(3, np.nan) + ) + worst_indices = np.argsort(np.abs(errors_deg))[-20:][::-1] + worst_pairs = tuple( + { + "session_id": pairs[index].session_id, + "t0_s": pairs[index].t0_s, + "t1_s": pairs[index].t1_s, + "duration_s": pairs[index].t1_s - pairs[index].t0_s, + "baseline_angle_residual_deg": float(errors_deg[index]), + } + for index in worst_indices + ) + rms = float(np.sqrt(np.mean(errors_deg**2))) + median = float(np.median(np.abs(errors_deg))) + p95 = float(np.percentile(np.abs(errors_deg), 95.0)) + finite_session_rms = [ + value for value in per_session_rms.values() if np.isfinite(value) + ] + finite_session_p95 = [ + value for value in per_session_p95.values() if np.isfinite(value) + ] + ok = bool( + len(pairs) >= 20 + and rms <= 1.0 + and p95 <= 2.0 + and (not finite_session_rms or max(finite_session_rms) <= 1.5) + and (not finite_session_p95 or max(finite_session_p95) <= 3.0) + ) + return BaselineConsistencyAudit( + baseline_axis_imu=baseline_axis, + pair_count=len(pairs), + residual_rms_deg=rms, + residual_median_deg=median, + residual_p95_deg=p95, + per_session_rms_deg=per_session_rms, + per_session_p95_deg=per_session_p95, + per_session_axis_rms_deg=per_session_axis_rms, + gyro_bias_by_session_rad_s={ + session.session_id: biases[index].copy() + for index, session in enumerate(sessions) + }, + worst_pairs=worst_pairs, + ok=ok, + notes=( + "ANT1(main,left)->ANT2(secondary,right) is fixed to IMU +X", + "axis residual XYZ labels are baseline-spin(unobservable), baseline-elevation, heading", + "full rotation about the baseline is not identifiable from two antennas", + ), + ) + + def solve_rtk_imu_rotation( sessions: list[RotationSession] | tuple[RotationSession, ...], *, @@ -351,41 +599,28 @@ def solve_rtk_imu_rotation( per_session[session.session_id] = ( float(np.sqrt(np.mean(np.asarray(values) ** 2))) if values else np.nan ) + baseline_audit = _solve_baseline_consistency(items, pairs) loo = {} if compute_loo and len(items) >= 3: for omitted in items: + kept_items = [item for item in items if item.session_id != omitted.session_id] + kept_index = {item.session_id: index for index, item in enumerate(kept_items)} kept_pairs = [ - pair + replace(pair, session_index=kept_index[pair.session_id]) for pair in pairs if pair.session_id != omitted.session_id ] if len(kept_pairs) < 6: loo[omitted.session_id] = np.nan continue - def loo_residual(rotvec: np.ndarray) -> np.ndarray: - candidate = orthonormalize_rotation(so3_exp(rotvec)) - rows = [] - for pair in kept_pairs: - corrected = apply_bias_jacobian_correction( - pair.delta_R_zero_bias, - pair.J_bg, - biases[pair.session_index], - ) - rows.append( - np.sqrt(pair.weight) - * so3_log(candidate.T @ pair.R_A @ candidate @ corrected.T) - ) - return np.concatenate(rows) - - loo_opt = least_squares( - loo_residual, - so3_log(rotation), - loss='huber', - f_scale=np.deg2rad(0.5), - max_nfev=30, + try: + loo_rotation, _, _, _ = _solve_core(kept_items, kept_pairs) + except ValueError: + loo[omitted.session_id] = np.nan + continue + loo[omitted.session_id] = float( + np.degrees(np.linalg.norm(so3_log(rotation.T @ loo_rotation))) ) - loo_rotation = orthonormalize_rotation(so3_exp(loo_opt.x)) - loo[omitted.session_id] = float(np.degrees(np.linalg.norm(so3_log(rotation.T @ loo_rotation)))) rotation_cov = covariance[:3, :3] std_deg = np.degrees(np.sqrt(np.maximum(np.diag(rotation_cov), 0.0))) singular_values = np.linalg.svd(np.linalg.pinv(rotation_cov, rcond=1e-12), compute_uv=False) @@ -395,7 +630,7 @@ def solve_rtk_imu_rotation( finite_loo = [value for value in loo.values() if np.isfinite(value)] sorted_scores = sorted(scores.values()) convention_gap = sorted_scores[1] - sorted_scores[0] if len(sorted_scores) > 1 else np.inf - ok = bool( + legacy_numeric_ok = bool( len(pairs) >= 20 and rms <= 1.0 and p95 <= 2.0 @@ -403,19 +638,29 @@ def solve_rtk_imu_rotation( and (not finite_loo or max(finite_loo) <= 1.0) and convention_gap >= 0.05 ) + # GNHPR supplies the ANT1-to-ANT2 direction but no independent rotation + # about that direction. A completed 3-D attitude is useful diagnostically, + # but cannot pass the full extrinsic-rotation gate from this dataset alone. + full_attitude_observable = False + ok = False notes = [ "transform convention: p_RTK = R_RTK_IMU p_IMU", f"residual time convention: t_IMU = t_RTK + {offset:+.6f} s", f"GNHPR convention score gap={convention_gap:.4f} deg", "GNHPR alternatives use zero-bias prescreen scores; only the winner is jointly refined", - "LOO is conditional: per-session gyro biases are held at their all-session estimates", + "LOO re-optimizes the remaining per-session gyro biases", + "legacy full-HPR rotation uses a zero-roll gauge completion and is diagnostic only", + "dual antennas do not observe rotation about the ANT1-to-ANT2 baseline", ] if not time_audit.reliable: notes.append("time-offset correlation was ambiguous; held residual offset at zero") if convention is not GNHPR_CANDIDATES[0]: notes.append("empirical best GNHPR convention differs from protocol expectation; manual verification required") - if not ok: - notes.append("rotation failed one or more strict acceptance gates") + if not baseline_audit.ok: + notes.append("the physically observable baseline consistency failed strict gates") + if not legacy_numeric_ok: + notes.append("the legacy gauge-completed rotation failed one or more numeric gates") + notes.append("full rotation is not accepted; translation must remain frozen") return RotationCalibrationResult( R_RTK_IMU=rotation, rpy_deg=rpy_deg_xyz(rotation), @@ -434,6 +679,10 @@ def solve_rtk_imu_rotation( information_singular_values=singular_values, per_session_rms_deg=per_session, loo_delta_deg=loo, + baseline_consistency=baseline_audit, + observable_rotation_dof=2, + full_attitude_observable=full_attitude_observable, + legacy_full_attitude_numeric_ok=legacy_numeric_ok, ok=ok, notes=tuple(notes), ) diff --git a/imu_lidar/rtk_io.py b/imu_lidar/rtk_io.py index 49341df..d9b63a5 100644 --- a/imu_lidar/rtk_io.py +++ b/imu_lidar/rtk_io.py @@ -22,23 +22,43 @@ class RtkSeries: roll_deg: np.ndarray fix_quality: np.ndarray heading_quality: np.ndarray + heading_satellites: np.ndarray + heading_age_s: np.ndarray hdop: np.ndarray + checksum_valid: np.ndarray origin_geodetic: tuple[float, float, float] source: Path @property def attitude_valid(self) -> np.ndarray: + """Strict fixed dual-antenna solutions suitable for calibration.""" + return ( np.isfinite(self.heading_deg) & np.isfinite(self.pitch_deg) & np.isfinite(self.roll_deg) - & np.isin(self.heading_quality, (4.0, 5.0)) + & (self.heading_quality == 4.0) + & self.checksum_valid + ) + + @property + def attitude_float(self) -> np.ndarray: + """Float solutions retained for diagnostics but never calibration.""" + + return ( + np.isfinite(self.heading_deg) + & np.isfinite(self.pitch_deg) + & np.isfinite(self.roll_deg) + & (self.heading_quality == 5.0) + & self.checksum_valid ) @property def position_valid(self) -> np.ndarray: - return np.all(np.isfinite(self.position_enu_m), axis=1) & np.isin( - self.fix_quality, (4.0, 5.0) + return ( + np.all(np.isfinite(self.position_enu_m), axis=1) + & (self.fix_quality == 4.0) + & self.checksum_valid ) @@ -88,7 +108,10 @@ def load_rtk_csv(path: Path | str) -> RtkSeries: roll_deg=_column(data, "roll_deg")[order], fix_quality=_column(data, "fix_quality", default=0.0)[order], heading_quality=_column(data, "heading_quality", default=0.0)[order], + heading_satellites=_column(data, "heading_satellites")[order], + heading_age_s=_column(data, "heading_age_s")[order], hdop=_column(data, "hdop")[order], + checksum_valid=_column(data, "checksum_valid", default=1.0)[order] == 1.0, origin_geodetic=origin, source=source, ) diff --git a/tests/test_g90_multisource.py b/tests/test_g90_multisource.py new file mode 100644 index 0000000..4fbfc6b --- /dev/null +++ b/tests/test_g90_multisource.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +import numpy as np + +from tools.rscap_v2.g90_rtk import ( + parse_bestnava, + parse_pvtslna, + unicore_checksum_valid, +) + + +BESTNAVA = ( + '#BESTNAVA,48,GPS,FINE,2430,552524000,0,0,18,8;SOL_COMPUTED,NARROW_INT,' + '30.46551864298,114.09274943336,29.6465,-15.1091,WGS84,0.0100,0.0091,' + '0.0279,"547",1.000,176.000,38,31,31,31,0,01,03,f3,SOL_COMPUTED,' + 'DOPPLER_VELOCITY,0.000,0.000,1.6286,81.665533,-0.0015,0.0320,0.0167' + '*3e7e4885' +) +PVTSLNA = ( + '#PVTSLNA,49,GPS,FINE,2430,552523000,0,0,18,42;NARROW_INT,29.6427,' + '30.46551666558,114.09273316982,0.0288,0.0098,0.0094,1.000,PSRDIFF,' + '29.2291,30.46551517805,114.09273145886,-15.1092,38,31,38,28,0.2276,' + '1.5569,-0.0014,NARROW_INT,0.8328,350.8610,-1.9697,37,31,31,31,' + '1.6227,1.3605,0.6398,1.0915,0.8843,5.0,28,10,25*00000000' +) + + +def test_bestnava_preserves_gnss_time_and_doppler_velocity() -> None: + assert unicore_checksum_valid(BESTNAVA) + row = parse_bestnava(BESTNAVA) + assert row["gnss_week"] == 2430 + assert row["gnss_tow_ms"] == 552524000 + assert row["position_fixed"] + assert row["doppler_velocity_valid"] + assert np.isclose(row["horizontal_speed_m_s"], 1.6286) + assert np.isclose( + np.hypot(row["velocity_east_m_s"], row["velocity_north_m_s"]), 1.6286 + ) + + +def test_pvtslna_preserves_quality_baseline_and_velocity() -> None: + row = parse_pvtslna(PVTSLNA) + assert row["position_fixed"] + assert row["heading_type"] == "NARROW_INT" + assert np.isclose(row["baseline_length_m"], 0.8328) + assert np.isclose(row["heading_deg"], 350.8610) + assert np.isclose(row["horizontal_speed_m_s"], np.hypot(0.2276, 1.5569)) diff --git a/tests/test_hi13_imu.py b/tests/test_hi13_imu.py index cef1798..e7a5934 100644 --- a/tests/test_hi13_imu.py +++ b/tests/test_hi13_imu.py @@ -5,7 +5,12 @@ from __future__ import annotations import struct from tools.rscap_v2.capture_format_v2 import CaptureFile, CaptureHeader, RawChunk -from tools.rscap_v2.hi13_imu import crc16_hi13, iter_hi13_imu_samples, parse_hi91_frame +from tools.rscap_v2.hi13_imu import ( + crc16_hi13, + iter_hi13_imu_samples, + parse_hi91_frame, + parse_hi91_sample, +) def _hi91_frame( @@ -13,6 +18,8 @@ def _hi91_frame( device_ms: int = 123456, accel_g=(0.0, 0.0, 1.0), gyro_dps=(1.0, -2.0, 3.0), + rpy_deg=(4.0, 5.0, 6.0), + quaternion_wxyz=(1.0, 0.0, 0.0, 0.0), ) -> bytes: payload = bytearray(76) payload[0] = 0x91 @@ -22,7 +29,8 @@ def _hi91_frame( struct.pack_into(" None: assert np.allclose(rotations[1][:, 0], [1.0, 0.0, 0.0], atol=1e-12) +def test_gnhpr_baseline_is_main_to_secondary_and_ignores_roll() -> None: + baseline = gnhpr_to_baseline_enu( + np.array([0.0, 90.0]), + np.array([30.0, 0.0]), + ) + assert np.allclose(baseline[0], [0.0, np.sqrt(0.75), 0.5], atol=1e-12) + assert np.allclose(baseline[1], [1.0, 0.0, 0.0], atol=1e-12) + + rotations = gnhpr_to_rotation_enu_rtk( + np.array([90.0, 90.0]), + np.zeros(2), + np.array([0.0, 45.0]), + ) + assert np.allclose(rotations[0], rotations[1], atol=1e-12) + + def test_rtk_loader_uses_hpr_measurement_time_for_attitude(tmp_path: Path) -> None: path = tmp_path / "rtk.csv" path.write_text( @@ -49,6 +65,21 @@ def test_rtk_loader_uses_hpr_measurement_time_for_attitude(tmp_path: Path) -> No assert np.all(loaded.attitude_valid) +def test_rtk_loader_accepts_only_checksum_valid_fixed_hpr(tmp_path: Path) -> None: + path = tmp_path / "rtk.csv" + path.write_text( + "t,lat_deg,lon_deg,altitude_m,fix_quality,heading_deg,pitch_deg,roll_deg," + "heading_quality,checksum_valid\n" + "0.0,30.0,114.0,20.0,4,10.0,0.0,0.0,4,1\n" + "0.1,30.0,114.0,20.0,4,11.0,0.0,0.0,5,1\n" + "0.2,30.0,114.0,20.0,4,12.0,0.0,0.0,4,0\n", + encoding="utf-8", + ) + loaded = load_rtk_csv(path) + assert loaded.attitude_valid.tolist() == [True, False, False] + assert loaded.attitude_float.tolist() == [False, True, False] + + def test_fast_local_preintegration_matches_reference() -> None: times = np.linspace(0.0, 1.0, 101) gyro = np.tile(np.array([0.03, -0.02, 0.15]), (times.size, 1)) diff --git a/tests/test_rtk_imu_engineering.py b/tests/test_rtk_imu_engineering.py new file mode 100644 index 0000000..72cbb3f --- /dev/null +++ b/tests/test_rtk_imu_engineering.py @@ -0,0 +1,535 @@ +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import pytest +from scipy.spatial.transform import Rotation + +from imu_lidar.contracts import ImuSeries +from imu_lidar.rtk_imu_engineering import ( + G0, + HPR_DIRECT_ANGULAR_SIGMA_RAD, + ResidualAudit, + _all_hpr, + _engineering_gates, + _enu, + _height_reference, + _hpr_angular_sigma_rad, + _marginal_lever_information, + _motion_flags, + _nodes, + _resample_segments_with_multiplicity, + _segments, + solve_engineering_6dof, +) +from imu_lidar.rtk_imu_multisource import UnifiedSession +from imu_lidar.rtk_imu_node_graph import ( + _additive_marginal_lever_information, + _free_residual, + _free_sparsity, + _hpr_sigma, + build_problem, + fit_states_at_fixed_lever, + initial_parameters as node_graph_initial_parameters, + jacobian_sparsity as node_graph_jacobian_sparsity, + residual as node_graph_residual, + solve_free_lever_many, +) +from tools.run_rtk_imu_mechanical_prior_heldout import _gate as heldout_gate +from tools.audit_rtk_imu_innovation_noise import _summarize as innovation_summary +from tools.audit_rtk_imu_propagation_bias_root_cause import _root_report + +EARTH_RADIUS_M = 6_378_137.0 + + +def _rows_from_motion( + session_id: str, + lever: np.ndarray, + R_RTK_IMU: np.ndarray, + rotation_at, + *, + duration_s: float = 10.0, + gga_altitude_offset_m: float | None = None, +) -> UnifiedSession: + imu_t = np.arange(0.0, duration_s + 0.005, 0.01) + rotations = Rotation.from_matrix(np.asarray([rotation_at(t) for t in imu_t])) + matrices = rotations.as_matrix() + relative = Rotation.from_matrix(np.einsum("nij,njk->nik", matrices[:-1].transpose(0, 2, 1), matrices[1:])) + gyro = np.empty((imu_t.size, 3)) + gyro[:-1] = relative.as_rotvec() / 0.01 + gyro[-1] = gyro[-2] + accel = np.einsum("nji,j->ni", matrices, np.array([0.0, 0.0, G0])) + + hpr_t = np.arange(0.0, duration_s + 0.001, 0.1) + rows_hpr: list[dict[str, str]] = [] + baseline_I = R_RTK_IMU.T[:, 0] + for t in hpr_t: + baseline = rotation_at(t) @ baseline_I + heading = np.degrees(np.arctan2(baseline[0], baseline[1])) % 360.0 + pitch = np.degrees(np.arcsin(np.clip(baseline[2], -1.0, 1.0))) + rows_hpr.append({ + "checksum_valid": "1", "heading_quality": "4", "t_device_s": str(t), + "heading_deg": str(heading), "pitch_deg": str(pitch), + }) + + rows_best: list[dict[str, str]] = [] + rows_gga: list[dict[str, str]] = [] + best_t = np.arange(0.0, duration_s + 0.001, 0.5) + dt_velocity = 1e-3 + for t in best_t: + R_WI = rotation_at(t) + position = R_WI @ lever + before = rotation_at(max(t - dt_velocity, 0.0)) @ lever + after = rotation_at(min(t + dt_velocity, duration_s)) @ lever + denominator = min(t + dt_velocity, duration_s) - max(t - dt_velocity, 0.0) + velocity = (after - before) / denominator + lat = position[1] / EARTH_RADIUS_M * 180.0 / np.pi + lon = position[0] / EARTH_RADIUS_M * 180.0 / np.pi + rows_best.append({ + "checksum_valid": "1", "position_fixed": "1", "t_device_s": str(t), + "lat_deg": str(lat), "lon_deg": str(lon), "altitude_m": str(50.0 + position[2]), + "doppler_velocity_valid": "1", "velocity_east_m_s": str(velocity[0]), + "velocity_north_m_s": str(velocity[1]), "vertical_speed_m_s": str(velocity[2]), + }) + if gga_altitude_offset_m is not None: + rows_gga.append({ + "checksum_valid": "1", "fix_quality": "4", "t_device_s": str(t), + "lat_deg": str(lat), "lon_deg": str(lon), + "altitude_msl_m": str(50.0 + position[2] + gga_altitude_offset_m), + }) + + rtk = {"BESTNAVA": rows_best, "GNHPR": rows_hpr} + if rows_gga: + rtk["GGA"] = rows_gga + return UnifiedSession( + session_id=session_id, + batch_id="synthetic", + imu=ImuSeries(t_s=imu_t, gyro_rad_s=gyro, acc_m_s2=accel), + imu_rpy_deg=np.zeros((imu_t.size, 3)), + imu_quaternion_wxyz=np.tile([1.0, 0.0, 0.0, 0.0], (imu_t.size, 1)), + imu_host_receive_utc_s=imu_t, + rtk_by_type=rtk, + ) + + +def _constant_velocity_session(speed_m_s: float = 3.0) -> UnifiedSession: + imu_t = np.arange(0.0, 5.01, 0.01) + rows_best = [] + rows_hpr = [] + for t in np.arange(0.0, 5.01, 0.1): + rows_hpr.append({ + "checksum_valid": "1", "heading_quality": "4", "t_device_s": str(t), + "heading_deg": "90", "pitch_deg": "0", + }) + for t in np.arange(0.0, 5.01, 0.5): + rows_best.append({ + "checksum_valid": "1", "position_fixed": "1", "t_device_s": str(t), + "lat_deg": "0", "lon_deg": str(speed_m_s * t / EARTH_RADIUS_M * 180.0 / np.pi), + "altitude_m": "50", "doppler_velocity_valid": "1", + "velocity_east_m_s": str(speed_m_s), "velocity_north_m_s": "0", + "vertical_speed_m_s": "0", + }) + return UnifiedSession( + session_id="constant_velocity", batch_id="synthetic", + imu=ImuSeries( + t_s=imu_t, gyro_rad_s=np.zeros((imu_t.size, 3)), + acc_m_s2=np.tile([0.0, 0.0, G0], (imu_t.size, 1)), + ), + imu_rpy_deg=np.zeros((imu_t.size, 3)), + imu_quaternion_wxyz=np.tile([1.0, 0.0, 0.0, 0.0], (imu_t.size, 1)), + imu_host_receive_utc_s=imu_t, + rtk_by_type={"BESTNAVA": rows_best, "GNHPR": rows_hpr}, + ) + + +def _audit(values: list[float]) -> ResidualAudit: + vector = np.asarray(values, dtype=float) + return ResidualAudit(20, vector, vector, float(np.linalg.norm(vector)), float(np.linalg.norm(vector))) + + +def test_constant_speed_straight_is_gravity_candidate_but_not_zupt() -> None: + session = _constant_velocity_session() + times = np.asarray([float(row["t_device_s"]) for row in session.rtk_by_type["BESTNAVA"]]) + velocities = np.asarray([[3.0, 0.0, 0.0]] * len(times)) + gravity_candidate, zupt_static = _motion_flags(session, 2.5, times, velocities) + assert gravity_candidate + assert not zupt_static + + +def test_bestnava_is_not_suppressed_by_earlier_gga_epochs() -> None: + session = _rows_from_motion( + "best_preferred", np.array([0.3, -0.2, 0.1]), np.eye(3), + lambda _: np.eye(3), gga_altitude_offset_m=100.0, + ) + for row in session.rtk_by_type["GGA"]: + row["t_device_s"] = str(float(row["t_device_s"]) - 0.05) + reference = _height_reference([session]) + assert reference is not None + nodes = _nodes(session, reference, 0.5) + assert nodes and all(node.source == "BESTNAVA" for node in nodes) + assert all(node.velocity_enu_m_s is not None for node in nodes) + +def test_gga_msl_altitude_never_enters_bestnava_z_reference() -> None: + lever = np.array([0.3, -0.2, 0.4]) + session = _rows_from_motion("mixed_height", lever, np.eye(3), lambda _: np.eye(3), + gga_altitude_offset_m=123.0) + reference = _height_reference([session]) + assert reference is not None and reference[2] == pytest.approx(50.4) + best, best_mask = _enu(session.rtk_by_type["BESTNAVA"][1], "BESTNAVA", reference) + gga, gga_mask = _enu(session.rtk_by_type["GGA"][1], "GGA", reference) + assert best_mask.tolist() == [True, True, True] + assert gga_mask.tolist() == [True, True, False] + assert best[2] == pytest.approx(0.0) + assert gga[2] == pytest.approx(0.0) + + +def test_schur_observability_uses_marginal_lever_information() -> None: + J_l = np.array([ + [1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 0.1], + [1.0, 1.0, 0.0], [1.0, 0.0, 0.0], + ]) + J_n = np.array([[1.0], [0.0], [0.0], [1.0], [0.0]]) + J = np.column_stack([J_l, J_n]) + marginal, singular, condition, rank, weakest, covariance = _marginal_lever_information(J, np.ones(4)) + H = J.T @ J + expected = H[:3, :3] - H[:3, 3:] @ np.linalg.pinv(H[3:, 3:]) @ H[3:, :3] + assert np.allclose(marginal, expected) + assert singular.shape == (3,) and rank == 3 and np.isfinite(condition) + assert abs(weakest[2]) > 0.99 + assert covariance.shape == (3, 3) + + +def test_bootstrap_resampling_preserves_session_multiplicity() -> None: + segments = [SimpleNamespace(session_id="a"), SimpleNamespace(session_id="b")] + sampled = _resample_segments_with_multiplicity(segments, ["a", "a", "b"]) + assert [segment.session_id for segment in sampled] == ["a", "a", "b"] + + +@pytest.mark.parametrize("fault", ["q5", "time_gap", "baseline_jump"]) +def test_isolated_hpr_faults_do_not_split_r0_trajectory(fault: str) -> None: + session = _constant_velocity_session(0.0) + if fault == "q5": + session.rtk_by_type["GNHPR"][25]["heading_quality"] = "5" + elif fault == "time_gap": + session.rtk_by_type["GNHPR"] = [ + row for row in session.rtk_by_type["GNHPR"] + if not 2.1 <= float(row["t_device_s"]) <= 2.9 + ] + else: + session.rtk_by_type["GNHPR"][25]["heading_deg"] = "270" + reference = _height_reference([session]) + assert reference is not None + nodes = _nodes(session, reference, 0.5) + assert nodes + assert all( + right.continuity_id == left.continuity_id + for left, right in zip(nodes[:-1], nodes[1:]) + ) + if fault == "baseline_jump": + assert any(not node.hpr_factor_valid and node.hpr_factor_method == "isolated_outlier" for node in nodes) + + +def test_hpr_bridge_covariance_is_weaker_than_direct_and_limited_to_half_second() -> None: + assert _hpr_angular_sigma_rad("nearest_q4", 0.0) == pytest.approx(HPR_DIRECT_ANGULAR_SIGMA_RAD) + assert _hpr_angular_sigma_rad("bracket_interpolation", 0.2) > HPR_DIRECT_ANGULAR_SIGMA_RAD + assert _hpr_angular_sigma_rad("bracket_interpolation", 0.5) >= _hpr_angular_sigma_rad( + "bracket_interpolation", 0.2 + ) + assert np.isinf(_hpr_angular_sigma_rad("bracket_interpolation", 0.500001)) + +def test_short_hpr_dropout_uses_interpolated_factor_without_splitting_r0() -> None: + session = _constant_velocity_session(0.0) + session.rtk_by_type["GNHPR"] = [ + row for row in session.rtk_by_type["GNHPR"] + if not 2.4 <= float(row["t_device_s"]) <= 2.6 + ] + reference = _height_reference([session]) + assert reference is not None + nodes = _nodes(session, reference, 0.5) + bridged = [node for node in nodes if abs(node.t_s - 2.5) < 1e-9] + assert len(bridged) == 1 + assert bridged[0].hpr_factor_valid + assert bridged[0].hpr_factor_method == "bracket_interpolation" + assert all(right.continuity_id == left.continuity_id for left, right in zip(nodes[:-1], nodes[1:])) + +def test_position_time_backwards_still_splits_r0_trajectory() -> None: + session = _constant_velocity_session(0.0) + session.rtk_by_type["BESTNAVA"][5]["t_device_s"] = "1.75" + reference = _height_reference([session]) + assert reference is not None + nodes = _nodes(session, reference, 0.5) + assert any( + right.continuity_id != left.continuity_id + for left, right in zip(nodes[:-1], nodes[1:]) + ) + + +def test_one_hz_timestamp_jitter_does_not_create_artificial_two_second_r0_gaps() -> None: + session = _constant_velocity_session(0.0) + rows = session.rtk_by_type["BESTNAVA"] + # Retain strict device-time monotonicity while making every second sample early. + for index, row in enumerate(rows): + row["t_device_s"] = str(float(row["t_device_s"]) - (0.002 if index % 2 else 0.0)) + reference = _height_reference([session]) + assert reference is not None + nodes = _nodes(session, reference, 1.0) + intervals = np.diff([node.t_s for node in nodes]) + assert len(nodes) == 6 + assert np.all(intervals > 0.75) + assert np.all(intervals < 1.25) + assert all(right.continuity_id == left.continuity_id for left, right in zip(nodes[:-1], nodes[1:])) + +def test_large_residuals_fail_engineering_acceptance() -> None: + gates = _engineering_gates( + np.array([0.01, 0.01, 0.01]), np.array([100.0, 10.0, 1.0]), 3, 100.0, + _audit([1.0, 1.0]), _audit([1.0, 1.0, 1.0]), _audit([2.0, 2.0, 2.0]), + {"a": 0.01, "b": 0.02, "c": 0.03}, np.array([0.01, 0.01, 0.01]), True, + 0.01, True, None, False, None, + ) + assert not gates["gga_xy_rms_p95"] + assert not gates["bestnava_xyz_rms_p95"] + assert not gates["doppler_velocity_rms_p95"] + assert not all(gates.values()) + + +def test_unobservable_free_solution_does_not_block_mechanical_prior_with_euclidean_delta() -> None: + gates = _engineering_gates( + np.array([0.01, 0.01, 0.01]), np.array([100.0, 10.0, 1.0]), 3, 100.0, + _audit([0.01, 0.01]), _audit([0.01, 0.01, 0.01]), _audit([0.01, 0.01, 0.01]), + {"a": 0.01, "b": 0.02, "c": 0.03}, np.array([0.01, 0.01, 0.01]), True, + 0.01, True, np.array([10.0, 10.0, 10.0]), False, None, + ) + assert gates["manual_lever_consistency"] is False + assert gates["free_manual_mahalanobis_consistency"] is True + observable_gates = _engineering_gates( + np.array([0.01, 0.01, 0.01]), np.array([100.0, 10.0, 1.0]), 3, 100.0, + _audit([0.01, 0.01]), _audit([0.01, 0.01, 0.01]), _audit([0.01, 0.01, 0.01]), + {"a": 0.01, "b": 0.02, "c": 0.03}, np.array([0.01, 0.01, 0.01]), True, + 0.01, True, None, True, 12.0, + ) + assert observable_gates["free_manual_mahalanobis_consistency"] is False +def test_yaw_only_recovers_xy_but_weak_z_is_rejected_and_transforms_are_inverse() -> None: + lever = np.array([0.30, -0.20, 0.10]) + session = _rows_from_motion( + "yaw", lever, np.eye(3), lambda t: Rotation.from_euler("z", 0.20 * t).as_matrix() + ) + result = solve_engineering_6dof( + [session], R_RTK_IMU=np.eye(3), sample_period_s=0.5, + run_loo=False, run_bootstrap=False, run_rotation_sensitivity=False, + ) + assert result.l_I_m is not None + assert np.allclose(result.l_I_m[:2], lever[:2], atol=0.05) + assert result.lever_precision_rank < 3 or not result.engineering_acceptance_gates["lever_marginal_std"] + assert not result.engineering_6dof_accepted + assert result.T_RTK_IMU is not None and result.T_IMU_RTK is not None + assert np.allclose(result.T_RTK_IMU @ result.T_IMU_RTK, np.eye(4), atol=1e-8) + + +def test_pitch_excitation_recovers_vertical_lever_arm() -> None: + lever = np.array([0.28, -0.16, 0.42]) + rotation_at = lambda t: Rotation.from_euler( + "xyz", [0.0, 0.12 * np.sin(0.55 * t), 0.16 * t] + ).as_matrix() + session = _rows_from_motion("pitch", lever, np.eye(3), rotation_at) + result = solve_engineering_6dof( + [session], R_RTK_IMU=np.eye(3), sample_period_s=0.5, + run_loo=False, run_bootstrap=False, run_rotation_sensitivity=False, + ) + assert result.l_I_m is not None + assert result.l_I_m[2] == pytest.approx(lever[2], abs=0.08) + + +def test_full_3d_excitation_with_nonzero_fixed_r2g_recovers_l_i() -> None: + lever = np.array([0.24, -0.18, 0.36]) + fixed_rotation = Rotation.from_euler("xyz", [0.454, -0.003, 0.012], degrees=True).as_matrix() + rotation_at = lambda t: Rotation.from_euler( + "xyz", [0.16 * np.sin(0.37 * t), 0.18 * np.sin(0.51 * t), 0.18 * t] + ).as_matrix() + session = _rows_from_motion( + "full_3d", lever, fixed_rotation, rotation_at, duration_s=15.0 + ) + result = solve_engineering_6dof( + [session], R_RTK_IMU=fixed_rotation, sample_period_s=0.5, + run_loo=False, run_bootstrap=False, run_rotation_sensitivity=False, + ) + assert result.l_I_m is not None + assert np.allclose(result.l_I_m, lever, atol=0.08) + assert np.allclose(result.R_RTK_IMU, fixed_rotation) + assert result.T_RTK_IMU is not None and result.T_IMU_RTK is not None + assert np.allclose(result.T_RTK_IMU @ result.T_IMU_RTK, np.eye(4), atol=1e-8) + +def test_mechanical_soft_prior_keeps_free_solution_and_reports_prior_solution() -> None: + lever = np.array([0.24, -0.18, 0.36]) + reference = np.array([0.30, -0.18, 0.36]) + rotation_at = lambda t: Rotation.from_euler( + "xyz", [0.14 * np.sin(0.37 * t), 0.16 * np.sin(0.51 * t), 0.18 * t] + ).as_matrix() + session = _rows_from_motion("prior", lever, np.eye(3), rotation_at, duration_s=12.0) + result = solve_engineering_6dof( + [session], R_RTK_IMU=np.eye(3), + manual_l_I_m=reference, + manual_l_I_covariance_m2=np.diag([0.02**2, 0.02**2, 0.02**2]), + sample_period_s=0.5, run_loo=False, run_bootstrap=False, + run_rotation_sensitivity=False, + ) + assert result.free_solution is not None + assert result.prior_constrained_solution is not None + assert result.translation_prior_applied + assert np.allclose(result.mechanical_reference_l_I_m, reference) + assert np.linalg.norm(result.prior_to_mechanical_delta_m) < np.linalg.norm(result.free_to_mechanical_delta_m) + assert np.allclose(result.l_I_m, result.prior_constrained_solution.l_I_m) + assert result.mechanical_reference_solution is not None + assert result.residual_comparison is not None + assert result.posterior_to_prior_covariance_ratio is not None + + +def test_manual_mean_and_covariance_must_be_provided_together() -> None: + session = _constant_velocity_session(0.0) + with pytest.raises(ValueError, match="must be provided together"): + solve_engineering_6dof([session], R_RTK_IMU=np.eye(3), manual_l_I_m=np.zeros(3)) + + +def test_blockwise_schur_information_is_additive_across_independent_segments() -> None: + rng = np.random.default_rng(7) + lever_blocks = [] + nuisance_blocks = [] + row_count = 30 + for scale in (1.0, 1e-3, 20.0): + lever_blocks.append(rng.normal(size=(row_count, 3)) * scale) + nuisance_blocks.append(rng.normal(size=(row_count, 15)) * scale) + jacobian = np.zeros((3 * row_count, 3 + 3 * 15)) + for index, (lever, nuisance) in enumerate(zip(lever_blocks, nuisance_blocks)): + rows = slice(index * row_count, (index + 1) * row_count) + columns = slice(3 + index * 15, 3 + (index + 1) * 15) + jacobian[rows, :3] = lever + jacobian[rows, columns] = nuisance + residual = np.ones(jacobian.shape[0]) + combined = _marginal_lever_information(jacobian, residual)[0] + expected = np.zeros((3, 3)) + for lever, nuisance in zip(lever_blocks, nuisance_blocks): + local = np.column_stack([lever, nuisance]) + expected += _marginal_lever_information(local, np.ones(row_count))[0] + assert np.allclose(combined, expected, rtol=1e-9, atol=1e-9) + + +def test_per_node_graph_has_finite_residual_and_matching_sparse_structure() -> None: + lever = np.array([0.24,-0.18,0.36]) + rotation_at = lambda t: Rotation.from_euler('z',.12*t).as_matrix() + session = _rows_from_motion('node_graph',lever,np.eye(3),rotation_at,duration_s=10.) + segments = _segments([session],.5) + assert segments + problem = build_problem(segments[0],np.eye(3),lever) + x0 = node_graph_initial_parameters(problem) + value = node_graph_residual(problem,x0) + sparsity = node_graph_jacobian_sparsity(problem,x0) + assert x0.size == 15*len(problem.segment.nodes) + assert np.all(np.isfinite(value)) + assert sparsity.shape == (value.size,x0.size) + assert sparsity.nnz > value.size + + +def test_node_graph_hpr_override_retains_bridge_extra_variance() -> None: + problem=SimpleNamespace(hpr_direct_angular_sigma_rad=.006) + direct=SimpleNamespace(hpr_angular_sigma_rad=HPR_DIRECT_ANGULAR_SIGMA_RAD) + assert _hpr_sigma(problem,direct)==pytest.approx(.006) + old=_hpr_angular_sigma_rad('bracket_interpolation',.4) + bridge=SimpleNamespace(hpr_angular_sigma_rad=old) + expected=np.sqrt(.006**2+old**2-HPR_DIRECT_ANGULAR_SIGMA_RAD**2) + assert _hpr_sigma(problem,bridge)==pytest.approx(expected) + + +def test_node_graph_free_lever_layout_extends_fixed_state_by_three() -> None: + lever=np.array([.24,-.18,.36]) + rotation_at=lambda t: Rotation.from_euler('z',.12*t).as_matrix() + session=_rows_from_motion('node_graph_free',lever,np.eye(3),rotation_at,duration_s=5.) + problem=build_problem(_segments([session],.5)[0],np.eye(3),lever,.006) + value=np.concatenate([np.zeros(3),node_graph_initial_parameters(problem,np.zeros(3))]) + residual=_free_residual(problem,value) + sparsity=_free_sparsity(problem,value) + assert np.all(np.isfinite(residual)) + assert sparsity.shape==(residual.size,value.size) + + +def test_node_graph_block_schur_information_is_additive() -> None: + rng=np.random.default_rng(7); rows=24; nuisance=5 + local=[rng.normal(size=(rows,3+nuisance)) for _ in range(2)] + global_jac=np.zeros((2*rows,3+2*nuisance)) + global_jac[:rows,:3]=local[0][:,:3] + global_jac[:rows,3:3+nuisance]=local[0][:,3:] + global_jac[rows:,:3]=local[1][:,:3] + global_jac[rows:,3+nuisance:]=local[1][:,3:] + actual=_additive_marginal_lever_information( + global_jac,[0,rows,2*rows],[3,3+nuisance,3+2*nuisance]) + expected=np.zeros((3,3)) + for jac in local: + H=jac.T@jac + expected+=H[:3,:3]-H[:3,3:]@np.linalg.pinv(H[3:,3:])@H[3:,:3] + assert np.allclose(actual,expected,rtol=1e-10,atol=1e-10) + + +def test_node_graph_soft_lever_prior_adds_exact_information() -> None: + lever=np.array([.24,-.18,.36]) + rotation_at=lambda t: Rotation.from_euler('z',.12*t).as_matrix() + session=_rows_from_motion('node_graph_prior',lever,np.eye(3),rotation_at, + duration_s=5.) + problem=build_problem(_segments([session],.5)[0],np.eye(3),lever,.006) + free=solve_free_lever_many([problem],lever,max_nfev=1) + covariance=np.diag([.02**2,.02**2,.03**2]) + prior=solve_free_lever_many([problem],lever,max_nfev=1, + lever_prior_mean_m=lever,lever_prior_covariance_m2=covariance) + free_information=np.linalg.pinv(free.lever_covariance_m2,rcond=1e-9) + prior_information=np.linalg.pinv(prior.lever_covariance_m2,rcond=1e-9) + assert np.allclose(prior_information-free_information, + np.linalg.inv(covariance),rtol=1e-6,atol=1e-5) + + +def test_heldout_gate_does_not_accept_underdispersed_statistics() -> None: + vector={'count':1,'axis_rms':[0.,0.,0.],'axis_p95_abs':[0.,0.,0.], + 'vector_rms':.01,'vector_p95':.02} + summary={'optimizer_converged_fraction':1., + 'global_chi_square_per_dof':.127, + 'best_position_physical_m':vector, + 'doppler_physical_m_s':vector, + 'residual_by_factor':{ + 'hpr':{'p95_abs':1.3}, + 'imu_preintegration':{'p95_abs':.28}}} + result=heldout_gate(summary) + assert not result['passed'] + assert not result['checks']['global_chi_square_per_dof_in_0p25_4'] + + +def test_fixed_lever_retry_starts_from_previous_final_state() -> None: + lever=np.array([.24,-.18,.36]) + rotation_at=lambda t: Rotation.from_euler('z',.12*t).as_matrix() + session=_rows_from_motion('node_graph_retry',lever,np.eye(3),rotation_at, + duration_s=5.) + problem=build_problem(_segments([session],.5)[0],np.eye(3),lever,.006) + state,first=fit_states_at_fixed_lever(problem,lever,max_nfev=1) + _,retry=fit_states_at_fixed_lever( + problem,lever,max_nfev=1,initial_state_values=state) + assert retry['initial_cost']==pytest.approx(first['cost']) + + +def test_innovation_summary_reports_vector_and_temporal_metrics() -> None: + records=[{'residual':np.array([float(i),0.,0.]), + 'normalized':np.array([float(i),0.,0.]), + 'normalized_factor_only':np.array([float(i),0.,0.]), + 't_s':float(i)} for i in range(4)] + result=innovation_summary(records) + assert result['vector_p95']>result['vector_p50'] + assert result['temporal_linear_drift_per_s'][0]==pytest.approx(1.) + assert np.asarray(result['empirical_covariance']).shape==(3,3) + + +def test_propagation_root_report_detects_common_constant_acceleration() -> None: + acceleration=np.array([.2,-.02,-.01]); position=[]; velocity=[] + for index,dt in enumerate((.8,1.,1.2,1.4)): + base={'interval_id':str(index),'session':'s','motion':'m','t_s':index, + 'speed_bin':'slow','gyro_bin':'low','dt_s':dt,'R0_WI':np.eye(3)} + position.append({**base,'residual':.5*acceleration*dt*dt}) + velocity.append({**base,'residual':acceleration*dt}) + report,_=_root_report(position,velocity) + assert report['common_constant_acceleration_error_detected'] + assert np.allclose( + report['overall']['difference_velocity_minus_position_m_s2']['bias'],0.) diff --git a/tests/test_rtk_imu_multisource.py b/tests/test_rtk_imu_multisource.py new file mode 100644 index 0000000..13d681f --- /dev/null +++ b/tests/test_rtk_imu_multisource.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import numpy as np + +from imu_lidar.contracts import ImuSeries +from imu_lidar.rtk_imu_multisource import R1bResult, UnifiedSession, solve_r2g + + +def _level_session(session_id: str) -> UnifiedSession: + t = np.arange(0.0, 25.0, 0.01) + return UnifiedSession( + session_id=session_id, + batch_id="test", + imu=ImuSeries( + t_s=t, + gyro_rad_s=np.zeros((t.size, 3)), + acc_m_s2=np.tile([0.0, 0.0, 9.80665], (t.size, 1)), + ), + imu_rpy_deg=np.zeros((t.size, 3)), + imu_quaternion_wxyz=np.tile([1.0, 0.0, 0.0, 0.0], (t.size, 1)), + imu_host_receive_utc_s=t, + rtk_by_type={}, + ) + + +def test_r2g_baseline_plus_level_gravity_completes_identity() -> None: + r1b = R1bResult( + baseline_axis_imu=np.array([1.0, 0.0, 0.0]), + tilt_yz_deg=np.zeros(2), + pair_count=100, + residual_rms_deg=0.1, + residual_p95_deg=0.2, + covariance_deg2=np.eye(2) * 0.01, + std_deg=np.ones(2) * 0.1, + information_singular_values=np.ones(2), + per_session_rms_deg={}, + gyro_bias_by_session_rad_s={}, + ok=True, + notes=(), + ) + sessions = [_level_session("a"), _level_session("b")] + result = solve_r2g(sessions, r1b, level_static_session_ids={"a", "b"}) + assert result.R_RTK_IMU is not None + assert np.allclose(result.R_RTK_IMU, np.eye(3), atol=1e-12) + assert result.sample_count == 6 + assert result.session_count == 2 diff --git a/tools/audit_rtk_imu_factor_consistency.py b/tools/audit_rtk_imu_factor_consistency.py new file mode 100644 index 0000000..668c448 --- /dev/null +++ b/tools/audit_rtk_imu_factor_consistency.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +'''Audit RTK/IMU factor conventions without running an optimizer.''' +from __future__ import annotations +import argparse, json, math, sys +from dataclasses import asdict, is_dataclass +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) +from imu_lidar.imu_preintegration import preintegrate_imu +from imu_lidar.rtk_imu_engineering import ( + G_ENU, MIN_SEGMENT_DURATION_S, MIN_SEGMENT_NODE_COUNT, + NUISANCE_DOF_PER_SEGMENT, _Segment, _all_hpr, _audit, + _dense_colored_jacobian, _enu, _height_reference, + _hpr_factor_observation, _initial_parameters, _marginal_lever_information, + _motion_flags, _nodes, _position_valid, _residual, + _segment_residual_size, _world_rtk) +from imu_lidar.rtk_imu_multisource import _f, _truth, load_unified_sessions +MECHANICAL_L_I_M = np.array([-0.45072, -0.25682, 0.73208]) + +def _jsonable(value): + if isinstance(value, np.ndarray): return _jsonable(value.tolist()) + if isinstance(value, np.generic): return _jsonable(value.item()) + if isinstance(value, float): return value if math.isfinite(value) else None + if is_dataclass(value): return _jsonable(asdict(value)) + if isinstance(value, dict): return {str(k): _jsonable(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): return [_jsonable(v) for v in value] + return value + +def _summary(error): + a = np.asarray(error, dtype=float) + return _jsonable(_audit(list(a.reshape(-1, 3)), 3)) if a.size else _jsonable(_audit([], 3)) + +def _corr(a, b): + out = np.full(3, np.nan) + for axis in range(3): + if len(a) >= 3 and np.std(a[:, axis]) > 1e-10 and np.std(b[:, axis]) > 1e-10: + out[axis] = np.corrcoef(a[:, axis], b[:, axis])[0, 1] + return out + +def _best_arrays(session, reference): + rows = [] + for row in session.rtk_by_type.get('BESTNAVA', []): + v = np.array([_f(row, 'velocity_east_m_s'), _f(row, 'velocity_north_m_s'), + _f(row, 'vertical_speed_m_s')]) + if _position_valid(row, 'BESTNAVA') and _truth(row, 'doppler_velocity_valid') and np.all(np.isfinite(v)): + rows.append(row) + rows.sort(key=lambda row: _f(row, 't_device_s')) + rows = [row for i, row in enumerate(rows) if i == 0 or _f(row, 't_device_s') > _f(rows[i-1], 't_device_s')] + t = np.asarray([_f(row, 't_device_s') for row in rows]) + p = np.asarray([_enu(row, 'BESTNAVA', reference)[0] for row in rows]).reshape(-1, 3) + v = np.asarray([[_f(row, 'velocity_east_m_s'), _f(row, 'velocity_north_m_s'), + _f(row, 'vertical_speed_m_s')] for row in rows]).reshape(-1, 3) + return rows, t, p, v + +def _gnss_audit(sessions, reference, max_dt): + all_dpdt, all_v, per_session = [], [], {} + for session in sessions: + _, t, p, v = _best_arrays(session, reference) + dt = np.diff(t); keep = (dt > 0) & (dt <= max_dt) + dpdt = np.diff(p, axis=0)[keep] / dt[keep, None] + v_avg = .5 * (v[:-1] + v[1:])[keep] + all_dpdt.append(dpdt); all_v.append(v_avg) + per_session[session.session_id] = { + 'interval_count': len(dpdt), 'nominal_correlation_xyz': _corr(dpdt, v_avg), + 'nominal_error_m_s': _summary(dpdt-v_avg)} + dpdt, velocity = np.vstack(all_dpdt), np.vstack(all_v) + hypotheses = [] + for swap in (False, True): + base = velocity[:, [1,0,2]] if swap else velocity + for sx in (-1.,1.): + for sy in (-1.,1.): + for sz in (-1.,1.): + transformed = base * [sx,sy,sz] + hypotheses.append({ + 'mapping': ('[N,E,Z]' if swap else '[E,N,Z]')+f'*[{sx:+.0f},{sy:+.0f},{sz:+.0f}]', + 'correlation_xyz': _corr(dpdt, transformed), + 'error_m_s': _summary(dpdt-transformed)}) + hypotheses.sort(key=lambda item: item['error_m_s']['vector_rms']) + nominal = next(x for x in hypotheses if x['mapping'] == '[E,N,Z]*[+1,+1,+1]') + return {'interval_count': len(dpdt), 'nominal': nominal, 'best_mapping': hypotheses[0], + 'all_hypotheses': hypotheses, 'per_session': per_session} + +def _nearest_imu(session, t, tolerance=.03): + right = int(np.searchsorted(session.imu.t_s, t)) + candidates = [i for i in (right-1,right) if 0 <= i < len(session.imu.t_s)] + if not candidates: return None + index = min(candidates, key=lambda i: abs(session.imu.t_s[i]-t)) + return index if abs(session.imu.t_s[index]-t) <= tolerance else None + +def _static_audit(sessions, rotation): + current, opposite, per_session = [], [], {} + for session in sessions: + hpr, local = _all_hpr(session), [] + last_t = -np.inf + for hpr_index in hpr.valid_indices: + t = float(hpr.t_s[hpr_index]) + if t-last_t < 1.: continue + last_t = t + gravity, _ = _motion_flags(session,t,np.zeros(0),np.zeros((0,3))) + baseline, _, valid, _, _ = _hpr_factor_observation(hpr,t) + imu_index = _nearest_imu(session,t) + if not (gravity and valid and imu_index is not None): + continue + R_WI = _world_rtk(baseline) @ rotation + accel = session.imu.acc_m_s2[imu_index] + value = R_WI @ accel + G_ENU + local.append(value); current.append(value); opposite.append(R_WI @ accel-G_ENU) + per_session[session.session_id] = {'count':len(local),'current_formula_m_s2':_summary(local)} + return {'formula':'a_W_linear = R_WI @ specific_force_I + G_ENU', + 'current_formula_m_s2':_summary(current), + 'opposite_gravity_sign_m_s2':_summary(opposite),'per_session':per_session} + +def _closure_audit(sessions, reference, rotation, lever, max_dt): + all_p, all_v, per_session = [], [], {} + for session in sessions: + _, t, p_ant, v_ant = _best_arrays(session, reference) + hpr, local_p, local_v = _all_hpr(session), [], [] + for index, dt in enumerate(np.diff(t)): + if not .5 <= dt <= max_dt: continue + baseline, _, valid, _, _ = _hpr_factor_observation(hpr, t[index]) + i0, i1 = _nearest_imu(session, t[index]), _nearest_imu(session, t[index+1]) + if not valid or i0 is None or i1 is None: continue + pre = preintegrate_imu(session.imu.t_s, session.imu.gyro_rad_s, + session.imu.acc_m_s2, t[index], t[index+1]) + if pre.duration_s <= 0 or abs(pre.duration_s-dt) > 1e-6: continue + R0 = _world_rtk(baseline) @ rotation + p_i0 = p_ant[index] - R0 @ lever + v_i0 = v_ant[index] - R0 @ np.cross(session.imu.gyro_rad_s[i0], lever) + p_i1 = p_i0 + v_i0*dt + .5*G_ENU*dt**2 + R0@pre.delta_p + v_i1 = v_i0 + G_ENU*dt + R0@pre.delta_v + R1 = R0 @ pre.delta_R + local_p.append(p_i1 + R1@lever - p_ant[index+1]) + local_v.append(v_i1 + R1@np.cross(session.imu.gyro_rad_s[i1],lever) - v_ant[index+1]) + all_p.extend(local_p); all_v.extend(local_v) + per_session[session.session_id] = { + 'interval_count':len(local_p),'position_m':_summary(local_p), + 'velocity_m_s':_summary(local_v)} + return {'method':'single-step forward closure; fixed R2G/mechanical lever/BEST p-v; no least_squares', + 'position_m':_summary(all_p),'velocity_m_s':_summary(all_v), + 'per_session':per_session} + +def _qualified_runs(sessions, reference, period): + result = [] + for session in sessions: + nodes, start, number = _nodes(session, reference, period), 0, 0 + for end in range(1,len(nodes)+1): + if end != len(nodes) and nodes[end].continuity_id == nodes[end-1].continuity_id: + continue + run, start = tuple(nodes[start:end]), end + if (len(run) >= MIN_SEGMENT_NODE_COUNT + and run[-1].t_s-run[0].t_s >= MIN_SEGMENT_DURATION_S + and any(node.hpr_factor_valid for node in run)): + result.append((session,run,f'{session.session_id}:{number:02d}')) + number += 1 + return result + +def _first_node_audit(runs, rotation, lever): + records, ranges = [], {} + for session, run, segment_id in runs: + node = run[0] + hpr_node = next(item for item in run if item.hpr_factor_valid) + R_WI = _world_rtk(hpr_node.baseline_enu) @ rotation + velocity = node.velocity_enu_m_s + v = np.full(3,np.nan) if velocity is None else velocity + records.append({ + 'segment_id':segment_id,'source':node.source,'t_s':node.t_s, + 'first_position_global_enu_m':node.p_enu_m,'first_velocity_enu_m_s':v, + 'free_x0_l0_position_residual_m':np.zeros(3), + 'free_x0_l0_velocity_residual_m_s':-v, + 'mechanical_l_unshifted_x0_position_residual_m':R_WI@lever, + 'mechanical_l_unshifted_x0_velocity_residual_m_s':R_WI@np.cross(node.gyro_rad_s,lever)-v, + 'observation_seeded_mechanical_position_residual_m':np.zeros(3), + 'observation_seeded_mechanical_velocity_residual_m_s': + np.zeros(3) if velocity is not None else np.full(3,np.nan)}) + ranges.setdefault(session.session_id,[]).append(node.p_enu_m) + ranges = {key:{'count':len(value),'min_global_enu_m':np.min(value,axis=0), + 'max_global_enu_m':np.max(value,axis=0)} for key,value in ranges.items()} + return {'common_global_enu_reference':True,'segment_state_is_global_imu_position':True, + 'per_session_first_node_ranges':ranges,'segments':records} + +def _gyro_score(session, run, category): + keep = (session.imu.t_s >= run[0].t_s) & (session.imu.t_s <= run[-1].t_s) + t, gyro = session.imu.t_s[keep], session.imu.gyro_rad_s[keep] + if len(t) < 2: return 0. + value = np.trapezoid(np.abs(gyro),t,axis=0) + return float(value[2] if category != 'slope' else np.hypot(value[0],value[1])) + +def _build_segment(session, run, segment_id): + pre = tuple(preintegrate_imu(session.imu.t_s,session.imu.gyro_rad_s, + session.imu.acc_m_s2,a.t_s,b.t_s) + for a,b in zip(run[:-1],run[1:])) + hpr = next(node for node in run if node.hpr_factor_valid) + return _Segment(segment_id,session.session_id,run,pre,_world_rtk(hpr.baseline_enu)) + +def _subset_marginal(jacobian,residual,segments,indices): + rows, row0 = [], 0 + for index,segment in enumerate(segments): + count = _segment_residual_size(segment) + if index in indices: rows.extend(range(row0,row0+count)) + row0 += count + columns = [0,1,2] + for index in indices: + start = 3 + NUISANCE_DOF_PER_SEGMENT*index + columns.extend(range(start,start+NUISANCE_DOF_PER_SEGMENT)) + rows, columns = np.asarray(rows), np.asarray(columns) + return _marginal_lever_information( + jacobian[np.ix_(rows,columns)],residual[rows])[0] + +def _jacobian_schur_audit(runs,categories,rotation,lever): + segments, indices = [], {} + for category in ('circle','left_right','slope'): + choices = [item for item in runs if categories[item[0].session_id] == category] + if not choices: + indices[category] = []; continue + best = max(choices,key=lambda item:_gyro_score(item[0],item[1],category)) + indices[category] = [len(segments)] + segments.append(_build_segment(*best)) + x = _initial_parameters(segments); x[:3] = lever + residual = _residual(x,segments,rotation) + jacobian = _dense_colored_jacobian(x,segments,rotation) + comparisons = [] + for axis in range(3): + plus, minus = x.copy(), x.copy() + plus[axis] += .001; minus[axis] -= .001 + numeric = (_residual(plus,segments,rotation)-_residual(minus,segments,rotation))/.002 + current = jacobian[:,axis]; delta = numeric-current + comparisons.append({ + 'axis':'XYZ'[axis],'perturbation_m':.001, + 'relative_difference':float(np.linalg.norm(delta)/max(np.linalg.norm(numeric),1e-12)), + 'difference_norm':float(np.linalg.norm(delta)), + 'max_absolute_difference':float(np.max(np.abs(delta))), + 'correlation':float(np.corrcoef(numeric,current)[0,1])}) + full = _marginal_lever_information(jacobian,residual)[0] + parts = {key:_subset_marginal(jacobian,residual,segments,value) + for key,value in indices.items() if value} + summed = sum(parts.values(),np.zeros((3,3))) + error = np.linalg.norm(summed-full,ord='fro') + return {'linearization':'same observation-seeded nuisance state and mechanical lever; no optimizer', + 'selected_segments':[segment.segment_id for segment in segments], + 'finite_difference_vs_solver_jacobian':comparisons, + 'full_marginal_information':full,'category_marginal_information':parts, + 'category_sum':summed,'additivity_error_fro':float(error), + 'additivity_relative_error':float(error/max(np.linalg.norm(full,ord='fro'),1e-12))} + +def _legacy_diagnostics(path): + if path is None or not path.exists(): + return {'available':False,'reason':'no prior artifact supplied'} + payload = json.loads(path.read_text(encoding='utf-8')) + final_l = np.asarray(payload.get('free_solution',{}).get('l_I_m',[np.nan]*3)) + return {'available':False,'source':str(path),'initial_cost':None,'final_cost':None, + 'cost_reduction':None,'nfev':None,'optimality':None,'gradient_norm':None, + 'initial_l_I_m':[0.,0.,0.],'final_l_I_m':final_l, + 'l_step_norm_m':float(np.linalg.norm(final_l)) if np.all(np.isfinite(final_l)) else None, + 'reason':'legacy artifact did not persist optimizer state; prohibited solve was not rerun'} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--left-right-session',required=True) + parser.add_argument('--slope-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--max-best-interval-s',type=float,default=1.75) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--previous-free-result',type=Path) + parser.add_argument('--level-static-session',action='append', + default=['0819_20260819_072130','0819_20260819_073045']) + args = parser.parse_args() + categories = {args.circle_session:'circle',args.left_right_session:'left_right', + args.slope_session:'slope'} + selected_ids = set(categories) | set(args.level_static_session) + all_sessions = load_unified_sessions(args.manifest,selected_session_ids=selected_ids) + sessions = [session for session in all_sessions if session.session_id in categories] + static_sessions = [session for session in all_sessions + if session.session_id in set(args.level_static_session)] + reference = _height_reference(sessions) + if reference is None: raise RuntimeError('no valid BESTNAVA height reference') + rotation = Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + lever = np.asarray(args.mechanical_l_I_m) + runs = _qualified_runs(sessions,reference,args.sample_period_s) + payload = { + 'scope':'factor consistency only; no free solve/LOO/prior/bootstrap/sensitivity', + 'least_squares_called':False,'rotation_source':'R2G_gravity_level_prior', + 'rotation_rpy_deg':args.rotation_rpy_deg,'mechanical_l_I_m':lever, + 'common_enu_reference':reference, + 'gnss_position_difference_vs_doppler':_gnss_audit( + sessions,reference,args.max_best_interval_s), + 'static_specific_force_gravity_sign':_static_audit(static_sessions,rotation), + 'one_step_imu_preintegration_closure':_closure_audit( + sessions,reference,rotation,lever,args.max_best_interval_s), + 'first_node_origin_and_initial_residual':_first_node_audit(runs,rotation,lever), + 'lever_jacobian_and_category_schur':_jacobian_schur_audit( + runs,categories,rotation,lever), + 'previous_free_fit_solver_diagnostics':_legacy_diagnostics(args.previous_free_result)} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({ + 'gnss':payload['gnss_position_difference_vs_doppler'], + 'static':payload['static_specific_force_gravity_sign'], + 'closure':payload['one_step_imu_preintegration_closure'], + 'jacobian_schur':payload['lever_jacobian_and_category_schur']}), + ensure_ascii=False,indent=2)) + return 0 + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/audit_rtk_imu_factor_yield.py b/tools/audit_rtk_imu_factor_yield.py new file mode 100644 index 0000000..088c56a --- /dev/null +++ b/tools/audit_rtk_imu_factor_yield.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +"""Audit BESTNAVA/Doppler factor yield for every unified RTK--IMU session.""" + +from __future__ import annotations + +import argparse +import json +import sys +from dataclasses import replace +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_engineering import ( + _all_hpr, + _height_reference, + _nodes, + _position_valid, +) +from imu_lidar.rtk_imu_multisource import _f, _nearest_index, _truth, load_unified_sessions + + +def _finite_doppler(row: dict[str, str]) -> bool: + value = np.asarray([ + _f(row, "velocity_east_m_s"), + _f(row, "velocity_north_m_s"), + _f(row, "vertical_speed_m_s"), + ]) + return bool(_truth(row, "doppler_velocity_valid") and np.all(np.isfinite(value))) + + +def _source_only_nodes(session, source: str, period_s: float): + rows = {source: session.rtk_by_type.get(source, [])} + if "GNHPR" in session.rtk_by_type: + rows["GNHPR"] = session.rtk_by_type["GNHPR"] + return _nodes(replace(session, rtk_by_type=rows), _height_reference([session]), period_s) + + +def _audit_session(session, period_s: float) -> dict[str, object]: + best = session.rtk_by_type.get("BESTNAVA", []) + hpr = _all_hpr(session) + counts = { + "raw_bestnava": len(best), + "checksum_valid": 0, + "fixed": 0, + "finite_position": 0, + "finite_doppler": 0, + "hpr_near": 0, + "hpr_q4": 0, + "imu_near": 0, + "raw_bestnava_candidate": 0, + } + for row in best: + if not _truth(row, "checksum_valid"): + continue + counts["checksum_valid"] += 1 + if not _truth(row, "position_fixed"): + continue + counts["fixed"] += 1 + if not _position_valid(row, "BESTNAVA"): + continue + counts["finite_position"] += 1 + if not _finite_doppler(row): + continue + counts["finite_doppler"] += 1 + t_s = _f(row, "t_device_s") + hpr_index = _nearest_index(hpr.t_s, t_s, 0.12) + if hpr_index is None: + continue + counts["hpr_near"] += 1 + if not hpr.valid[hpr_index]: + continue + counts["hpr_q4"] += 1 + if _nearest_index(session.imu.t_s, t_s, 0.03) is None: + continue + counts["imu_near"] += 1 + counts["raw_bestnava_candidate"] += 1 + + reference = _height_reference([session]) + combined_nodes = _nodes(session, reference, period_s) if reference is not None else [] + best_nodes = _source_only_nodes(session, "BESTNAVA", period_s) if reference is not None else [] + combined_best = [node for node in combined_nodes if node.source == "BESTNAVA"] + best_velocity_nodes = [node for node in combined_best if node.velocity_enu_m_s is not None] + source_best_velocity = [node for node in best_nodes if node.velocity_enu_m_s is not None] + run_nodes: dict[int, list] = {} + for node in combined_nodes: + run_nodes.setdefault(node.continuity_id, []).append(node) + qualifying_runs = [ + run for run in run_nodes.values() + if len(run) >= 6 and run[-1].t_s - run[0].t_s >= 5.0 + ] + qualifying_best = [ + node for run in qualifying_runs for node in run if node.source == "BESTNAVA" + ] + qualifying_doppler = [node for node in qualifying_best if node.velocity_enu_m_s is not None] + return { + "session_id": session.session_id, + "batch_id": session.batch_id, + "counts": counts, + "combined_selected_nodes": len(combined_nodes), + "combined_selected_bestnava": len(combined_best), + "combined_selected_doppler": len(best_velocity_nodes), + "best_only_selected_nodes": len(best_nodes), + "best_only_selected_doppler": len(source_best_velocity), + "best_suppressed_by_mixed_selection": max(0, len(best_nodes) - len(combined_best)), + "doppler_suppressed_by_mixed_selection": max(0, len(source_best_velocity) - len(best_velocity_nodes)), + "continuous_run_count": len(run_nodes), + "qualified_run_count": len(qualifying_runs), + "qualified_bestnava_factors": len(qualifying_best), + "qualified_doppler_factors": len(qualifying_doppler), + } + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--sample-period-s", type=float, default=1.0) + args = parser.parse_args(argv) + sessions = load_unified_sessions(args.manifest) + audit = [_audit_session(session, args.sample_period_s) for session in sessions] + totals: dict[str, int] = {} + for item in audit: + for key, value in item["counts"].items(): + totals[key] = totals.get(key, 0) + int(value) + for key in ( + "combined_selected_nodes", "combined_selected_bestnava", "combined_selected_doppler", + "best_only_selected_nodes", "best_only_selected_doppler", + "best_suppressed_by_mixed_selection", "doppler_suppressed_by_mixed_selection", + "continuous_run_count", "qualified_run_count", "qualified_bestnava_factors", + "qualified_doppler_factors", + ): + totals[key] = totals.get(key, 0) + int(item[key]) + payload = { + "sample_period_s": args.sample_period_s, + "session_count": len(audit), + "totals": totals, + "sessions": audit, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(json.dumps({"session_count": len(audit), "totals": totals}, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/tools/audit_rtk_imu_heldout_innovation.py b/tools/audit_rtk_imu_heldout_innovation.py new file mode 100644 index 0000000..8e766b8 --- /dev/null +++ b/tools/audit_rtk_imu_heldout_innovation.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +'''Independent prediction innovations on the frozen 267 held-out windows.''' +from __future__ import annotations +import argparse,json,sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem +from tools.audit_rtk_imu_factor_consistency import _jsonable +from tools.audit_rtk_imu_innovation_noise import ( + _factor_report,_hpr_innovations,_interval_innovations) +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments + +LIMITS={ + 'best_position':{'bias_norm':.10,'vector_p95':.50}, + 'doppler':{'bias_norm':.10,'vector_p95':.50}, + 'hpr':{'bias_norm':.01,'vector_p95':.05}, +} + +def _finite_max_abs(values): + a=np.asarray(values,dtype=float) + return float(np.max(np.abs(a[np.isfinite(a)]))) if np.any(np.isfinite(a)) else np.inf + +def _summary_gate(summary,limits,min_samples=20): + if summary.get('sample_count',0)1e-12 and np.std(right)>1e-12: + result[axis] = np.corrcoef(left,right)[0,1] + return result + +def _summarize(records): + if not records: return {'sample_count':0} + residual = np.asarray([item['residual'] for item in records]) + normalized = np.asarray([item['normalized'] for item in records]) + factor_only = np.asarray([item['normalized_factor_only'] for item in records]) + nis = np.sum(normalized*normalized,axis=1) + dimension = residual.shape[1] + effective_dimension = int(records[0].get('effective_dimension',dimension)) + total_dof = len(records)*effective_dimension + norm = np.linalg.norm(residual,axis=1) + centered_t = np.asarray([item['t_s'] for item in records],dtype=float) + centered_t -= np.mean(centered_t) + temporal_drift = np.zeros(dimension) + if len(records)>=3 and np.ptp(centered_t)>1e-9: + temporal_drift = np.asarray([ + np.polyfit(centered_t,residual[:,axis],1)[0] + for axis in range(dimension)]) + return {'sample_count':len(records),'dimension':dimension, + 'effective_dof_per_sample':effective_dimension, + 'innovation_bias':np.mean(residual,axis=0), + 'innovation_distribution':_distribution(residual), + 'axis_rms':np.sqrt(np.mean(residual*residual,axis=0)), + 'axis_p50_abs':np.percentile(np.abs(residual),50.,axis=0), + 'axis_p95_abs':np.percentile(np.abs(residual),95.,axis=0), + 'axis_p99_abs':np.percentile(np.abs(residual),99.,axis=0), + 'vector_rms':float(np.sqrt(np.mean(norm*norm))), + 'vector_p50':float(np.percentile(norm,50.)), + 'vector_p95':float(np.percentile(norm,95.)), + 'vector_p99':float(np.percentile(norm,99.)), + 'normalized_distribution':_distribution(normalized), + 'empirical_covariance':np.cov(residual,rowvar=False), + 'nis_distribution':_distribution(nis), + 'nis_total':float(np.sum(nis)),'nis_per_dof':float(np.sum(nis)/total_dof), + 'alpha_factor':float(np.sum(nis)/total_dof), + 'alpha_composite_prediction':float(np.sum(nis)/total_dof), + 'alpha_factor_only_upper_bound':float(np.sum(factor_only*factor_only)/total_dof), + 'temporal_autocorrelation_lag1':_autocorrelation(residual), + 'temporal_linear_drift_per_s':temporal_drift} + +def _group(records,key): + values = {} + for item in records: values.setdefault(str(item[key]),[]).append(item) + return {name:_summarize(items) for name,items in values.items()} + +def _base_record(session_id,motion,t,speed,gyro_norm,gap): + return {'session':session_id,'motion':motion,'t_s':float(t), + 'speed_bin':_bin_speed(speed),'gyro_bin':_bin_gyro(gyro_norm), + 'hpr_gap_bin':_bin_gap(gap)} + +def _interval_innovations(problem,motion,bg=None,ba=None): + records = {'best_position':[],'doppler':[],'imu_preintegration':[]} + lever = problem.fixed_l_I_m + bg=np.zeros(3) if bg is None else np.asarray(bg,dtype=float) + ba=np.zeros(3) if ba is None else np.asarray(ba,dtype=float) + nodes = problem.segment.nodes + for index,(left,right,pre) in enumerate(zip(nodes[:-1],nodes[1:],problem.segment.preintegrations)): + if not (left.hpr_factor_valid and right.hpr_factor_valid): continue + if left.velocity_enu_m_s is None or right.velocity_enu_m_s is None: continue + R0 = _world_rtk(left.baseline_enu)@problem.R_RTK_IMU + R1 = _world_rtk(right.baseline_enu)@problem.R_RTK_IMU + p_i0 = left.p_enu_m-R0@lever + p_i1 = right.p_enu_m-R1@lever + v_i0 = left.velocity_enu_m_s-R0@np.cross(left.gyro_rad_s-bg,lever) + v_i1 = right.velocity_enu_m_s-R1@np.cross(right.gyro_rad_s-bg,lever) + dt = pre.duration_s + delta_R,delta_v,delta_p=apply_bias_correction_imu(pre,bg,ba) + pred_p_i1 = p_i0+v_i0*dt+.5*G_ENU*dt*dt+R0@delta_p + pred_v_i1 = v_i0+G_ENU*dt+R0@delta_v + pred_R1 = R0@delta_R + p_innovation = pred_p_i1+pred_R1@lever-right.p_enu_m + v_innovation = pred_v_i1+pred_R1@np.cross( + right.gyro_rad_s-bg,lever)-right.velocity_enu_m_s + speed = float(np.linalg.norm(left.velocity_enu_m_s)) + gyro_norm = float(pre.mean_gyro_norm) + gap = max(left.hpr_support_gap_s,right.hpr_support_gap_s) + base = _base_record(problem.segment.session_id,motion,right.t_s,speed,gyro_norm,gap) + base.update({'interval_id':f'{problem.segment.segment_id}:{index}', + 'dt_s':float(dt),'R0_WI':R0}) + p_cov = (np.diag(BEST_SIGMA**2)+dt*dt*np.diag(DOPPLER_SIGMA**2) + +R0@pre.cov[6:9,6:9]@R0.T+np.diag(BEST_SIGMA**2)) + v_cov = (np.diag(DOPPLER_SIGMA**2)+R0@pre.cov[3:6,3:6]@R0.T + +np.diag(DOPPLER_SIGMA**2)) + records['best_position'].append({**base,'residual':p_innovation, + 'normalized':_whiten(p_innovation,p_cov), + 'normalized_factor_only':p_innovation/BEST_SIGMA}) + records['doppler'].append({**base,'residual':v_innovation, + 'normalized':_whiten(v_innovation,v_cov), + 'normalized_factor_only':v_innovation/DOPPLER_SIGMA}) + imu_error = np.concatenate([ + so3_log(delta_R.T@R0.T@R1), + R0.T@(v_i1-v_i0-G_ENU*dt)-delta_v, + R0.T@(p_i1-p_i0-v_i0*dt-.5*G_ENU*dt*dt)-delta_p]) + anchored_cov = pre.cov.copy() + hpr_var = left.hpr_angular_sigma_rad**2+right.hpr_angular_sigma_rad**2 + anchored_cov[:3,:3] += np.eye(3)*hpr_var + anchored_cov[3:6,3:6] += R0.T@np.diag(2.*DOPPLER_SIGMA**2)@R0 + anchored_cov[6:9,6:9] += R0.T@np.diag( + 2.*BEST_SIGMA**2+dt*dt*DOPPLER_SIGMA**2)@R0 + records['imu_preintegration'].append({**base,'residual':imu_error, + 'normalized':_whiten(imu_error,anchored_cov), + 'normalized_factor_only':_whiten(imu_error,pre.cov)}) + return records + +def _hpr_innovations(session,problem,motion): + hpr = _all_hpr(session) + indices = [int(i) for i in hpr.valid_indices + if problem.segment.nodes[0].t_s <= hpr.t_s[i] <= problem.segment.nodes[-1].t_s] + records = [] + baseline_I = problem.R_RTK_IMU.T[:,0] + node_times = np.asarray([node.t_s for node in problem.segment.nodes]) + for position in range(1,len(indices)-1): + left,index,right = indices[position-1],indices[position],indices[position+1] + t0,t,t1 = hpr.t_s[left],hpr.t_s[index],hpr.t_s[right] + total_gap = float(t1-t0) + if not 0. < total_gap <= .5: continue + fraction = float((t-t0)/total_gap) + predicted = (1.-fraction)*hpr.baseline_enu[left]+fraction*hpr.baseline_enu[right] + predicted /= np.linalg.norm(predicted) + innovation = np.cross(predicted,hpr.baseline_enu[index]) + sigma = HPR_DIRECT_ANGULAR_SIGMA_RAD*np.sqrt( + 1.+(1.-fraction)**2+fraction**2) + nearest = int(np.argmin(np.abs(node_times-t))) + node = problem.segment.nodes[nearest] + speed = float(np.linalg.norm(node.velocity_enu_m_s)) if node.velocity_enu_m_s is not None else 0. + gyro_norm = float(np.linalg.norm(node.gyro_rad_s)) + base = _base_record(session.session_id,motion,t,speed,gyro_norm,total_gap) + records.append({**base,'method':'leave_one_out_interpolation', + 'effective_dimension':2,'residual':innovation, + 'normalized':innovation/sigma, + 'normalized_factor_only':innovation/HPR_DIRECT_ANGULAR_SIGMA_RAD}) + gyro_pre = preintegrate_gyro(session.imu.t_s,session.imu.gyro_rad_s,float(t0),float(t)) + R0 = _world_rtk(hpr.baseline_enu[left])@problem.R_RTK_IMU + propagated = R0@gyro_pre.delta_R@baseline_I + gyro_innovation = np.cross(propagated,hpr.baseline_enu[index]) + gyro_sigma = np.sqrt(2.*HPR_DIRECT_ANGULAR_SIGMA_RAD**2 + +float(np.trace(gyro_pre.cov))/3.) + records.append({**base,'method':'gyro_propagation', + 'effective_dimension':2,'residual':gyro_innovation, + 'normalized':gyro_innovation/gyro_sigma, + 'normalized_factor_only':gyro_innovation/HPR_DIRECT_ANGULAR_SIGMA_RAD}) + return records + +def _factor_report(records): + return {'overall':_summarize(records), + 'by_session':_group(records,'session'),'by_motion':_group(records,'motion'), + 'by_speed':_group(records,'speed_bin'),'by_gyro_norm':_group(records,'gyro_bin'), + 'by_hpr_bridge_gap':_group(records,'hpr_gap_bin'), + 'by_method':_group(records,'method') if records and 'method' in records[0] else {}} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--left-right-session',required=True) + parser.add_argument('--slope-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--target-duration-s',type=float,default=15.) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + args = parser.parse_args() + categories = {'circle':args.circle_session,'left_right':args.left_right_session, + 'slope':args.slope_session} + sessions = load_unified_sessions(args.manifest,selected_session_ids=set(categories.values())) + reference = _height_reference(sessions) + if reference is None: raise RuntimeError('no BEST reference') + rotation = Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + lever = np.asarray(args.mechanical_l_I_m) + selections, all_records = {}, { + 'best_position':[],'doppler':[],'hpr':[],'imu_preintegration':[]} + for motion,session_id in categories.items(): + session = next(item for item in sessions if item.session_id == session_id) + segment,selection = _select_window( + [session],reference,args.sample_period_s,args.target_duration_s) + selections[motion] = selection + problem = build_problem(segment,rotation,lever) + interval = _interval_innovations(problem,motion) + for key,records in interval.items(): all_records[key].extend(records) + all_records['hpr'].extend(_hpr_innovations(session,problem,motion)) + factors = {key:_factor_report(records) for key,records in all_records.items()} + payload = { + 'scope':'independent prediction innovations; no node optimization/covariance writeback', + 'least_squares_called':False,'covariance_parameters_modified':False, + 'fixed_l_I_m':lever,'selections':selections, + 'current_physical_sigma':{ + 'best_position_xyz_m':BEST_SIGMA, + 'doppler_xyz_m_s':DOPPLER_SIGMA, + 'hpr_direct_angular_rad':HPR_DIRECT_ANGULAR_SIGMA_RAD, + 'bias_random_walk_source':'unchanged device/static/Allan noise model'}, + 'alpha_semantics':{ + 'alpha_factor':'innovation NIS/dof using composite prediction covariance', + 'alpha_factor_only_upper_bound':( + 'innovation divided only by current factor covariance; includes predictor and ' + 'endpoint-anchor noise and must not be written back directly')}, + 'factors':factors, + 'recommended_covariance_writeback':False, + 'covariance_freeze_assessment':{ + 'best_position':'predictor-confounded; unchanged', + 'doppler':'predictor-confounded; unchanged', + 'imu_preintegration':'endpoint-anchor dominated; unchanged', + 'bias_random_walk':'static/Allan/device-model based; unchanged', + 'hpr':{ + 'identifiable':True, + 'evidence':'264 withheld samples; LOO and gyro predictions agree', + 'frozen_direct_sigma_rad':.006, + 'writeback_policy':'explicit audit decision, not automatic alpha'}}} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + compact = {key:{'count':value['overall'].get('sample_count',0), + 'bias':value['overall'].get('innovation_bias'), + 'rms':value['overall'].get('innovation_distribution',{}).get('rms'), + 'p95':value['overall'].get('innovation_distribution',{}).get('p95_abs'), + 'alpha_composite':value['overall'].get('alpha_composite_prediction'), + 'alpha_factor_only_upper_bound':value['overall'].get('alpha_factor_only_upper_bound'), + 'autocorrelation':value['overall'].get('temporal_autocorrelation_lag1')} + for key,value in factors.items()} + print(json.dumps(_jsonable(compact),ensure_ascii=False,indent=2)) + return 0 + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/audit_rtk_imu_lever_excitation.py b/tools/audit_rtk_imu_lever_excitation.py new file mode 100644 index 0000000..ce72e14 --- /dev/null +++ b/tools/audit_rtk_imu_lever_excitation.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python3 +"""Audit strict RTK--IMU segments for lever-arm excitation and marginal information.""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +from dataclasses import asdict +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_engineering import _fit_segments, _fit_summary, _segments +from imu_lidar.rtk_imu_multisource import load_unified_sessions + + +def _jsonable(value): + if isinstance(value, np.ndarray): + return _jsonable(value.tolist()) + if isinstance(value, np.generic): + return _jsonable(value.item()) + if isinstance(value, float): + return value if math.isfinite(value) else None + if hasattr(value, "__dataclass_fields__"): + return {key: _jsonable(item) for key, item in asdict(value).items()} + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (tuple, list)): + return [_jsonable(item) for item in value] + return value + + +def _orientation_spans_deg(segment, rotation_rtk_imu: np.ndarray) -> np.ndarray: + matrices = [segment.R_WRTK_initial @ rotation_rtk_imu] + for pre in segment.preintegrations: + matrices.append(matrices[-1] @ pre.delta_R) + euler = Rotation.from_matrix(np.asarray(matrices)).as_euler("xyz", degrees=False) + return np.degrees(np.ptp(np.unwrap(euler, axis=0), axis=0)) + + +def _audit_segment(segment, rotation_rtk_imu: np.ndarray, max_nfev: int) -> dict[str, object]: + gyro = np.asarray([node.gyro_rad_s for node in segment.nodes]) + gyro_norm = np.linalg.norm(gyro, axis=1) + best_count = sum(node.source == "BESTNAVA" for node in segment.nodes) + doppler_count = sum( + node.source == "BESTNAVA" and node.velocity_enu_m_s is not None + for node in segment.nodes + ) + fit, residual, detail = _fit_segments([segment], rotation_rtk_imu, max_nfev=max_nfev) + summary = _fit_summary(fit, residual, detail) + item: dict[str, object] = { + "segment_id": segment.segment_id, + "session_id": segment.session_id, + "node_count": len(segment.nodes), + "duration_s": float(segment.nodes[-1].t_s - segment.nodes[0].t_s), + "yaw_pitch_roll_span_deg": _orientation_spans_deg(segment, rotation_rtk_imu)[[2, 1, 0]], + "gyro_rms_deg_s": np.degrees(np.sqrt(np.mean(gyro ** 2, axis=0))), + "gyro_peak_deg_s": np.degrees(np.max(np.abs(gyro), axis=0)), + "gyro_norm_rms_deg_s": float(np.degrees(np.sqrt(np.mean(gyro_norm ** 2)))), + "gyro_norm_peak_deg_s": float(np.degrees(np.max(gyro_norm))), + "bestnava_count": best_count, + "doppler_count": doppler_count, + "fit": None, + } + if summary is not None: + item["fit"] = { + "optimizer_converged": summary.optimizer_converged, + "lever_information_singular_values": summary.lever_information_singular_values, + "lever_information_condition_number": summary.lever_information_condition_number, + "lever_precision_rank": summary.lever_precision_rank, + "weakest_lever_direction_I": summary.weakest_lever_direction_I, + "lever_std_m": summary.l_I_std_m, + } + singular = summary.lever_information_singular_values + item["information_score"] = float(singular[-1]) if summary.lever_precision_rank == 3 else 0.0 + else: + item["information_score"] = 0.0 + spans = np.asarray(item["yaw_pitch_roll_span_deg"]) + # Short strict runs rarely accumulate a full vehicle turn; retain clearly non-straight motion. + item["turn_or_slope"] = bool(spans[0] >= 3.0 or abs(spans[1]) >= 0.5 or abs(spans[2]) >= 0.5) + return item + + +def _recommended(items: list[dict[str, object]]) -> list[str]: + candidates = [ + item for item in items + if item["turn_or_slope"] and int(item["bestnava_count"]) >= 6 + and int(item["doppler_count"]) >= 6 + and item["fit"] is not None + and int(item["fit"]["lever_precision_rank"]) == 3 + ] + candidates.sort(key=lambda item: float(item["information_score"]), reverse=True) + selected: list[str] = [] + per_session: dict[str, int] = {} + for item in candidates: + session_id = str(item["session_id"]) + if per_session.get(session_id, 0) >= 2: + continue + selected.append(str(item["segment_id"])) + per_session[session_id] = per_session.get(session_id, 0) + 1 + return selected + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--sample-period-s", type=float, default=1.0) + parser.add_argument("--rotation-rpy-deg", nargs=3, type=float, + default=[0.4543066225, -0.0026392019, 0.0122384129]) + parser.add_argument("--max-nfev", type=int, default=80) + args = parser.parse_args(argv) + + sessions = load_unified_sessions(args.manifest) + rotation = Rotation.from_euler("xyz", args.rotation_rpy_deg, degrees=True).as_matrix() + items = [ + _audit_segment(segment, rotation, args.max_nfev) + for segment in _segments(sessions, args.sample_period_s) + ] + payload = { + "session_count": len(sessions), + "strict_segment_count": len(items), + "sample_period_s": args.sample_period_s, + "rotation_rpy_deg": args.rotation_rpy_deg, + "recommended_segment_ids": _recommended(items), + "segments": items, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload), ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(json.dumps({ + "strict_segment_count": len(items), + "recommended_segment_ids": payload["recommended_segment_ids"], + }, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/tools/audit_rtk_imu_motion_excitation.py b/tools/audit_rtk_imu_motion_excitation.py new file mode 100644 index 0000000..d97a044 --- /dev/null +++ b/tools/audit_rtk_imu_motion_excitation.py @@ -0,0 +1,668 @@ +#!/usr/bin/env python3 +"""Penetration audit for RTK--IMU motion excitation. + +This is read-only diagnostics. It never applies a lever prior, solves a lever arm, +or changes continuity/acceptance thresholds. Gyro trajectory integrals are the +primary excitation metrics; start/end Euler differences are deliberately absent. +""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +from dataclasses import asdict +from pathlib import Path +from typing import Iterable + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.imu_audit import audit_imu +from imu_lidar.rtk_imu_engineering import ( + MIN_SEGMENT_DURATION_S, + MIN_SEGMENT_NODE_COUNT, + _all_hpr, + _height_reference, + _hpr_factor_observation, + _node_interval_threshold_s, + _trajectory_continuity_reasons, + _nearest_index, + _nodes, + _position_valid, + _segments, + _source_nodes, +) +from imu_lidar.rtk_imu_multisource import _f, _truth, load_unified_sessions + + +RAW_GAP_S = 1.5 + + +def _jsonable(value): + if isinstance(value, np.ndarray): + return _jsonable(value.tolist()) + if isinstance(value, np.generic): + return _jsonable(value.item()) + if isinstance(value, float): + return value if math.isfinite(value) else None + if hasattr(value, "__dataclass_fields__"): + return {key: _jsonable(item) for key, item in asdict(value).items()} + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (tuple, list)): + return [_jsonable(item) for item in value] + return value + + +def _trapz(values: np.ndarray, t_s: np.ndarray) -> np.ndarray: + if t_s.size < 2: + return np.zeros(values.shape[1], dtype=float) + return np.trapezoid(values, t_s, axis=0) + + +def _interval_imu(session, start_s: float, end_s: float) -> tuple[np.ndarray, np.ndarray]: + mask = (session.imu.t_s >= start_s) & (session.imu.t_s <= end_s) + return session.imu.t_s[mask], session.imu.gyro_rad_s[mask] + + +def _gyro_metrics(session, start_s: float, end_s: float, gyro_offset_rad_s: np.ndarray | None = None) -> dict[str, object]: + t_s, gyro = _interval_imu(session, start_s, end_s) + if gyro_offset_rad_s is not None: + gyro = gyro - np.asarray(gyro_offset_rad_s, dtype=float).reshape(1, 3) + if t_s.size < 2: + nan = np.full(3, np.nan) + return { + "sample_count": int(t_s.size), "net_rotation_xyz_deg": nan, + "unwrap_rotation_range_xyz_deg": nan, + "cumulative_absolute_rotation_xyz_deg": nan, + "gyro_integral_squared_xyz_rad2_s": nan, + "gyro_rms_xyz_deg_s": nan, "gyro_peak_xyz_deg_s": nan, + } + dt = np.diff(t_s) + midpoint = 0.5 * (gyro[:-1] + gyro[1:]) + trajectory = np.vstack([np.zeros(3), np.cumsum(midpoint * dt[:, None], axis=0)]) + # The integrated trajectory is continuous. Explicit unwrap documents that + # the yaw range is never inferred from a wrapped heading/Euler endpoint. + trajectory[:, 2] = np.unwrap(trajectory[:, 2]) + duration = float(t_s[-1] - t_s[0]) + return { + "sample_count": int(t_s.size), + "net_rotation_xyz_deg": np.degrees(trajectory[-1]), + "unwrap_rotation_range_xyz_deg": np.degrees(np.ptp(trajectory, axis=0)), + "cumulative_absolute_rotation_xyz_deg": np.degrees(_trapz(np.abs(gyro), t_s)), + "gyro_integral_squared_xyz_rad2_s": _trapz(gyro * gyro, t_s), + "gyro_rms_xyz_deg_s": np.degrees(np.sqrt(_trapz(gyro * gyro, t_s) / duration)), + "gyro_peak_xyz_deg_s": np.degrees(np.max(np.abs(gyro), axis=0)), + } + + +def _interval_summary(session, start_s: float, end_s: float, *, label: str, + best_rows: Iterable[dict[str, str]], hpr) -> dict[str, object]: + best = list(best_rows) + in_range = [row for row in best if start_s <= _f(row, "t_device_s") <= end_s] + doppler = [ + row for row in in_range + if _truth(row, "doppler_velocity_valid") and np.all(np.isfinite([ + _f(row, "velocity_east_m_s"), _f(row, "velocity_north_m_s"), + _f(row, "vertical_speed_m_s"), + ])) + ] + q4 = hpr.valid & (hpr.t_s >= start_s) & (hpr.t_s <= end_s) + return { + "label": label, + "start_s": float(start_s), "end_s": float(end_s), + "duration_s": float(max(0.0, end_s - start_s)), + "bestnava_count": len(in_range), "doppler_count": len(doppler), + "q4_hpr_count": int(np.count_nonzero(q4)), + "gyro": _gyro_metrics(session, start_s, end_s), + } + + +def _coalesce(records: list[dict[str, object]], *, include: bool, label: str, + session, best_rows, hpr) -> list[dict[str, object]]: + result: list[dict[str, object]] = [] + current: list[dict[str, object]] = [] + key: tuple[str, ...] | None = None + for record in records: + active = bool(record["accepted"]) == include + reasons = tuple(record["reasons"]) + same = ( + current and active and key == reasons + and float(record["t_s"]) - float(current[-1]["t_s"]) <= RAW_GAP_S + ) + if active and (not current or same): + current.append(record) + key = reasons + continue + if current: + summary = _interval_summary( + session, float(current[0]["t_s"]), float(current[-1]["t_s"]), + label=label, best_rows=best_rows, hpr=hpr, + ) + if not include: + summary["cut_reason"] = list(key or ()) + result.append(summary) + current = [record] if active else [] + key = reasons if active else None + if current: + summary = _interval_summary( + session, float(current[0]["t_s"]), float(current[-1]["t_s"]), + label=label, best_rows=best_rows, hpr=hpr, + ) + if not include: + summary["cut_reason"] = list(key or ()) + result.append(summary) + return result + + +def _raw_records(session) -> list[dict[str, object]]: + """Raw-valid is position/IMU validity; HPR support remains a separate factor audit.""" + hpr = _all_hpr(session) + rows = session.rtk_by_type.get("BESTNAVA", []) + ordered = sorted(rows, key=lambda row: _f(row, "t_device_s")) + records: list[dict[str, object]] = [] + last_t = -np.inf + for row in ordered: + t_s = _f(row, "t_device_s") + reasons: list[str] = [] + if not np.isfinite(t_s): + reasons.append("position_device_time_invalid") + elif t_s <= last_t: + reasons.append("position_device_time_nonmonotonic") + if np.isfinite(t_s): + last_t = max(last_t, t_s) + if not _truth(row, "checksum_valid"): + reasons.append("position_checksum_invalid") + if not _truth(row, "position_fixed"): + reasons.append("position_not_fixed") + if not _position_valid(row, "BESTNAVA"): + reasons.append("position_required_field_invalid") + imu_index = _nearest_index(session.imu.t_s, t_s, 0.03) if np.isfinite(t_s) else None + if imu_index is None: + reasons.append("imu_missing_near") + _, _, hpr_factor_valid, hpr_method, hpr_gap = _hpr_factor_observation(hpr, t_s) + doppler_ok = bool( + _truth(row, "doppler_velocity_valid") and np.all(np.isfinite([ + _f(row, "velocity_east_m_s"), _f(row, "velocity_north_m_s"), + _f(row, "vertical_speed_m_s"), + ])) + ) + records.append({ + "t_s": t_s, "row": row, "accepted": not reasons, + "reasons": sorted(set(reasons)), "doppler_valid": doppler_ok, + "hpr_factor_valid": hpr_factor_valid, "hpr_factor_method": hpr_method, + "hpr_support_gap_s": hpr_gap, + }) + return records +def _r0_runs_and_cuts(session, nodes, hpr, best_rows, period_s: float) -> tuple[list[dict[str, object]], list[dict[str, object]], list[dict[str, object]]]: + runs: list[list] = [] + cuts: list[dict[str, object]] = [] + intervals: list[dict[str, object]] = [] + if not nodes: + return [], [], [] + current = [nodes[0]] + threshold = _node_interval_threshold_s(period_s) + for previous, node in zip(nodes[:-1], nodes[1:]): + dt = float(node.t_s - previous.t_s) + structural_reasons = list(_trajectory_continuity_reasons(session, previous.t_s, node.t_s, period_s)) + continuity_break = node.continuity_id != previous.continuity_id + reasons = structural_reasons or (["position_source_quality_or_merge_break"] if continuity_break else []) + intervals.append({ + "left_t_s": float(previous.t_s), "right_t_s": float(node.t_s), + "dt_s": dt, "threshold_s": threshold, + "trajectory_continuous": not structural_reasons, + "continuity_id_changed": continuity_break, + "cut_reason": reasons, + "left_hpr_factor": {"valid": previous.hpr_factor_valid, "method": previous.hpr_factor_method, + "support_gap_s": previous.hpr_support_gap_s}, + "right_hpr_factor": {"valid": node.hpr_factor_valid, "method": node.hpr_factor_method, + "support_gap_s": node.hpr_support_gap_s}, + }) + if not continuity_break: + current.append(node) + continue + runs.append(current) + cuts.append({ + **_interval_summary(session, previous.t_s, node.t_s, label="r0_cut", best_rows=best_rows, hpr=hpr), + "dt_s": dt, "threshold_s": threshold, "cut_reason": reasons, + }) + current = [node] + runs.append(current) + summaries = [ + _interval_summary(session, run[0].t_s, run[-1].t_s, label="R0_after_cuts", best_rows=best_rows, hpr=hpr) + | { + "node_count": len(run), "continuity_id": int(run[0].continuity_id), + "bestnava_count": sum(node.source == "BESTNAVA" for node in run), + "doppler_count": sum(node.velocity_enu_m_s is not None for node in run), + "hpr_factor_count": sum(node.hpr_factor_valid for node in run), + "hpr_factor_rejected_count": sum(not node.hpr_factor_valid for node in run), + } + for run in runs + ] + return summaries, cuts, intervals +def _qualified_summary(session, segments, hpr, best_rows) -> list[dict[str, object]]: + return [ + _interval_summary(session, segment.nodes[0].t_s, segment.nodes[-1].t_s, + label="qualified_segment", best_rows=best_rows, hpr=hpr) + | { + "segment_id": segment.segment_id, "node_count": len(segment.nodes), + "bestnava_count": sum(node.source == "BESTNAVA" for node in segment.nodes), + "doppler_count": sum(node.velocity_enu_m_s is not None for node in segment.nodes), + "hpr_factor_count": sum(node.hpr_factor_valid for node in segment.nodes), + "hpr_factor_rejected_count": sum(not node.hpr_factor_valid for node in segment.nodes), + } + for segment in segments + ] + +def _dropped_r0_runs(session, r0_nodes, qualified, hpr, best_rows) -> list[dict[str, object]]: + qualified_ranges = [(s.nodes[0].t_s, s.nodes[-1].t_s) for s in qualified] + result: list[dict[str, object]] = [] + by_id: dict[int, list] = {} + for node in r0_nodes: + by_id.setdefault(node.continuity_id, []).append(node) + for run in by_id.values(): + start_s, end_s = run[0].t_s, run[-1].t_s + retained = any(abs(start_s - left) < 1e-6 and abs(end_s - right) < 1e-6 for left, right in qualified_ranges) + if retained: + continue + reasons = [] + if len(run) < MIN_SEGMENT_NODE_COUNT: + reasons.append("qualified_min_node_count") + if end_s - start_s < MIN_SEGMENT_DURATION_S: + reasons.append("qualified_min_duration") + if not reasons: + reasons.append("preintegration_or_segment_validation") + result.append({ + **_interval_summary(session, start_s, end_s, label="dropped_before_qualified", + best_rows=best_rows, hpr=hpr), + "node_count": len(run), "cut_reason": reasons, + }) + return result + + +def _interval_overlap(left: dict[str, object], right: dict[str, object]) -> float: + return max(0.0, min(float(left["end_s"]), float(right["end_s"])) - max(float(left["start_s"]), float(right["start_s"]))) + + +def _interval_penetration(raw_intervals, r0_intervals, qualified_intervals, cut_intervals): + """Link every raw-valid dynamic interval to its downstream R0/qualified survivors.""" + result = [] + for raw in raw_intervals: + r0 = [item for item in r0_intervals if _interval_overlap(raw, item) > 0.0 or ( + item["start_s"] == item["end_s"] and raw["start_s"] <= item["start_s"] <= raw["end_s"] + )] + qualified = [item for item in qualified_intervals if _interval_overlap(raw, item) > 0.0] + cuts = [item for item in cut_intervals if _interval_overlap(raw, item) > 0.0] + raw_best = max(int(raw["bestnava_count"]), 1) + raw_doppler = max(int(raw["doppler_count"]), 1) + r0_duration = sum(_interval_overlap(raw, item) for item in r0) + qualified_duration = sum(_interval_overlap(raw, item) for item in qualified) + cut_reasons = sorted({reason for item in cuts for reason in item.get("cut_reason", [])}) + result.append({ + "raw_start_s": raw["start_s"], "raw_end_s": raw["end_s"], + "raw_duration_s": raw["duration_s"], "raw_bestnava_count": raw["bestnava_count"], + "raw_doppler_count": raw["doppler_count"], "raw_gyro": raw["gyro"], + "R0_overlap_duration_s": r0_duration, + "qualified_overlap_duration_s": qualified_duration, + "R0_bestnava_count": sum(int(item["bestnava_count"]) for item in r0), + "R0_doppler_count": sum(int(item["doppler_count"]) for item in r0), + "qualified_bestnava_count": sum(int(item["bestnava_count"]) for item in qualified), + "qualified_doppler_count": sum(int(item["doppler_count"]) for item in qualified), + "retention": { + "raw_to_R0_bestnava": sum(int(item["bestnava_count"]) for item in r0) / raw_best, + "raw_to_R0_doppler": sum(int(item["doppler_count"]) for item in r0) / raw_doppler, + "raw_to_R0_duration": r0_duration / max(float(raw["duration_s"]), 1e-9), + "raw_to_qualified_bestnava": sum(int(item["bestnava_count"]) for item in qualified) / raw_best, + "raw_to_qualified_doppler": sum(int(item["doppler_count"]) for item in qualified) / raw_doppler, + "raw_to_qualified_duration": qualified_duration / max(float(raw["duration_s"]), 1e-9), + }, + "cut_reason": cut_reasons, + }) + return result + + +def _union_time_intervals(intervals: list[dict[str, object]]) -> list[tuple[float, float]]: + ordered = sorted( + (float(item["start_s"]), float(item["end_s"])) for item in intervals + if np.isfinite(float(item["start_s"])) and np.isfinite(float(item["end_s"])) + ) + merged: list[list[float]] = [] + for start_s, end_s in ordered: + if end_s < start_s: + continue + if not merged or start_s > merged[-1][1]: + merged.append([start_s, end_s]) + else: + merged[-1][1] = max(merged[-1][1], end_s) + return [(start_s, end_s) for start_s, end_s in merged] + + +def _stage_statistics(session, intervals: list[dict[str, object]]) -> dict[str, object]: + """Audit each stage on unique IMU samples over the union of its time ranges.""" + total = _stage_total(intervals) + union = _union_time_intervals(intervals) + selected_count = 0 + unique_mask = np.zeros(session.imu.t_s.size, dtype=bool) + for item in intervals: + mask = (session.imu.t_s >= float(item["start_s"])) & (session.imu.t_s <= float(item["end_s"])) + selected_count += int(np.count_nonzero(mask)) + unique_mask |= mask + unique_count = int(np.count_nonzero(unique_mask)) + input_duration = float(sum(max(0.0, float(item["end_s"]) - float(item["start_s"])) for item in intervals)) + union_duration = float(sum(end_s - start_s for start_s, end_s in union)) + net = np.zeros(3) + unwrap_range_sum = np.zeros(3) + cumulative_abs = np.zeros(3) + energy = np.zeros(3) + peak = np.zeros(3) + metric_duration = 0.0 + for start_s, end_s in union: + gyro = _gyro_metrics(session, start_s, end_s) + current_net = np.asarray(gyro["net_rotation_xyz_deg"], dtype=float) + if not np.all(np.isfinite(current_net)): + continue + net += current_net + unwrap_range_sum += np.asarray(gyro["unwrap_rotation_range_xyz_deg"], dtype=float) + cumulative_abs += np.asarray(gyro["cumulative_absolute_rotation_xyz_deg"], dtype=float) + energy += np.asarray(gyro["gyro_integral_squared_xyz_rad2_s"], dtype=float) + peak = np.maximum(peak, np.asarray(gyro["gyro_peak_xyz_deg_s"], dtype=float)) + metric_duration += max(0.0, end_s - start_s) + total["unique_imu_coverage"] = { + "input_interval_count": len(intervals), + "union_interval_count": len(union), + "input_duration_s": input_duration, + "union_duration_s": union_duration, + "overlap_duration_s": max(0.0, input_duration - union_duration), + "selected_imu_sample_count_before_dedup": selected_count, + "unique_imu_sample_count": unique_count, + "duplicate_imu_sample_count": selected_count - unique_count, + } + total["gyro"] = { + "net_rotation_xyz_deg": net, + "sum_interval_unwrap_rotation_range_xyz_deg": unwrap_range_sum, + "cumulative_absolute_rotation_xyz_deg": cumulative_abs, + "gyro_integral_squared_xyz_rad2_s": energy, + "gyro_rms_xyz_deg_s": np.degrees(np.sqrt(energy / max(metric_duration, 1e-9))), + "gyro_peak_xyz_deg_s": peak, + } + return total + +def _stage_total(intervals: list[dict[str, object]]) -> dict[str, object]: + total = {"interval_count": len(intervals), "duration_s": 0.0, "bestnava_count": 0, + "doppler_count": 0, "q4_hpr_count": 0} + for item in intervals: + for key in ("duration_s", "bestnava_count", "doppler_count", "q4_hpr_count"): + total[key] += item[key] + return total + + +def _hpr_chain_diagnostics(hpr) -> dict[str, object]: + if hpr.t_s.size < 2: + return {"sample_count": int(hpr.t_s.size), "pair_count": 0} + dt = np.diff(hpr.t_s) + finite_vector = np.all(np.isfinite(hpr.baseline_enu), axis=1) + dot = np.sum(hpr.baseline_enu[:-1] * hpr.baseline_enu[1:], axis=1) + jump_deg = np.degrees(np.arccos(np.clip(dot, -1.0, 1.0))) + rate = jump_deg / np.maximum(dt, 1e-12) + return { + "sample_count": int(hpr.t_s.size), + "q4_valid_sample_count": int(np.count_nonzero(hpr.valid)), + "pair_count": int(dt.size), + "pair_with_invalid_endpoint_count": int(np.count_nonzero(~(hpr.valid[:-1] & hpr.valid[1:]))), + "dt_s_p50_p95_max": np.percentile(dt[np.isfinite(dt)], [50.0, 95.0, 100.0]), + "dt_too_short_count": int(np.count_nonzero(dt < 0.03)), + "dt_too_long_count": int(np.count_nonzero(dt > 0.25)), + "baseline_jump_rate_over_45deg_s_count": int(np.count_nonzero( + finite_vector[:-1] & finite_vector[1:] & (rate > 45.0) + )), + } + + +def _hpr_axis_mapping(session, hpr) -> dict[str, object]: + valid = hpr.valid & np.isfinite(hpr.t_s) + if np.count_nonzero(valid) < 8: + return {"available": False, "reason": "fewer_than_8_q4_hpr_samples"} + t = hpr.t_s[valid] + dt = np.diff(t) + keep = np.r_[True, (dt > 0.03) & (dt <= 0.25)] + t = t[keep] + # hpr arrays preserve GNHPR order after time sorting; heading must unwrap. + hpr_rows = sorted(session.rtk_by_type.get("GNHPR", []), key=lambda row: _f(row, "t_device_s")) + heading = np.unwrap(np.deg2rad(np.asarray([_f(row, "heading_deg") for row in hpr_rows])))[valid][keep] + pitch = np.asarray([_f(row, "pitch_deg") for row in hpr_rows])[valid][keep] + if t.size < 8: + return {"available": False, "reason": "insufficient_contiguous_q4_hpr"} + heading_rate = np.gradient(heading, t) + gyro = np.column_stack([np.interp(t, session.imu.t_s, session.imu.gyro_rad_s[:, axis]) for axis in range(3)]) + correlation = [] + for axis in range(3): + value = np.corrcoef(heading_rate, gyro[:, axis])[0, 1] + correlation.append(float(value) if np.isfinite(value) else np.nan) + best_axis = int(np.nanargmax(np.abs(correlation))) if np.any(np.isfinite(correlation)) else None + return { + "available": best_axis is not None, + "hpr_heading_unwrapped_range_deg": float(np.degrees(np.ptp(heading))), + "hpr_heading_net_rotation_deg": float(np.degrees(heading[-1] - heading[0])), + "hpr_heading_cumulative_absolute_rotation_deg": float(np.degrees(np.sum(np.abs(np.diff(heading))))), + "hpr_pitch_range_deg": float(np.ptp(pitch)), + "hpr_pitch_cumulative_absolute_change_deg": float(np.sum(np.abs(np.diff(pitch)))), + "heading_rate_to_imu_gyro_correlation_xyz": np.asarray(correlation), + "best_correlated_imu_axis": best_axis, + "expected_z_axis_correlation": correlation[2], + "note": "heading is unwrapped; sign depends on GNHPR clockwise-from-north convention", + } + + +def _bias_absorption_check(session, intervals: list[dict[str, object]]) -> dict[str, object]: + report = audit_imu(session.imu) + checks = [] + for item in intervals: + if item["duration_s"] < 1.0: + continue + start_s, end_s = item["start_s"], item["end_s"] + raw = _gyro_metrics(session, start_s, end_s) + static_corrected = _gyro_metrics(session, start_s, end_s, report.gyro_bias_rad_s) + t, gyro = _interval_imu(session, start_s, end_s) + mean = np.mean(gyro, axis=0) if gyro.size else np.zeros(3) + mean_removed = _gyro_metrics(session, start_s, end_s, mean) + raw_abs = np.asarray(raw["cumulative_absolute_rotation_xyz_deg"]) + removed_abs = np.asarray(mean_removed["cumulative_absolute_rotation_xyz_deg"]) + ratio = removed_abs / np.maximum(raw_abs, 1e-9) + checks.append({ + "start_s": start_s, "end_s": end_s, + "static_bias_rad_s": report.gyro_bias_rad_s, + "segment_mean_gyro_rad_s": mean, + "segment_mean_removed_to_raw_abs_rotation_ratio_xyz": ratio, + "static_bias_corrected": static_corrected, + "mean_removal_would_absorb_motion": bool(np.any(ratio < 0.5)), + }) + return {"imu_static_audit": report, "interval_checks": checks, + "note": "Engineering audit integrates raw gyro; it does not subtract a segment mean."} + + +def _unit_check(session) -> dict[str, object]: + gyro = session.imu.gyro_rad_s + norm = np.linalg.norm(gyro, axis=1) + p99 = float(np.percentile(norm, 99.0)) if norm.size else np.nan + return { + "gyro_p99_norm_rad_s": p99, + "gyro_p99_norm_deg_s": float(np.degrees(p99)), + "gyro_peak_norm_rad_s": float(np.max(norm)) if norm.size else np.nan, + "suspect_deg_per_second_stored_as_rad_per_second": bool(np.isfinite(p99) and p99 > 20.0), + "suspect_near_zero_gyro_scale": bool(np.isfinite(p99) and p99 < 1e-4), + "unit_contract": "unified imu.npz gyro_rad_s is radians per second", + } + + +def _candidate_scores(session_audit: dict[str, object]) -> dict[str, float]: + raw = session_audit["raw_valid_intervals"] + if not raw: + return {"circle": 0.0, "left_right": 0.0, "slope": 0.0} + cumulative = np.zeros(3) + net = np.zeros(3) + for interval in raw: + gyro = interval["gyro"] + value = np.asarray(gyro["cumulative_absolute_rotation_xyz_deg"], dtype=float) + signed = np.asarray(gyro["net_rotation_xyz_deg"], dtype=float) + if np.all(np.isfinite(value)): + cumulative += value + if np.all(np.isfinite(signed)): + net += signed + axis = session_audit["axis_mapping"] + heading_range = abs(float(axis.get("hpr_heading_unwrapped_range_deg", 0.0) or 0.0)) + heading_abs = abs(float(axis.get("hpr_heading_cumulative_absolute_rotation_deg", 0.0) or 0.0)) + heading_net = abs(float(axis.get("hpr_heading_net_rotation_deg", 0.0) or 0.0)) + pitch_range = abs(float(axis.get("hpr_pitch_range_deg", 0.0) or 0.0)) + pitch_abs = abs(float(axis.get("hpr_pitch_cumulative_absolute_change_deg", 0.0) or 0.0)) + return { + "circle": max(heading_range, cumulative[2]), + "left_right": max(0.0, heading_abs - heading_net, cumulative[2] - abs(net[2])), + "slope": max(cumulative[0], cumulative[1]) ** 2 / max(1.0, cumulative[2]), + } + + +def _select_candidates(audits: list[dict[str, object]]) -> dict[str, dict[str, object] | None]: + remaining = list(audits) + chosen: dict[str, dict[str, object] | None] = {} + for kind in ("circle", "left_right", "slope"): + ranked = sorted(remaining, key=lambda item: item["candidate_scores"][kind], reverse=True) + choice = ranked[0] if ranked and ranked[0]["candidate_scores"][kind] > 0.0 else None + chosen[kind] = None if choice is None else { + "session_id": choice["session_id"], "score_deg": choice["candidate_scores"][kind], + "selection_metric": { + "circle": "max(unwrapped HPR heading range, raw gyro-z net/absolute rotation)", + "left_right": "unwrapped HPR heading cumulative change minus net change, cross-checked with gyro-z", + "slope": "tilt-dominance: max(raw gyro-x/y cumulative rotation)^2 / raw gyro-z cumulative rotation", + }[kind], + } + if choice is not None: + remaining.remove(choice) + return chosen + + +def _audit_session(session, period_s: float) -> dict[str, object]: + reference = _height_reference([session]) + hpr = _all_hpr(session) + best_rows = session.rtk_by_type.get("BESTNAVA", []) + records = _raw_records(session) + raw_valid = _coalesce(records, include=True, label="raw_valid", session=session, + best_rows=best_rows, hpr=hpr) + raw_rejected = _coalesce(records, include=False, label="dropped_before_raw_valid", session=session, + best_rows=best_rows, hpr=hpr) + r0_nodes = [] if reference is None else _nodes(session, reference, period_s) + r0_intervals, r0_cuts, r0_node_intervals = _r0_runs_and_cuts( + session, r0_nodes, hpr, best_rows, period_s + ) + all_qualified = _segments([session], period_s) + qualified = [segment for segment in all_qualified if segment.session_id == session.session_id] + qualified_intervals = _qualified_summary(session, qualified, hpr, best_rows) + dropped_r0 = _dropped_r0_runs(session, r0_nodes, qualified, hpr, best_rows) + r0_selected_times = np.asarray([node.t_s for node in r0_nodes]) + decimated = [] + for record in records: + if not record["accepted"]: + continue + t_s = float(record["t_s"]) + selected = r0_selected_times.size and np.min(np.abs(r0_selected_times - t_s)) < 1e-8 + if not selected: + decimated.append({**record, "accepted": False, "reasons": ["sample_period_decimation"]}) + decimation_cuts = _coalesce(decimated, include=False, label="dropped_raw_to_R0", session=session, + best_rows=best_rows, hpr=hpr) + raw_total, r0_total, qualified_total = map(_stage_total, (raw_valid, r0_intervals, qualified_intervals)) + retention = { + "raw_to_R0": { + "bestnava_count_ratio": r0_total["bestnava_count"] / max(raw_total["bestnava_count"], 1), + "doppler_count_ratio": r0_total["doppler_count"] / max(raw_total["doppler_count"], 1), + "duration_ratio": r0_total["duration_s"] / max(raw_total["duration_s"], 1e-9), + }, + "R0_to_qualified": { + "bestnava_count_ratio": qualified_total["bestnava_count"] / max(r0_total["bestnava_count"], 1), + "doppler_count_ratio": qualified_total["doppler_count"] / max(r0_total["doppler_count"], 1), + "duration_ratio": qualified_total["duration_s"] / max(r0_total["duration_s"], 1e-9), + }, + } + audit = { + "session_id": session.session_id, "batch_id": session.batch_id, + "raw_valid_intervals": raw_valid, "R0_after_cuts_intervals": r0_intervals, + "qualified_segments": qualified_intervals, + "stage_statistics": { + "raw_valid": _stage_statistics(session, raw_valid), + "R0_after_cuts": _stage_statistics(session, r0_intervals), + "qualified": _stage_statistics(session, qualified_intervals), + }, + "retention": retention, + "cut_intervals": [*raw_rejected, *decimation_cuts, *r0_cuts, *dropped_r0], + "r0_consecutive_node_intervals": r0_node_intervals, + "interval_penetration": _interval_penetration( + raw_valid, r0_intervals, qualified_intervals, + [*raw_rejected, *decimation_cuts, *r0_cuts, *dropped_r0], + ), + "hpr_chain_diagnostics": _hpr_chain_diagnostics(hpr), + "axis_mapping": _hpr_axis_mapping(session, hpr), + "unit_check": _unit_check(session), + "bias_absorption_check": _bias_absorption_check(session, raw_valid), + } + audit["candidate_scores"] = _candidate_scores(audit) + return audit + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--sample-period-s", type=float, default=1.0) + parser.add_argument("--session", action="append", help="Optional session id; may repeat.") + parser.add_argument( + "--inventory-only", action="store_true", + help="Only scan raw-valid gyro/HPR excitation; skip R0 and qualified-segment work.", + ) + args = parser.parse_args(argv) + sessions = load_unified_sessions( + args.manifest, + selected_session_ids=None if args.session is None else set(args.session), + ) + if args.inventory_only: + audits = [] + for session in sessions: + hpr = _all_hpr(session) + best_rows = session.rtk_by_type.get("BESTNAVA", []) + records = _raw_records(session) + raw_valid = _coalesce(records, include=True, label="raw_valid", session=session, + best_rows=best_rows, hpr=hpr) + audit = { + "session_id": session.session_id, + "batch_id": session.batch_id, + "raw_valid_intervals": raw_valid, + "hpr_chain_diagnostics": _hpr_chain_diagnostics(hpr), + "axis_mapping": _hpr_axis_mapping(session, hpr), + "unit_check": _unit_check(session), + } + audit["candidate_scores"] = _candidate_scores(audit) + audits.append(audit) + scope = "raw-valid motion inventory only; no R0/qualified work or optimisation" + else: + audits = [_audit_session(session, args.sample_period_s) for session in sessions] + scope = "motion-excitation penetration audit only; no lever fit/prior/bootstrap/sensitivity" + payload = { + "scope": scope, + "sample_period_s": args.sample_period_s, + "session_count": len(audits), + "selected_dynamic_candidates": _select_candidates(audits), + "sessions": audits, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload), ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(json.dumps({ + "session_count": len(audits), + "selected_dynamic_candidates": payload["selected_dynamic_candidates"], + }, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/tools/audit_rtk_imu_multihorizon.py b/tools/audit_rtk_imu_multihorizon.py new file mode 100644 index 0000000..16fe59e --- /dev/null +++ b/tools/audit_rtk_imu_multihorizon.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +'''Multi-horizon open-loop RTK/IMU propagation audit without optimization.''' +from __future__ import annotations +import argparse, json, sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.imu_preintegration import preintegrate_imu +from imu_lidar.rtk_imu_engineering import ( + G_ENU, _all_hpr, _height_reference, _hpr_factor_observation, _world_rtk) +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from tools.audit_rtk_imu_factor_consistency import ( + MECHANICAL_L_I_M, _best_arrays, _jsonable, _nearest_imu, _summary) + +HORIZONS_S = (1.,2.,5.,10.,20.) + +def _angle_deg(left,right): + return float(np.degrees(np.arccos(np.clip(np.dot(left,right),-1.,1.)))) + +def _scalar_summary(values): + a = np.asarray(values,dtype=float) + if not a.size: + return {'count':0,'rms':np.nan,'p95_abs':np.nan} + return {'count':len(a),'rms':float(np.sqrt(np.mean(a*a))), + 'p95_abs':float(np.percentile(np.abs(a),95.))} + +def _evaluate_session(session,reference,rotation,lever,bg,ba): + _,times,p_ant,v_ant = _best_arrays(session,reference) + hpr = _all_hpr(session) + baseline_I = rotation.T[:,0] + values = {h:{'position':[],'velocity':[],'attitude':[],'baseline':[]} + for h in HORIZONS_S} + pair_pre = [] + for left,right in zip(times[:-1],times[1:]): + if not .5 <= right-left <= 1.75: + pair_pre.append(None) + else: + pair_pre.append(preintegrate_imu( + session.imu.t_s,session.imu.gyro_rad_s,session.imu.acc_m_s2, + float(left),float(right),bg,ba)) + for start,t0 in enumerate(times): + baseline0,_,valid0,_,_ = _hpr_factor_observation(hpr,float(t0)) + imu0 = _nearest_imu(session,float(t0)) + if not valid0 or imu0 is None: continue + R0 = _world_rtk(baseline0) @ rotation + p_i0 = p_ant[start]-R0@lever + v_i0 = v_ant[start]-R0@np.cross(session.imu.gyro_rad_s[imu0]-bg,lever) + delta_R, delta_v, delta_p = np.eye(3), np.zeros(3), np.zeros(3) + elapsed, matched = 0., set() + for end in range(start+1,len(times)): + pre = pair_pre[end-1] + if pre is None: break + delta_p = delta_p + delta_v*pre.duration_s + delta_R@pre.delta_p + delta_v = delta_v + delta_R@pre.delta_v + delta_R = delta_R@pre.delta_R + elapsed = float(times[end]-t0) + if elapsed > max(HORIZONS_S)+.25: break + candidates = [h for h in HORIZONS_S if h not in matched and abs(elapsed-h) <= .25] + if not candidates: continue + horizon = min(candidates,key=lambda h:abs(elapsed-h)) + matched.add(horizon) + baseline1,_,valid1,_,_ = _hpr_factor_observation(hpr,float(times[end])) + imu1 = _nearest_imu(session,float(times[end])) + if not valid1 or imu1 is None: continue + p_i1 = p_i0+v_i0*elapsed+.5*G_ENU*elapsed**2+R0@delta_p + v_i1 = v_i0+G_ENU*elapsed+R0@delta_v + R1 = R0@delta_R + predicted_p = p_i1+R1@lever + predicted_v = v_i1+R1@np.cross(session.imu.gyro_rad_s[imu1]-bg,lever) + observed_R1 = _world_rtk(baseline1)@rotation + values[horizon]['position'].append(predicted_p-p_ant[end]) + values[horizon]['velocity'].append(predicted_v-v_ant[end]) + values[horizon]['attitude'].append( + np.degrees(Rotation.from_matrix(observed_R1.T@R1).magnitude())) + values[horizon]['baseline'].append(_angle_deg(R1@baseline_I,baseline1)) + summary = {str(int(h)):{ + 'position_error_m':_summary(values[h]['position']), + 'velocity_error_m_s':_summary(values[h]['velocity']), + 'attitude_level_completed_error_deg':_scalar_summary(values[h]['attitude']), + 'observable_baseline_angular_error_deg':_scalar_summary(values[h]['baseline'])} + for h in HORIZONS_S} + return summary, values + +def _summarize_values(values): + return {str(int(h)):{ + 'position_error_m':_summary(values[h]['position']), + 'velocity_error_m_s':_summary(values[h]['velocity']), + 'attitude_level_completed_error_deg':_scalar_summary(values[h]['attitude']), + 'observable_baseline_angular_error_deg':_scalar_summary(values[h]['baseline'])} + for h in HORIZONS_S} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--session',action='append',required=True) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--gyro-bias-rad-s',nargs=3,type=float,default=[0.,0.,0.]) + parser.add_argument('--accel-bias-m-s2',nargs=3,type=float,default=[0.,0.,0.]) + args = parser.parse_args() + sessions = load_unified_sessions(args.manifest,selected_session_ids=set(args.session)) + reference = _height_reference(sessions) + if reference is None: raise RuntimeError('no BEST height reference') + rotation = Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + lever = np.asarray(args.mechanical_l_I_m) + bg, ba = np.asarray(args.gyro_bias_rad_s), np.asarray(args.accel_bias_m_s2) + combined = {h:{'position':[],'velocity':[],'attitude':[],'baseline':[]} + for h in HORIZONS_S} + per_session = {} + for session in sessions: + per_session[session.session_id], raw = _evaluate_session( + session,reference,rotation,lever,bg,ba) + for h in HORIZONS_S: + for key in combined[h]: combined[h][key].extend(raw[h][key]) + aggregate = _summarize_values(combined) + one, twenty = aggregate['1'], aggregate['20'] + growth = { + 'position_p95_ratio_20s_over_1s':twenty['position_error_m']['vector_p95']/one['position_error_m']['vector_p95'], + 'velocity_p95_ratio_20s_over_1s':twenty['velocity_error_m_s']['vector_p95']/one['velocity_error_m_s']['vector_p95'], + 'attitude_p95_ratio_20s_over_1s':twenty['observable_baseline_angular_error_deg']['p95_abs']/one['observable_baseline_angular_error_deg']['p95_abs']} + significant = bool( + growth['position_p95_ratio_20s_over_1s'] >= 3. + and twenty['position_error_m']['vector_p95']-one['position_error_m']['vector_p95'] >= .5) + payload = { + 'scope':'open-loop only; no least_squares/free/prior/LOO/bootstrap/sensitivity', + 'least_squares_called':False,'rotation_source':'R2G_gravity_level_prior', + 'mechanical_l_I_m':lever,'gyro_bias_rad_s':bg,'accel_bias_m_s2':ba, + 'bias_source':'current nominal engineering bias; configurable CLI; no fit artifact bias was persisted', + 'aggregate_by_horizon_s':aggregate,'per_session_by_horizon_s':per_session, + 'one_second_reanchored_control':{ + 'definition':'each interval restarts from observed BEST p/v and HPR-completed R', + 'result':one}, + 'growth_20s_over_1s':growth, + 'significant_accumulated_drift':significant, + 'legacy_long_segment_deterministic_model_deprecated':significant} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'aggregate':aggregate,'growth':growth, + 'legacy_deprecated':significant}),ensure_ascii=False,indent=2)) + return 0 + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/audit_rtk_imu_propagation_bias_root_cause.py b/tools/audit_rtk_imu_propagation_bias_root_cause.py new file mode 100644 index 0000000..46befe1 --- /dev/null +++ b/tools/audit_rtk_imu_propagation_bias_root_cause.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 +'''Root-cause audit for paired BEST/Doppler propagation innovations.''' +from __future__ import annotations +import argparse,json,sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import G0,G_ENU,_height_reference,_nodes,_world_rtk +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem +from tools.audit_rtk_imu_factor_consistency import _jsonable +from tools.audit_rtk_imu_heldout_innovation import _calibration_biases +from tools.audit_rtk_imu_innovation_noise import _factor_report,_interval_innovations +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments + +def _vector_summary(values): + a=np.asarray(values,dtype=float).reshape(-1,3) + if not len(a): return {'count':0} + norm=np.linalg.norm(a,axis=1) + return {'count':len(a),'bias':np.mean(a,axis=0), + 'axis_rms':np.sqrt(np.mean(a*a,axis=0)), + 'axis_p95_abs':np.percentile(np.abs(a),95,axis=0), + 'vector_rms':float(np.sqrt(np.mean(norm*norm))), + 'vector_p95':float(np.percentile(norm,95)), + 'empirical_covariance':np.cov(a,rowvar=False)} + +def _correlation(left,right): + a,b=np.asarray(left),np.asarray(right); result=np.full(3,np.nan) + for axis in range(3): + if len(a)>2 and np.std(a[:,axis])>1e-12 and np.std(b[:,axis])>1e-12: + result[axis]=np.corrcoef(a[:,axis],b[:,axis])[0,1] + return result + +def _root_report(position,velocity): + p={x['interval_id']:x for x in position}; v={x['interval_id']:x for x in velocity} + ids=sorted(set(p)&set(v)); records=[] + for key in ids: + left,right=p[key],v[key]; dt=float(left['dt_s']) + a_p=2.*np.asarray(left['residual'])/(dt*dt) + a_v=np.asarray(right['residual'])/dt + R=np.asarray(left['R0_WI']) + records.append({**{k:left[k] for k in ( + 'interval_id','session','motion','t_s','speed_bin','gyro_bin')}, + 'dt_s':dt,'a_position':a_p,'a_velocity':a_v, + 'difference':a_v-a_p,'a_common':.5*(a_p+a_v), + 'a_common_body':R.T@(.5*(a_p+a_v))}) + def summarize(items): + if not items: + return {'interval_count':0, + 'a_err_from_position_m_s2':{'count':0}, + 'a_err_from_velocity_m_s2':{'count':0}, + 'per_axis_correlation':[np.nan]*3, + 'mean_direction_cosine':np.nan, + 'mean_magnitude_ratio_velocity_over_position':np.nan, + 'difference_velocity_minus_position_m_s2':{'count':0}, + 'common_acceleration_world_m_s2':{'count':0}, + 'common_acceleration_body_m_s2':{'count':0}, + 'equivalent_horizontal_tilt_rad':np.nan, + 'equivalent_horizontal_tilt_deg':np.nan} + ap=[x['a_position'] for x in items]; av=[x['a_velocity'] for x in items] + diff=[x['difference'] for x in items]; common=[x['a_common'] for x in items] + body=[x['a_common_body'] for x in items] + mean_p=np.mean(ap,axis=0); mean_v=np.mean(av,axis=0) + denom=np.linalg.norm(mean_p)*np.linalg.norm(mean_v) + cosine=float(mean_p@mean_v/denom) if denom>1e-12 else np.nan + ratio=float(np.linalg.norm(mean_v)/max(np.linalg.norm(mean_p),1e-12)) + body_bias=np.mean(body,axis=0) + body_std=np.std(body,axis=0) + horizontal=float(np.linalg.norm(body_bias[:2])) + return {'interval_count':len(items), + 'a_err_from_position_m_s2':_vector_summary(ap), + 'a_err_from_velocity_m_s2':_vector_summary(av), + 'per_axis_correlation':_correlation(ap,av), + 'mean_direction_cosine':cosine,'mean_magnitude_ratio_velocity_over_position':ratio, + 'difference_velocity_minus_position_m_s2':_vector_summary(diff), + 'common_acceleration_world_m_s2':_vector_summary(common), + 'common_acceleration_body_m_s2':_vector_summary(body), + 'body_bias_stability_std_over_bias_norm':float( + np.linalg.norm(body_std)/max(np.linalg.norm(body_bias),1e-12)), + 'constant_body_accelerometer_bias_direction_stable':bool( + np.linalg.norm(body_std)<=np.linalg.norm(body_bias)), + 'equivalent_horizontal_tilt_rad':horizontal/G0, + 'equivalent_horizontal_tilt_deg':float(np.degrees(horizontal/G0)), + 'gravity_tilt_leakage_magnitude_le_0p5deg':bool( + np.degrees(horizontal/G0)<=.5)} + def grouped(key): + groups={} + for record in records: groups.setdefault(record[key],[]).append(record) + return {name:summarize(items) for name,items in groups.items()} + overall=summarize(records) + detection_checks={} + if records: + mp=np.asarray(overall['a_err_from_position_m_s2']['bias']) + mv=np.asarray(overall['a_err_from_velocity_m_s2']['bias']) + detection_checks={'mean_direction_cosine_ge_0p95': + overall['mean_direction_cosine']>=.95, + 'mean_magnitude_ratio_in_0p75_1p25': + .75<=overall['mean_magnitude_ratio_velocity_over_position']<=1.25, + 'mean_acceleration_difference_norm_le_0p05_m_s2': + np.linalg.norm(mv-mp)<=.05} + detected=bool(all(detection_checks.values())) + else: + detected=False + return {'overall':overall,'per_session':grouped('session'), + 'by_motion_class':grouped('motion'),'by_speed':grouped('speed_bin'), + 'by_gyro_norm':grouped('gyro_bin'), + 'common_acceleration_detection_gate':{ + 'thresholds':{'direction_cosine_min':.95, + 'magnitude_ratio_range':[.75,1.25], + 'mean_difference_norm_max_m_s2':.05}, + 'checks':detection_checks,'passed':detected}, + 'common_constant_acceleration_error_detected':detected},records + +def _outside_targets(t,intervals): + return all(not (start-1.<=t<=end+1.) for start,end in intervals) + +def _physical_static_biases(sessions,reference,R,heldout): + ranges={} + for item in heldout: + ranges.setdefault(item['session_id'],[]).append((item['start_s'],item['end_s'])) + result={} + for session in sessions: + estimates=[]; times=[] + for node in _nodes(session,reference,1.): + if (node.zupt_static and node.hpr_factor_valid and + _outside_targets(node.t_s,ranges.get(session.session_id,[]))): + R_WI=_world_rtk(node.baseline_enu)@R + estimates.append(node.accel_m_s2-R_WI.T@(-G_ENU)); times.append(node.t_s) + if len(estimates)>=3: + a=np.asarray(estimates) + result[session.session_id]={'available':True,'sample_count':len(a), + 'time_min_s':min(times),'time_max_s':max(times), + 'physical_accel_bias_m_s2':np.median(a,axis=0), + 'sample_axis_std_m_s2':np.std(a,axis=0), + 'method':('target-excluded zupt_static + fixed R2G/HPR gravity; ' + 'horizontal components remain gravity-tilt confounded')} + else: + result[session.session_id]={'available':False,'sample_count':len(estimates), + 'reason':'fewer than 3 target-excluded independent static nodes'} + return result + +def _bias_distribution(values,key): + a=np.asarray([x[key] for x in values.values()],dtype=float) + return {'session_count':len(a),'mean':np.mean(a,axis=0),'std':np.std(a,axis=0), + 'min':np.min(a,axis=0),'max':np.max(a,axis=0), + 'peak_to_peak':np.ptp(a,axis=0)} + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--manifest',type=Path,required=True) + p.add_argument('--calibration-selection',type=Path,required=True) + p.add_argument('--all-selection',type=Path,required=True) + p.add_argument('--engineering-result',type=Path,required=True) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + args=p.parse_args() + engineering=json.loads(args.engineering_result.read_text(encoding='utf-8')) + calibration=json.loads(args.calibration_selection.read_text(encoding='utf-8')) + selected=json.loads(args.all_selection.read_text(encoding='utf-8')) + calibration_ids={x['candidate_id'] for x in calibration['selected_windows']} + heldout=[x for x in selected['selected_windows'] + if x['candidate_id'] not in calibration_ids] + if len(calibration_ids)!=47 or len(heldout)!=267: + raise RuntimeError(f'expected 47+267 windows, got {len(calibration_ids)}+{len(heldout)}') + lever=np.asarray(engineering['prior_constrained_solution']['result']['final_l_I_m']) + frozen=np.array([-.4518015159,-.2644749820,.7314656115]) + if not np.allclose(lever,frozen,atol=1e-10): + raise RuntimeError('candidate lever differs from frozen root-cause value') + sessions=load_unified_sessions( + args.manifest,selected_session_ids={x['session_id'] for x in heldout}) + reference=_height_reference(sessions) + segments=_restore_segments(sessions,reference,heldout,1.) + R=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + problems=[build_problem(segment,R,lever,.006) for segment in segments] + calibration_bias=_calibration_biases(engineering,problems) + physical_bias=_physical_static_biases(sessions,reference,R,heldout) + motion_map={'0808_20260808_092827':'circle', + '0808_20260808_082148':'left_right', + '0815_20260812_123424':'slope'} + + def evaluate(mode): + output={'best_position':[],'doppler':[],'imu_preintegration':[]} + for problem in problems: + session_id=problem.segment.session_id + if mode=='calibration_frozen': + bg=calibration_bias[session_id]['gyro_bias_rad_s'] + ba=calibration_bias[session_id]['accel_bias_m_s2'] + elif mode=='zero': + bg=np.zeros(3); ba=np.zeros(3) + else: + source=physical_bias[session_id] + if not source['available']: continue + bg=np.zeros(3); ba=source['physical_accel_bias_m_s2'] + motion=motion_map.get(session_id,'other_recovered_dynamic') + records=_interval_innovations(problem,motion,bg,ba) + for key,value in records.items(): output[key].extend(value) + root,pairs=_root_report(output['best_position'],output['doppler']) + return {'bias_source':mode, + 'BEST_position_innovation':_factor_report(output['best_position']), + 'Doppler_innovation':_factor_report(output['doppler']), + 'IMU_preintegration_innovation':_factor_report(output['imu_preintegration']), + 'propagation_acceleration_consistency':root},pairs,output + + modes={}; pairs={}; raw={} + for mode in ('calibration_frozen','zero','session_static_physical'): + modes[mode],pairs[mode],raw[mode]=evaluate(mode) + static_ids={x['interval_id'] for x in pairs['session_static_physical']} + common_subset={} + for mode in ('calibration_frozen','zero','session_static_physical'): + pos=[x for x in raw[mode]['best_position'] if x['interval_id'] in static_ids] + vel=[x for x in raw[mode]['doppler'] if x['interval_id'] in static_ids] + root,_=_root_report(pos,vel) + common_subset[mode]={'BEST_position_innovation':_factor_report(pos), + 'Doppler_innovation':_factor_report(vel), + 'propagation_acceleration_consistency':root} + def bias_norm(report,factor): + return float(np.linalg.norm(report[factor]['overall']['innovation_bias'])) + A,C=common_subset['calibration_frozen'],common_subset['session_static_physical'] + static_available=bool(static_ids) + best_reduction=(bias_norm(C,'BEST_position_innovation')/ + max(bias_norm(A,'BEST_position_innovation'),1e-12) + if static_available else np.inf) + doppler_reduction=(bias_norm(C,'Doppler_innovation')/ + max(bias_norm(A,'Doppler_innovation'),1e-12) + if static_available else np.inf) + nuisance_transfer=bool(static_available and best_reduction<=.5 and doppler_reduction<=.5) + high_gyro={} + for factor in ('BEST_position_innovation','Doppler_innovation'): + summary=modes['calibration_frozen'][factor]['by_gyro_norm'].get('gyro_ge_0p10',{}) + limit=.5 + high_gyro[factor]={'sample_count':summary.get('sample_count',0), + 'bias_norm':float(np.linalg.norm(summary.get('innovation_bias',[np.inf]*3))), + 'vector_p95':summary.get('vector_p95',np.inf), + 'passed':bool(summary.get('sample_count',0)>=20 and + np.linalg.norm(summary['innovation_bias'])<=.10 and + summary['vector_p95']<=limit)} + common_detected=modes['calibration_frozen'][ + 'propagation_acceleration_consistency'][ + 'common_constant_acceleration_error_detected'] + extrinsic_sensitive=bool(common_detected and + all(x['passed'] for x in high_gyro.values())) + propagation_passed=bool(nuisance_transfer) + graph_bias=engineering['prior_constrained_solution'][ + 'calibration_only_frozen_bias_by_session'] + graph_distribution=_bias_distribution(graph_bias,'accel_bias_m_s2') + physical_available={k:v for k,v in physical_bias.items() if v['available']} + physical_values={k:{'accel_bias_m_s2':v['physical_accel_bias_m_s2']} + for k,v in physical_available.items()} + payload={'scope':'propagation bias root-cause only; frozen extrinsic and covariance', + 'lever_reoptimized':False,'R2G_refit':False,'covariance_retuned':False, + 'R0_parser_modified':False,'new_window_selection':False, + 'data_only_free_bootstrap_LOO_called':False, + 'fixed_l_I_m':lever,'fixed_rotation_rpy_deg':args.rotation_rpy_deg, + 'calibration_window_count':47,'heldout_window_count':267, + 'target_GNSS_observation_used_for_bias_estimation':False, + 'bias_semantics':{ + 'graph_nuisance_accel_bias':( + 'node-graph nuisance absorbing IMU/model/attitude effects; ' + 'not assumed transferable physical sensor zero bias'), + 'physical_IMU_accel_bias':( + 'target-excluded session static estimate; horizontal components ' + 'remain gravity-tilt confounded')}, + 'graph_nuisance_accel_bias_distribution_m_s2':graph_distribution, + 'per_session_graph_nuisance_bias':graph_bias, + 'per_session_static_physical_bias':physical_bias, + 'static_physical_bias_distribution_m_s2':( + _bias_distribution(physical_values,'accel_bias_m_s2') + if physical_values else {'session_count':0}), + 'bias_source_ablation':modes, + 'common_static_interval_subset_ablation':common_subset, + 'static_common_subset_interval_count':len(static_ids), + 'physical_over_calibration_bias_norm_ratio':{ + 'BEST_position':best_reduction,'Doppler':doppler_reduction}, + 'common_constant_acceleration_error_detected':common_detected, + 'nuisance_bias_transfer_failure_detected':nuisance_transfer, + 'physical_ba_ablation_available':static_available, + 'nuisance_bias_transfer_assessment':( + 'confirmed' if nuisance_transfer else + 'not_testable_no_independent_static_segments' if not static_available + else 'not_confirmed_by_static_ablation'), + 'lever_sensitive_high_gyro_validation':high_gyro, + 'independent_propagation_validation_passed':propagation_passed, + 'independent_extrinsic_sensitive_validation_passed':extrinsic_sensitive, + 'engineering_translation_accepted':False, + 'acceptance_modified_by_this_audit':False} + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + compact={'common_constant_acceleration_error_detected':common_detected, + 'static_session_count':len(physical_available), + 'static_common_subset_interval_count':len(static_ids), + 'bias_norm_ratio':payload['physical_over_calibration_bias_norm_ratio'], + 'nuisance_bias_transfer_failure_detected':nuisance_transfer, + 'high_gyro':high_gyro, + 'independent_propagation_validation_passed':propagation_passed, + 'independent_extrinsic_sensitive_validation_passed':extrinsic_sensitive, + 'engineering_translation_accepted':False, + 'calibration_frozen_acceleration': + modes['calibration_frozen']['propagation_acceleration_consistency']['overall']} + print(json.dumps(_jsonable(compact),ensure_ascii=False,indent=2)) + return 0 +if __name__=='__main__': raise SystemExit(main()) diff --git a/tools/export_g90_rtk_to_sessions.py b/tools/export_g90_rtk_to_sessions.py index 5989e69..c47944a 100644 --- a/tools/export_g90_rtk_to_sessions.py +++ b/tools/export_g90_rtk_to_sessions.py @@ -46,6 +46,9 @@ CSV_FIELDS = [ "pitch_deg", "roll_deg", "heading_quality", + "heading_satellites", + "heading_age_s", + "heading_station_id", "heading_valid", "gga_utc", "hpr_utc", @@ -170,6 +173,9 @@ def merged_row( "pitch_deg": hpr_fields.get("pitch_deg"), "roll_deg": hpr_fields.get("roll_deg"), "heading_quality": hpr_fields.get("heading_quality"), + "heading_satellites": hpr_fields.get("heading_satellites"), + "heading_age_s": hpr_fields.get("heading_age_s"), + "heading_station_id": hpr_fields.get("heading_station_id"), "heading_valid": hpr_fields.get("heading_valid"), "gga_utc": fields.get("position_time_utc"), "hpr_utc": hpr_fields.get("position_time_utc"), diff --git a/tools/export_rtk_imu_unified.py b/tools/export_rtk_imu_unified.py new file mode 100644 index 0000000..5b90ef6 --- /dev/null +++ b/tools/export_rtk_imu_unified.py @@ -0,0 +1,290 @@ +#!/usr/bin/env python3 +"""Export paired G90/HI13 captures without replacing sensor time by host time.""" + +from __future__ import annotations + +import argparse +import csv +import json +import re +import sys +from collections import Counter +from datetime import datetime, timedelta, timezone +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from tools.h32_dlog.timeutil import utc_dotnet_ticks_to_unix_s +from tools.rscap_v2.capture_format_v2 import file_summary, read_capture +from tools.rscap_v2.g90_rtk import RtkSentence, iter_g90_sentences +from tools.rscap_v2.hi13_imu import Hi13Sample, iter_hi13_imu_samples +from tools.time_alignment import AffineClockModel, fit_affine_clock + +GPS_EPOCH_UNIX_S = datetime(1980, 1, 6, tzinfo=timezone.utc).timestamp() +DEFAULT_SOURCES = ( + ("0808", Path("D:/data/raw_serial_capture_v2"), "20260808"), + ("0815", Path("D:/data/0815/raw_serial_capture_v2"), None), + ("0819", Path("D:/data/0819/raw_serial_capture_v2"), None), +) +RTK_FIELDS = [ + "message_type", "t_device_s", "measurement_utc_s", "gnss_time_s", + "gnss_week", "gnss_tow_ms", "leap_seconds", "host_receive_utc_s", + "host_minus_measurement_s", "receive_utc_ticks", "checksum_valid", + "position_time_utc", "lat_deg", "lon_deg", "altitude_m", + "position_status", "position_type", "position_fixed", "fix_quality", + "satellites", "solution_satellites", "hdop", "undulation_m", + "lat_std_m", "lon_std_m", "altitude_std_m", "differential_age_s", + "solution_age_s", "station_id", "heading_deg", "pitch_deg", "roll_deg", + "heading_quality", "heading_satellites", "heading_age_s", + "heading_station_id", "heading_valid", "baseline_length_m", + "heading_type", "heading_solution_satellites", "velocity_status", + "velocity_type", "doppler_velocity_valid", "velocity_latency_s", + "velocity_age_s", "horizontal_speed_m_s", "track_ground_deg", + "velocity_east_m_s", "velocity_north_m_s", "vertical_speed_m_s", + "horizontal_speed_std_m_s", "vertical_speed_std_m_s", "raw_line", +] + + +def _capture_time(path: Path) -> datetime: + match = re.search(r"_(\d{8}-\d{6}\.\d+)_", path.name) + if not match: + raise ValueError(f"capture filename has no timestamp: {path}") + return datetime.strptime(match.group(1), "%Y%m%d-%H%M%S.%f") + + +def discover_pairs( + sources: tuple[tuple[str, Path, str | None], ...], + *, + max_start_delta_s: float = 1.5, +) -> tuple[list[dict], list[dict]]: + pairs: list[dict] = [] + unmatched: list[dict] = [] + for batch, folder, date_prefix in sources: + rtk = sorted(folder.glob("wheeltec-g90*.rscap")) + imu = sorted(folder.glob("hi13*.rscap")) + if date_prefix: + rtk = [path for path in rtk if date_prefix in path.name] + imu = [path for path in imu if date_prefix in path.name] + available = set(imu) + for rtk_path in rtk: + candidates = sorted( + ( + (abs((_capture_time(path) - _capture_time(rtk_path)).total_seconds()), path) + for path in available + ), + key=lambda item: item[0], + ) + if not candidates or candidates[0][0] > max_start_delta_s: + unmatched.append({"batch": batch, "rtk_rscap": str(rtk_path)}) + continue + delta, imu_path = candidates[0] + available.remove(imu_path) + stamp = _capture_time(rtk_path).strftime("%Y%m%d_%H%M%S") + pairs.append( + { + "session_id": f"{batch}_{stamp}", + "batch_id": batch, + "rtk_rscap": rtk_path, + "imu_rscap": imu_path, + "capture_start_delta_s": delta, + } + ) + return pairs, unmatched + + +def _fit_clock(samples: list[Hi13Sample]) -> AffineClockModel: + stride = max(1, len(samples) // 20000) + selected = samples[::stride] + device = np.asarray([sample.t_s for sample in selected], dtype=float) + host = np.asarray( + [utc_dotnet_ticks_to_unix_s(sample.host_receive_utc_ticks) for sample in selected], + dtype=float, + ) + return fit_affine_clock(device, host) + + +def _nmea_utc_to_unix_s(value: str, host_s: float) -> float: + packed = float(value) + hour = int(packed // 10000) + minute = int((packed - hour * 10000) // 100) + second = packed - hour * 10000 - minute * 100 + receive = datetime.fromtimestamp(host_s, tz=timezone.utc) + midnight = datetime(receive.year, receive.month, receive.day, tzinfo=timezone.utc).timestamp() + same_day = midnight + hour * 3600 + minute * 60 + second + return min((same_day - 86400.0, same_day, same_day + 86400.0), + key=lambda candidate: abs(candidate - host_s)) + + +def _measurement_time(row: RtkSentence) -> tuple[float, float | None]: + fields = row.fields + week = fields.get("gnss_week") + tow_ms = fields.get("gnss_tow_ms") + leap = fields.get("leap_seconds") + if week is not None and tow_ms is not None: + gnss_s = float(week) * 604800.0 + float(tow_ms) * 1e-3 + utc_s = GPS_EPOCH_UNIX_S + gnss_s - float(leap or 0) + return utc_s, gnss_s + value = fields.get("position_time_utc") + if value is None: + raise ValueError(f"{row.sentence_type} has no sensor measurement time") + host_s = utc_dotnet_ticks_to_unix_s(row.receive_utc_ticks) + return _nmea_utc_to_unix_s(str(value), host_s), None + + +def _format(value: object) -> object: + if value is None: + return "" + if isinstance(value, bool): + return int(value) + return value + + +def _write_imu_npz(path: Path, samples: list[Hi13Sample]) -> None: + np.savez_compressed( + path, + system_time_s=np.asarray([row.t_s for row in samples], dtype=np.float64), + system_time_ms=np.asarray([row.system_time_ms for row in samples], dtype=np.uint32), + host_receive_utc_s=np.asarray( + [utc_dotnet_ticks_to_unix_s(row.host_receive_utc_ticks) for row in samples], + dtype=np.float64, + ), + gyro_rad_s=np.asarray([row.gyro_rad_s for row in samples], dtype=np.float64), + accel_m_s2=np.asarray([row.accel_m_s2 for row in samples], dtype=np.float64), + rpy_deg=np.asarray([row.rpy_deg for row in samples], dtype=np.float64), + quaternion_wxyz=np.asarray([row.quaternion_wxyz for row in samples], dtype=np.float64), + mag_ut=np.asarray([row.mag_ut for row in samples], dtype=np.float64), + pps_sync_stamp_ms=np.asarray([row.pps_sync_stamp_ms for row in samples], dtype=np.uint16), + temperature_c=np.asarray([row.temperature_c for row in samples], dtype=np.int16), + air_pressure_pa=np.asarray([row.air_pressure_pa for row in samples], dtype=np.float64), + frame_tag=np.asarray([row.frame_tag for row in samples], dtype=np.uint8), + ) + + +def _write_rtk_csv( + path: Path, + rows: list[RtkSentence], + clock: AffineClockModel, +) -> Counter: + counts: Counter = Counter() + with path.open("w", encoding="utf-8", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=RTK_FIELDS) + writer.writeheader() + for row in rows: + host_s = utc_dotnet_ticks_to_unix_s(row.receive_utc_ticks) + try: + measurement_s, gnss_s = _measurement_time(row) + t_device_s = clock.inverse(measurement_s) + host_minus_measurement_s = host_s - measurement_s + except (TypeError, ValueError): + measurement_s = None + gnss_s = None + t_device_s = None + host_minus_measurement_s = None + counts[f"{row.sentence_type}_invalid_measurement_time"] += 1 + fields = dict(row.fields) + fields.update( + { + "message_type": row.sentence_type, + "t_device_s": t_device_s, + "measurement_utc_s": measurement_s, + "gnss_time_s": gnss_s, + "host_receive_utc_s": host_s, + "host_minus_measurement_s": host_minus_measurement_s, + "receive_utc_ticks": row.receive_utc_ticks, + "checksum_valid": row.checksum_valid, + "raw_line": row.raw_line, + } + ) + writer.writerow({name: _format(fields.get(name)) for name in RTK_FIELDS}) + counts[row.sentence_type] += 1 + return counts + + +def export_pair(pair: dict, output_root: Path, *, overwrite: bool) -> dict: + destination = output_root / str(pair["session_id"]) + if destination.exists() and not overwrite: + raise FileExistsError(f"{destination} exists; pass --overwrite") + destination.mkdir(parents=True, exist_ok=True) + imu_capture = read_capture(pair["imu_rscap"]) + rtk_capture = read_capture(pair["rtk_rscap"]) + imu_rows = iter_hi13_imu_samples(imu_capture) + if len(imu_rows) < 2: + raise ValueError(f"not enough CRC-valid HI91 rows: {pair['imu_rscap']}") + if np.any(np.diff(np.asarray([row.t_s for row in imu_rows])) <= 0): + raise ValueError(f"HI13 system_time is not strictly increasing: {pair['imu_rscap']}") + clock = _fit_clock(imu_rows) + rtk_rows = iter_g90_sentences(rtk_capture) + _write_imu_npz(destination / "imu.npz", imu_rows) + counts = _write_rtk_csv(destination / "rtk.csv", rtk_rows, clock) + quaternion = np.asarray([row.quaternion_wxyz for row in imu_rows], dtype=float) + quaternion_norm = np.linalg.norm(quaternion, axis=1) + summary = { + **{key: str(value) if isinstance(value, Path) else value for key, value in pair.items()}, + "time_policy": { + "master": "HI13 system_time; RTK GNSS measurement time mapped into that clock", + "host_receive_time": "diagnostic and affine cross-clock bridge only", + }, + "imu_capture": file_summary(imu_capture), + "rtk_capture": file_summary(rtk_capture), + "imu_valid_rows": len(imu_rows), + "imu_time_span_s": float(imu_rows[-1].t_s - imu_rows[0].t_s), + "quaternion_norm_p01_p50_p99": [ + float(np.percentile(quaternion_norm, percentile)) for percentile in (1, 50, 99) + ], + "rtk_records": dict(counts), + "clock_model_device_to_host": clock.to_dict(), + } + (destination / "export_summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" + ) + return summary + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output-root", type=Path, required=True) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--session", action="append") + args = parser.parse_args(argv) + pairs, unmatched = discover_pairs(DEFAULT_SOURCES) + if args.session: + selected = set(args.session) + pairs = [pair for pair in pairs if pair["session_id"] in selected] + args.output_root.mkdir(parents=True, exist_ok=True) + summaries = [] + for index, pair in enumerate(pairs, 1): + print(f"[{index}/{len(pairs)}] {pair['session_id']}", flush=True) + summary_path = args.output_root / pair["session_id"] / "export_summary.json" + if args.resume and summary_path.is_file(): + summaries.append(json.loads(summary_path.read_text(encoding="utf-8"))) + continue + summaries.append(export_pair(pair, args.output_root, overwrite=args.overwrite)) + manifest = { + "schema_version": 3, + "session_count": len(summaries), + "unmatched_rtk": unmatched, + "sessions": [ + { + "session_id": item["session_id"], + "batch_id": item["batch_id"], + "directory": str((args.output_root / item["session_id"]).resolve()), + "rtk_records": item["rtk_records"], + "imu_valid_rows": item["imu_valid_rows"], + } + for item in summaries + ], + } + (args.output_root / "manifest.json").write_text( + json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps({"output_root": str(args.output_root), **manifest}, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/finalize_rtk_imu_engineering_release.py b/tools/finalize_rtk_imu_engineering_release.py new file mode 100644 index 0000000..7eef10f --- /dev/null +++ b/tools/finalize_rtk_imu_engineering_release.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +'''Combine final mechanical-prior engineering release gates without refitting.''' +from __future__ import annotations +import argparse,json,sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.geometry import make_transform +from tools.audit_rtk_imu_factor_consistency import _jsonable + +SUMMARY=('Translation is mechanically anchored and dynamically validated. ' + 'The current dataset does not independently observe translation accurately ' + 'enough for data-only calibration, and does not provide meaningful refinement ' + 'beyond the mechanical prior.') + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--calibration',type=Path,required=True) + p.add_argument('--heldout-postfit',type=Path,required=True) + p.add_argument('--innovation',type=Path,required=True) + p.add_argument('--sensitivity',type=Path,required=True) + p.add_argument('--convergence-retry',type=Path,required=True) + p.add_argument('--propagation-root-cause',type=Path) + p.add_argument('--output',type=Path,required=True) + args=p.parse_args() + calibration=json.loads(args.calibration.read_text(encoding='utf-8')) + heldout=json.loads(args.heldout_postfit.read_text(encoding='utf-8')) + innovation=json.loads(args.innovation.read_text(encoding='utf-8')) + sensitivity=json.loads(args.sensitivity.read_text(encoding='utf-8')) + retry=json.loads(args.convergence_retry.read_text(encoding='utf-8')) + root_cause=(None if args.propagation_root_cause is None else + json.loads(args.propagation_root_cause.read_text(encoding='utf-8'))) + lever=np.asarray(calibration['prior_constrained_solution']['result']['final_l_I_m']) + R=Rotation.from_euler('xyz',[.4543066225,-.0026392019,.0122384129], + degrees=True).as_matrix() + candidate_T=make_transform(-R@lever,R) + candidate_inverse=np.linalg.inv(candidate_T) + inverse_error=float(np.linalg.norm(candidate_T@candidate_inverse-np.eye(4))) + if inverse_error>=1e-10: raise RuntimeError('candidate transforms are not inverse') + comparison=calibration['comparisons'] + def nonconflicting(name): + value=comparison[name] + return (value['relative_cost_delta']<=.05 and + all(x['p95_abs_delta']<=.25 + for x in value['factor_residual_delta'].values())) + mechanical_consistent=bool( + nonconflicting('fixed_vs_free') and nonconflicting('prior_data_vs_free')) + overall=heldout['heldout_validation']['overall'] + physical_checks={ + 'all_267_converged_after_retry': + retry['heldout_convergence_after_retry']==1., + 'BEST_position_vector_p95_le_0p20_m': + overall['best_position_physical_m']['vector_p95']<=.20, + 'Doppler_vector_p95_le_0p50_m_s': + overall['doppler_physical_m_s']['vector_p95']<=.50, + 'HPR_normalized_p95_le_4': + overall['residual_by_factor']['hpr']['p95_abs']<=4., + 'preintegration_normalized_p95_le_3': + overall['residual_by_factor']['imu_preintegration']['p95_abs']<=3.} + physical_passed=bool(all(physical_checks.values())) + statistical_passed=bool(.25<=overall['global_chi_square_per_dof']<=4.) + underdispersion=bool(physical_passed and not statistical_passed and + overall['global_chi_square_per_dof']<.25) + independent=bool(innovation['independent_heldout_innovation_passed']) + rotation=bool(sensitivity['rotation_sensitivity_passed']) + accepted=bool(mechanical_consistent and physical_passed and independent and rotation) + payload={'scope':'final mechanical-prior RTK-IMU engineering release decision', + 'no_refit_performed':True,'data_only_full_free_called':False, + 'bootstrap_called':False,'loo_called':False, + 'covariance_retuned':False,'new_window_selection_called':False, + 'parser_R0_modified':False, + 'data_only_translation_accepted':False, + 'translation_refined_by_data':False, + 'mechanical_prior_consistent_with_calibration':mechanical_consistent, + 'heldout_physical_validation_passed':physical_passed, + 'heldout_physical_gate_checks':physical_checks, + 'heldout_statistical_scale_passed':statistical_passed, + 'heldout_postfit_chi_square_per_dof': + overall['global_chi_square_per_dof'], + 'heldout_covariance_underdispersion_warning':underdispersion, + 'independent_heldout_innovation_passed':independent, + 'common_constant_acceleration_error_detected':( + None if root_cause is None else + root_cause['common_constant_acceleration_error_detected']), + 'independent_propagation_validation_passed':( + None if root_cause is None else + root_cause['independent_propagation_validation_passed']), + 'independent_extrinsic_sensitive_validation_passed':( + None if root_cause is None else + root_cause['independent_extrinsic_sensitive_validation_passed']), + 'rotation_sensitivity_passed':rotation, + 'engineering_translation_acceptance_formula':( + 'mechanical_prior_consistent_with_calibration AND ' + 'heldout_physical_validation_passed AND ' + 'independent_heldout_innovation_passed AND rotation_sensitivity_passed'), + 'engineering_translation_accepted':accepted, + 'result_nature':'mechanically anchored + dynamically validated', + 'summary':SUMMARY,'forbidden_descriptions':[ + 'data-only calibrated translation','dynamically refined mechanical lever'], + 'candidate_l_I_engineering_m':lever, + 'candidate_T_RTK_IMU':candidate_T, + 'candidate_T_IMU_RTK':candidate_inverse, + 'candidate_transform_inverse_error_norm':inverse_error, + 'l_I_engineering_m':lever if accepted else None, + 'T_RTK_IMU':candidate_T if accepted else None, + 'T_IMU_RTK':candidate_inverse if accepted else None, + 'transform_convention':{ + 'equation':'p_RTK = R_RTK_IMU * p_IMU + t_RTK_IMU', + 'translation':'t_RTK_IMU = -R_RTK_IMU * l_I', + 'RTK_origin':'ANT1 phase center'}, + 'rotation_source':'R2G_gravity_level_prior', + 'translation_conditional_on_rotation':True, + 'evidence':{ + 'calibration_path':str(args.calibration), + 'heldout_postfit_path':str(args.heldout_postfit), + 'innovation_path':str(args.innovation), + 'sensitivity_path':str(args.sensitivity), + 'convergence_retry_path':str(args.convergence_retry), + 'propagation_root_cause_path':( + None if args.propagation_root_cause is None + else str(args.propagation_root_cause)), + 'posterior_prior_variance_ratio': + calibration['prior_constrained_solution']['posterior_prior_variance_ratio'], + 'heldout_convergence_after_retry': + retry['heldout_convergence_after_retry'], + 'rotation_sensitivity_summary':sensitivity['summary']}} + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({key:payload[key] for key in ( + 'data_only_translation_accepted','translation_refined_by_data', + 'mechanical_prior_consistent_with_calibration', + 'heldout_physical_validation_passed','heldout_statistical_scale_passed', + 'heldout_covariance_underdispersion_warning', + 'independent_heldout_innovation_passed','rotation_sensitivity_passed', + 'engineering_translation_accepted')}),indent=2)) + return 0 +if __name__=='__main__': raise SystemExit(main()) diff --git a/tools/refine_rtk_imu_windows_by_actual_information.py b/tools/refine_rtk_imu_windows_by_actual_information.py new file mode 100644 index 0000000..6daba4f --- /dev/null +++ b/tools/refine_rtk_imu_windows_by_actual_information.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +'''Refine non-overlapping windows after comparing predicted and actual lever information.''' +from __future__ import annotations +import argparse,json,sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem,linearized_lever_information +from tools.audit_rtk_imu_factor_consistency import _jsonable +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments +from tools.select_rtk_imu_windows_by_lever_information import ( + STD_GATE,_overlap,_sample_keys,_summary,_window_candidates) + + +def _sqrt_psd(matrix,inverse=False): + values,vectors=np.linalg.eigh(.5*(matrix+matrix.T)) + values=np.maximum(values,1e-12) + scale=1./np.sqrt(values) if inverse else np.sqrt(values) + return (vectors*scale)@vectors.T + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--base-selection',type=Path,required=True) + parser.add_argument('--actual-result',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--window-duration-s',type=float,default=15.) + parser.add_argument('--max-additional-windows',type=int,default=400) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + args=parser.parse_args() + base=json.loads(args.base_selection.read_text(encoding='utf-8')) + actual=json.loads(args.actual_result.read_text(encoding='utf-8')) + actual_solution=next(iter(actual['solutions'].values())) + lever=np.asarray(actual_solution['final_l_I_m'],dtype=float) + actual_cov=np.asarray(actual_solution['lever_covariance_m2'],dtype=float) + actual_information=np.linalg.pinv(actual_cov,rcond=1e-9) + predicted_information=sum((np.asarray(item['single_window_information']) + for item in base['selected_windows']),np.zeros((3,3))) + correction=_sqrt_psd(actual_information)@_sqrt_psd(predicted_information,True) + sessions=load_unified_sessions(args.manifest) + reference=_height_reference(sessions) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + restored=_restore_segments(sessions,reference,base['selected_windows'], + args.sample_period_s) + session_by_id={session.session_id:session for session in sessions} + selected=[] + for item,segment in zip(base['selected_windows'],restored): + selected.append({**item,'segment':segment, + 'session':session_by_id[item['session_id']]}) + candidates=[entry for entry in _window_candidates( + sessions,reference,args.sample_period_s,args.window_duration_s) + if not any(_overlap(entry,item) for item in selected)] + for entry in candidates: + problem=build_problem(entry['segment'],rotation,lever,args.hpr_direct_sigma_rad) + raw,_,singular,weak=linearized_lever_information(problem,lever) + entry['raw_information']=raw + entry['information']=correction@raw@correction.T + entry['single_window_singular_values']=singular + entry['single_window_weakest_direction_I']=weak + total=actual_information.copy() + curve=[{'window_count':len(selected),'added_candidate_id':'actual_base', + 'information_source':'actual_joint_free_schur',**_summary(total)}] + remaining=list(candidates); saturation_count=0 + stop_reason='candidate_exhausted' + while remaining and len(selected)=3: + stop_reason='incremental_lambda_min_gain_saturated'; break + else: + if len(selected)>=len(base['selected_windows'])+args.max_additional_windows: + stop_reason='max_additional_windows_reached' + seen={'imu':set(),'gnss':set(),'hpr':set()}; duplicate={key:0 for key in seen} + output=[] + for order,entry in enumerate(selected): + keys=_sample_keys(entry); shared={key:len(value&seen[key]) for key,value in keys.items()} + for key,value in keys.items(): duplicate[key]+=shared[key]; seen[key].update(value) + information=np.asarray(entry['information'] if 'information' in entry + else entry['single_window_information']) + output.append({'selection_order':order,'candidate_id':entry['candidate_id'], + 'session_id':entry['session_id'],'start_s':entry['start_s'], + 'end_s':entry['end_s'],'duration_s':entry['duration_s'], + 'node_count':entry['node_count'],'seed_window':order=args.max_nfev), + 'retry_success':meta['success'],'residual':summary}) + converged=sum(x['retry_success'] for x in results) + payload={'scope':'10 held-out max_nfev windows; exact-model continuation retry', + 'lever_reoptimized':False,'covariance_parameters_modified':False, + 'parser_R0_modified':False,'windows_removed':False, + 'fixed_l_I_m':lever,'retry_max_nfev':args.max_nfev, + 'initial_nonconverged_count':len(results), + 'converged_after_retry_count':converged, + 'heldout_convergence_after_retry':(257+converged)/267., + 'remaining_nonconverged_count':len(results)-converged, + 'windows':results} + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'converged_after_retry':converged, + 'remaining':len(results)-converged, + 'heldout_convergence_after_retry':payload['heldout_convergence_after_retry'], + 'windows':[{'index':x['heldout_index'],'session':x['session_id'], + 'motion':x['motion_class'],'success':x['retry_success'], + 'nfev':x['retry_nfev'],'cost':x['retry_final_cost']} + for x in results]}),ensure_ascii=False,indent=2)) + return 0 +if __name__=='__main__': raise SystemExit(main()) diff --git a/tools/rscap_v2/g90_rtk.py b/tools/rscap_v2/g90_rtk.py index 115a2d6..58c3fe4 100644 --- a/tools/rscap_v2/g90_rtk.py +++ b/tools/rscap_v2/g90_rtk.py @@ -1,4 +1,9 @@ -"""Decode Wheeltec G90 NMEA (GGA / GNHPR) from a V2 .rscap capture.""" +"""Decode calibration-relevant Wheeltec G90 logs from a V2 capture. + +GNSS-owned measurement time is preserved for every record. Host receive time +only identifies the chunk that completed the line and must not be substituted +for the measurement timestamp. +""" from __future__ import annotations @@ -23,6 +28,32 @@ def nmea_checksum_valid(line: str) -> bool: return value == expected +def unicore_checksum_valid(line: str) -> bool: + """Validate the CRC32 suffix used by Unicore hash-prefixed logs.""" + + star = line.rfind("*") + if star < 0: + return False + try: + expected = int(line[star + 1 : star + 9], 16) + except ValueError: + return False + crc = 0 + for value in line[1:star].encode("ascii", "replace"): + crc ^= value + for _ in range(8): + crc = (crc >> 1) ^ (0xEDB88320 if crc & 1 else 0) + return (crc & 0xFFFFFFFF) == expected + + +def g90_checksum_valid(line: str) -> bool: + if line.startswith("$"): + return nmea_checksum_valid(line) + if line.startswith("#"): + return unicore_checksum_valid(line) + return False + + def _safe_float(value: str): try: return float(value) @@ -76,8 +107,124 @@ def parse_gnhpr(line: str) -> dict: "pitch_deg": _safe_float(fields[3]), "roll_deg": _safe_float(fields[4]), "heading_quality": quality, - "satellites": _safe_int(fields[6]), - "heading_valid": quality in {4, 5}, + "heading_satellites": _safe_int(fields[6]), + "heading_age_s": _safe_float(fields[7]) if len(fields) > 7 else None, + "heading_station_id": fields[8] if len(fields) > 8 else None, + "heading_valid": quality == 4, + } + + +def _split_unicore(line: str) -> tuple[list[str], list[str]]: + before_checksum = line[: line.rfind("*")] + header, payload = before_checksum.split(";", 1) + return header[1:].split(","), payload.split(",") + + +def _parse_unicore_header(fields: list[str]) -> dict: + if len(fields) < 9: + raise ValueError("Unicore ASCII header is incomplete") + return { + "gnss_week": _safe_int(fields[4]), + "gnss_tow_ms": _safe_int(fields[5]), + "leap_seconds": _safe_int(fields[8]), + } + + +def parse_bestnava(line: str) -> dict: + """Parse BESTNAVA position and Doppler-velocity fields.""" + + header, fields = _split_unicore(line) + if len(fields) < 30: + raise ValueError("BESTNAVA has too few fields") + result = { + "type": "BESTNAVA", + **_parse_unicore_header(header), + "position_status": fields[0], + "position_type": fields[1], + "lat_deg": _safe_float(fields[2]), + "lon_deg": _safe_float(fields[3]), + "altitude_m": _safe_float(fields[4]), + "undulation_m": _safe_float(fields[5]), + "lat_std_m": _safe_float(fields[7]), + "lon_std_m": _safe_float(fields[8]), + "altitude_std_m": _safe_float(fields[9]), + "station_id": fields[10].strip('"'), + "differential_age_s": _safe_float(fields[11]), + "solution_age_s": _safe_float(fields[12]), + "satellites": _safe_int(fields[13]), + "solution_satellites": _safe_int(fields[14]), + "velocity_status": fields[21], + "velocity_type": fields[22], + "velocity_latency_s": _safe_float(fields[23]), + "velocity_age_s": _safe_float(fields[24]), + "horizontal_speed_m_s": _safe_float(fields[25]), + "track_ground_deg": _safe_float(fields[26]), + "vertical_speed_m_s": _safe_float(fields[27]), + "vertical_speed_std_m_s": _safe_float(fields[28]), + "horizontal_speed_std_m_s": _safe_float(fields[29]), + } + speed = result["horizontal_speed_m_s"] + track = result["track_ground_deg"] + if speed is not None and track is not None: + angle = math.radians(track) + result["velocity_east_m_s"] = speed * math.sin(angle) + result["velocity_north_m_s"] = speed * math.cos(angle) + else: + result["velocity_east_m_s"] = None + result["velocity_north_m_s"] = None + result["position_fixed"] = ( + result["position_status"] == "SOL_COMPUTED" + and result["position_type"] == "NARROW_INT" + ) + result["doppler_velocity_valid"] = ( + result["velocity_status"] == "SOL_COMPUTED" + and result["velocity_type"] == "DOPPLER_VELOCITY" + ) + return result + + +def parse_pvtslna(line: str) -> dict: + """Parse PVTSLNA as a quality-rich fallback/diagnostic record.""" + + header, fields = _split_unicore(line) + if len(fields) < 34: + raise ValueError("PVTSLNA has too few fields") + speed_north = _safe_float(fields[17]) + speed_east = _safe_float(fields[18]) + return { + "type": "PVTSLNA", + **_parse_unicore_header(header), + "position_type": fields[0], + "altitude_m": _safe_float(fields[1]), + "lat_deg": _safe_float(fields[2]), + "lon_deg": _safe_float(fields[3]), + "altitude_std_m": _safe_float(fields[4]), + "lat_std_m": _safe_float(fields[5]), + "lon_std_m": _safe_float(fields[6]), + "differential_age_s": _safe_float(fields[7]), + "psr_position_type": fields[8], + "undulation_m": _safe_float(fields[12]), + "satellites": _safe_int(fields[13]), + "solution_satellites": _safe_int(fields[14]), + "velocity_north_m_s": speed_north, + "velocity_east_m_s": speed_east, + "horizontal_speed_m_s": ( + None if speed_north is None or speed_east is None + else math.hypot(speed_north, speed_east) + ), + "vertical_speed_m_s": _safe_float(fields[19]), + "heading_type": fields[20], + "baseline_length_m": _safe_float(fields[21]), + "heading_deg": _safe_float(fields[22]), + "pitch_deg": _safe_float(fields[23]), + "heading_satellites": _safe_int(fields[24]), + "heading_solution_satellites": _safe_int(fields[25]), + "gdop": _safe_float(fields[28]), + "pdop": _safe_float(fields[29]), + "hdop": _safe_float(fields[30]), + "htdop": _safe_float(fields[31]), + "tdop": _safe_float(fields[32]), + "position_fixed": fields[0] == "NARROW_INT", } @@ -105,7 +252,7 @@ class RtkSentence: def iter_g90_sentences(capture: CaptureFile) -> list[RtkSentence]: - """Parse GGA/GNHPR lines; host time comes from the containing serial chunk.""" + """Parse native asynchronous GGA/GNHPR/BESTNAVA/PVTSLNA records.""" rows: list[RtkSentence] = [] for _segment_id, chunks in iter_contiguous_segments(capture.chunks): @@ -122,21 +269,27 @@ def iter_g90_sentences(capture: CaptureFile) -> list[RtkSentence]: if not raw_line: continue line = raw_line.decode("ascii", "replace") - if not (line.startswith("$GNGGA") or line.startswith("$GPGGA") or line.startswith("$GNHPR")): + parser = None + if line.startswith("$GNGGA") or line.startswith("$GPGGA"): + parser = parse_gga + elif line.startswith("$GNHPR"): + parser = parse_gnhpr + elif line.startswith("#BESTNAVA"): + parser = parse_bestnava + elif line.startswith("#PVTSLNA"): + parser = parse_pvtslna + if parser is None: continue ticks = _host_ticks_for_span(chunks, starts, end) try: - if line.startswith("$GNGGA") or line.startswith("$GPGGA"): - fields = parse_gga(line) - else: - fields = parse_gnhpr(line) + fields = parser(line) except ValueError: continue rows.append( RtkSentence( sentence_type=str(fields["type"]), receive_utc_ticks=int(ticks), - checksum_valid=nmea_checksum_valid(line), + checksum_valid=g90_checksum_valid(line), fields=fields, raw_line=line, ) diff --git a/tools/rscap_v2/hi13_imu.py b/tools/rscap_v2/hi13_imu.py index 82689a8..373d7c6 100644 --- a/tools/rscap_v2/hi13_imu.py +++ b/tools/rscap_v2/hi13_imu.py @@ -12,6 +12,7 @@ HI91 (preferred for calibration): from __future__ import annotations import struct +from dataclasses import dataclass import numpy as np @@ -22,6 +23,29 @@ G0 = 9.80665 DEG2RAD = np.pi / 180.0 +@dataclass(frozen=True) +class Hi13Sample: + """One CRC-valid native HI91 record. + + t_s/system_time_ms are sensor-owned. Host receive ticks are retained only + for clock diagnostics and cross-clock fitting. + """ + + t_s: float + system_time_ms: int + device_timestamp_us: int + host_receive_utc_ticks: int + gyro_rad_s: tuple[float, float, float] + accel_m_s2: tuple[float, float, float] + rpy_deg: tuple[float, float, float] + quaternion_wxyz: tuple[float, float, float, float] + mag_ut: tuple[float, float, float] + pps_sync_stamp_ms: int + temperature_c: int + air_pressure_pa: float + frame_tag: int = 0x91 + + def crc16_hi13(frame: bytes, payload_length: int) -> int: crc = 0 for value in frame[:4]: @@ -41,8 +65,8 @@ def _update_crc16(crc: int, value: int) -> int: return crc -def parse_hi91_frame(raw: bytes) -> tuple[tuple[float, float, float], tuple[float, float, float], int] | None: - """Return (gyro_rad_s, accel_m_s2, device_timestamp_ms) for a CRC-valid HI91 frame.""" +def parse_hi91_sample(raw: bytes, host_receive_utc_ticks: int = 0) -> Hi13Sample | None: + """Decode every calibration-relevant field from a CRC-valid HI91 frame.""" if len(raw) < 6 + 76: return None @@ -54,12 +78,34 @@ def parse_hi91_frame(raw: bytes) -> tuple[tuple[float, float, float], tuple[floa expected = raw[4] | (raw[5] << 8) if crc16_hi13(raw, payload_length) != expected: return None - device_ms = struct.unpack_from(" tuple[tuple[float, float, float], tuple[float, float, float], int] | None: + """Backward-compatible compact HI91 decoder.""" + + sample = parse_hi91_sample(raw) + if sample is None: + return None + return sample.gyro_rad_s, sample.accel_m_s2, sample.system_time_ms def iter_hi13_imu_samples( @@ -67,14 +113,14 @@ def iter_hi13_imu_samples( *, host_utc_ticks_min: int | None = None, host_utc_ticks_max: int | None = None, -) -> list[ImuSample]: +) -> list[Hi13Sample]: """Return CRC-valid HI91 samples sorted by device timestamp. Streams chunk-by-chunk (no giant join) and can skip whole chunks outside the host UTC receive window before parsing. """ - samples: list[ImuSample] = [] + samples: list[Hi13Sample] = [] carry = b"" for chunk in capture.chunks: if host_utc_ticks_min is not None and chunk.receive_utc_ticks < host_utc_ticks_min: @@ -102,25 +148,16 @@ def iter_hi13_imu_samples( if end > len(stream): carry = stream[sync:] break - parsed = parse_hi91_frame(stream[sync:end]) + host_ticks = chunk.receive_utc_ticks + parsed = parse_hi91_sample(stream[sync:end], host_ticks) cursor = end if parsed is None: continue - gyro, accel, device_ms = parsed - host_ticks = chunk.receive_utc_ticks if host_utc_ticks_min is not None and host_ticks < host_utc_ticks_min: continue if host_utc_ticks_max is not None and host_ticks > host_utc_ticks_max: continue - samples.append( - ImuSample( - t_s=float(device_ms) * 1e-3, - gyro_rad_s=gyro, - accel_m_s2=accel, - host_receive_utc_ticks=host_ticks, - device_timestamp_us=int(device_ms) * 1000, - ) - ) + samples.append(parsed) else: carry = b"" samples.sort(key=lambda sample: (sample.t_s, sample.device_timestamp_us)) @@ -129,8 +166,10 @@ def iter_hi13_imu_samples( __all__ = [ "ImuSample", + "Hi13Sample", "crc16_hi13", "iter_hi13_imu_samples", "parse_hi91_frame", + "parse_hi91_sample", "samples_to_arrays", ] diff --git a/tools/run_rtk_imu_calibration.py b/tools/run_rtk_imu_calibration.py index d382013..163a1e7 100644 --- a/tools/run_rtk_imu_calibration.py +++ b/tools/run_rtk_imu_calibration.py @@ -12,7 +12,13 @@ ROOT = Path(__file__).resolve().parents[1] if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT)) -from imu_lidar.rtk_imu_replay import load_inventory, load_sessions, run_calibration +from imu_lidar.rtk_imu_replay import ( + DEFAULT_RTK_FRAME_DEFINITION, + DEFAULT_RTK_REFERENCE_POINT, + load_inventory, + load_sessions, + run_calibration, +) def main(argv: list[str] | None = None) -> int: @@ -33,8 +39,8 @@ def main(argv: list[str] | None = None) -> int: parser.add_argument("--no-loo", action="store_true") parser.add_argument("--knot-step-s", type=float, default=2.0) parser.add_argument("--per-batch", action="store_true") - parser.add_argument("--rtk-frame-definition", default='') - parser.add_argument("--rtk-reference-point", default='') + parser.add_argument("--rtk-frame-definition", default=DEFAULT_RTK_FRAME_DEFINITION) + parser.add_argument("--rtk-reference-point", default=DEFAULT_RTK_REFERENCE_POINT) args = parser.parse_args(argv) entries = load_inventory(args.inventory) diff --git a/tools/run_rtk_imu_engineering_6dof.py b/tools/run_rtk_imu_engineering_6dof.py new file mode 100644 index 0000000..d2bb960 --- /dev/null +++ b/tools/run_rtk_imu_engineering_6dof.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +"""Run the conditional engineering RTK--IMU 6DoF lever-arm branch.""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +from dataclasses import asdict +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_engineering import ( + DEFAULT_MANUAL_L_I_M, + DEFAULT_MANUAL_L_I_COVARIANCE_M2, + solve_engineering_6dof, +) +from imu_lidar.rtk_imu_multisource import load_unified_sessions + + +def _jsonable(value): + if isinstance(value, np.ndarray): + return _jsonable(value.tolist()) + if isinstance(value, np.generic): + return _jsonable(value.item()) + if isinstance(value, float): + return value if math.isfinite(value) else None + if hasattr(value, "__dataclass_fields__"): + return {key: _jsonable(item) for key, item in asdict(value).items()} + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_jsonable(item) for item in value] + return value + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument( + "--session", action="append", required=True, + help="Dynamic/static sessions intentionally admitted to this engineering run.", + ) + parser.add_argument( + "--rotation-rpy-deg", nargs=3, type=float, + default=[0.4543066225, -0.0026392019, 0.0122384129], + ) + parser.add_argument( + "--manual-l-i-m", nargs=3, type=float, default=DEFAULT_MANUAL_L_I_M.tolist(), + help="Mechanical ANT1 phase-centre lever arm p_ANT1^I in metres.", + ) + prior_group = parser.add_mutually_exclusive_group() + prior_group.add_argument( + "--manual-l-i-std-m", nargs=3, type=float, + default=np.sqrt(np.diag(DEFAULT_MANUAL_L_I_COVARIANCE_M2)).tolist(), + help="Mechanical 1-sigma prior standard deviation for lx, ly, lz in metres.", + ) + prior_group.add_argument( + "--manual-l-i-covariance-m2", nargs=9, type=float, + help="Row-major 3x3 mechanical prior covariance in m^2.", + ) + parser.add_argument( + "--no-manual-prior", action="store_true", + help="Run a free solve only; retain the mechanical reference in no optimisation factor.", + ) + parser.add_argument( + "--segment-id", action="append", + help="Strict-continuity segment id from the excitation audit; may be repeated.", + ) + parser.add_argument("--sample-period-s", type=float, default=0.5) + parser.add_argument("--skip-loo", action="store_true") + parser.add_argument("--run-bootstrap", action="store_true") + parser.add_argument("--bootstrap-repetitions", type=int, default=40) + parser.add_argument("--bootstrap-seed", type=int, default=0) + parser.add_argument("--run-rotation-sensitivity", action="store_true") + args = parser.parse_args(argv) + + manual_l_i_m = None if args.no_manual_prior else args.manual_l_i_m + prior_covariance = None + if not args.no_manual_prior: + if args.manual_l_i_std_m is not None: + prior_covariance = np.diag(np.square(args.manual_l_i_std_m)) + elif args.manual_l_i_covariance_m2 is not None: + prior_covariance = np.asarray(args.manual_l_i_covariance_m2, dtype=float).reshape(3, 3) + else: + parser.error("mechanical prior requires --manual-l-i-std-m or --manual-l-i-covariance-m2") + + sessions = load_unified_sessions(args.manifest, selected_session_ids=set(args.session)) + rotation = Rotation.from_euler("xyz", args.rotation_rpy_deg, degrees=True).as_matrix() + result = solve_engineering_6dof( + sessions, + R_RTK_IMU=rotation, + manual_l_I_m=manual_l_i_m, + manual_l_I_covariance_m2=prior_covariance, + sample_period_s=args.sample_period_s, + run_loo=not args.skip_loo, + run_bootstrap=args.run_bootstrap, + bootstrap_repetitions=args.bootstrap_repetitions, + bootstrap_seed=args.bootstrap_seed, + run_rotation_sensitivity=args.run_rotation_sensitivity, + selected_segment_ids=None if args.segment_id is None else set(args.segment_id), + ) + payload = { + "data_only_6dof_accepted": False, + "data_only_translation_accepted": result.data_only_translation_accepted, + "engineering_6dof": _jsonable(result), + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8" + ) + print(json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/tools/run_rtk_imu_engineering_free_base.py b/tools/run_rtk_imu_engineering_free_base.py new file mode 100644 index 0000000..e1bd4fa --- /dev/null +++ b/tools/run_rtk_imu_engineering_free_base.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +"""Run one prior-free engineering base fit on high-excitation qualified segments. + +This diagnostic never adds a mechanical lever factor, never profiles the +mechanical reference, and never runs LOO/bootstrap/rotation sensitivity. +""" +from __future__ import annotations + +import argparse +import json +import math +import sys +from dataclasses import asdict +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_engineering import ( + MIN_SEGMENT_DURATION_S, + MIN_SEGMENT_NODE_COUNT, + NUISANCE_DOF_PER_SEGMENT, + _Segment, + _fit_segments, + _fit_summary, + _height_reference, + _initial_parameters, + _marginal_lever_information, + _nodes, + _residual, + _segment_residual_size, + _world_rtk, +) +from imu_lidar.imu_preintegration import preintegrate_imu +from imu_lidar.rtk_imu_multisource import load_unified_sessions + +MANUAL_REFERENCE_L_I_M = np.array([-0.45072, -0.25682, 0.73208], dtype=float) + + +def _jsonable(value): + if isinstance(value, np.ndarray): + return _jsonable(value.tolist()) + if isinstance(value, np.generic): + return _jsonable(value.item()) + if isinstance(value, float): + return value if math.isfinite(value) else None + if hasattr(value, "__dataclass_fields__"): + return {key: _jsonable(item) for key, item in asdict(value).items()} + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (tuple, list)): + return [_jsonable(item) for item in value] + return value + + +def _r0_segment_candidates(sessions, period_s: float) -> list[tuple[object, tuple, str]]: + """Split R0 first; defer expensive preintegration until a candidate is selected.""" + reference = _height_reference(sessions) + if reference is None: + return [] + candidates: list[tuple[object, tuple, str]] = [] + for session in sessions: + nodes = _nodes(session, reference, period_s) + start = 0 + qualifying_index = 0 + for end in range(1, len(nodes) + 1): + if end != len(nodes) and nodes[end].continuity_id == nodes[end - 1].continuity_id: + continue + run = tuple(nodes[start:end]) + start = end + if len(run) < MIN_SEGMENT_NODE_COUNT or run[-1].t_s - run[0].t_s < MIN_SEGMENT_DURATION_S: + continue + if not any(node.hpr_factor_valid for node in run): + continue + candidates.append((session, run, f"{session.session_id}:{qualifying_index:02d}")) + qualifying_index += 1 + return candidates + + +def _build_segment(session, run: tuple, segment_id: str) -> _Segment | None: + pre = tuple(preintegrate_imu( + session.imu.t_s, session.imu.gyro_rad_s, session.imu.acc_m_s2, left.t_s, right.t_s, + ) for left, right in zip(run[:-1], run[1:])) + if any(item.duration_s <= 0.0 for item in pre): + return None + initial_hpr = next((node for node in run if node.hpr_factor_valid), None) + if initial_hpr is None: + return None + return _Segment(segment_id, session.session_id, run, pre, _world_rtk(initial_hpr.baseline_enu)) + +def _gyro_abs_rotation_deg(session, segment) -> np.ndarray: + start_s, end_s = segment.nodes[0].t_s, segment.nodes[-1].t_s + mask = (session.imu.t_s >= start_s) & (session.imu.t_s <= end_s) + t_s = session.imu.t_s[mask] + gyro = session.imu.gyro_rad_s[mask] + if t_s.size < 2: + return np.zeros(3) + return np.degrees(np.trapezoid(np.abs(gyro), t_s, axis=0)) + + +def _category_score(category: str, gyro_abs_deg: np.ndarray) -> float: + if category in {"circle", "left_right"}: + return float(gyro_abs_deg[2]) + return float(np.hypot(gyro_abs_deg[0], gyro_abs_deg[1])) + + +def _marginal_for_segment_indices(jacobian: np.ndarray, residual: np.ndarray, + segments, indices: list[int]) -> tuple[np.ndarray, np.ndarray, float, int, np.ndarray]: + row = 0 + rows: list[np.ndarray] = [] + for index, segment in enumerate(segments): + count = _segment_residual_size(segment) + if index in indices: + rows.append(np.arange(row, row + count)) + row += count + selected_rows = np.concatenate(rows) if rows else np.empty(0, dtype=int) + columns = [0, 1, 2] + for index in indices: + offset = 3 + NUISANCE_DOF_PER_SEGMENT * index + columns.extend(range(offset, offset + NUISANCE_DOF_PER_SEGMENT)) + marginal, singular, condition, rank, weakest, _ = _marginal_lever_information( + jacobian[np.ix_(selected_rows, columns)], residual[selected_rows] + ) + return marginal, singular, condition, rank, weakest + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--circle-session", required=True) + parser.add_argument("--left-right-session", required=True) + parser.add_argument("--slope-session", required=True) + parser.add_argument("--top-per-category", type=int, default=10) + parser.add_argument("--sample-period-s", type=float, default=1.0) + parser.add_argument("--rotation-rpy-deg", nargs=3, type=float, + default=[0.4543066225, -0.0026392019, 0.0122384129]) + args = parser.parse_args() + category_by_session = { + args.circle_session: "circle", + args.left_right_session: "left_right", + args.slope_session: "slope", + } + sessions = load_unified_sessions(args.manifest, selected_session_ids=set(category_by_session)) + candidates: dict[str, list[tuple[float, object, tuple, str, np.ndarray]]] = { + key: [] for key in category_by_session.values() + } + all_candidates = _r0_segment_candidates(sessions, args.sample_period_s) + for session, run, segment_id in all_candidates: + category = category_by_session[session.session_id] + gyro_abs = _gyro_abs_rotation_deg(session, type("Run", (), {"nodes": run})()) + candidates[category].append((_category_score(category, gyro_abs), session, run, segment_id, gyro_abs)) + selected: list[object] = [] + selected_entries: list[dict[str, object]] = [] + category_indices: dict[str, list[int]] = {} + for category, entries in candidates.items(): + selected_before = len(selected) + for score, session, run, segment_id, gyro_abs in sorted(entries, key=lambda item: item[0], reverse=True): + segment = _build_segment(session, run, segment_id) + if segment is None: + continue + selected.append(segment) + selected_entries.append({ + "category": category, "segment_id": segment.segment_id, + "session_id": segment.session_id, "duration_s": segment.nodes[-1].t_s - segment.nodes[0].t_s, + "node_count": len(segment.nodes), "excitation_score_deg": score, + "cumulative_absolute_gyro_rotation_xyz_deg": gyro_abs, + }) + if len(selected) - selected_before >= args.top_per_category: + break + category_indices[category] = list(range(selected_before, len(selected))) + rotation = Rotation.from_euler("xyz", args.rotation_rpy_deg, degrees=True).as_matrix() + initial_parameters = _initial_parameters(selected) + initial_residual = _residual(initial_parameters, selected, rotation) + fit, residual, detail = _fit_segments(selected, rotation) + summary = _fit_summary(fit, residual, detail) + if fit is None or summary is None: + raise RuntimeError("no selected qualified segment could be fit") + contributions: dict[str, dict[str, object]] = {} + contribution_sum = np.zeros((3, 3)) + for category, indices in category_indices.items(): + marginal, singular, condition, rank, weakest = _marginal_for_segment_indices( + fit.jac, residual, selected, indices + ) + contribution_sum += marginal + contributions[category] = { + "selected_segment_count": len(indices), + "lever_marginal_information": marginal, + "lever_information_singular_values": singular, + "condition_number": condition, + "precision_rank": rank, + "weakest_direction_I": weakest, + "axis_information_diagonal_I": np.diag(marginal), + } + total_diag = np.diag(summary.lever_marginal_information) + for category, item in contributions.items(): + item["axis_information_fraction_of_total_I"] = np.divide( + item["axis_information_diagonal_I"], total_diag, + out=np.full(3, np.nan), where=np.abs(total_diag) > 1e-12, + ) + payload = { + 'solver_diagnostics': { + 'initial_cost': 0.5 * float(np.dot(initial_residual, initial_residual)), + 'final_cost': 0.5 * float(np.dot(residual, residual)), + 'cost_reduction': 0.5 * float( + np.dot(initial_residual, initial_residual) - np.dot(residual, residual) + ), + 'cost_definition': '0.5 * unmodified residual squared norm, comparable initial/final', + 'scipy_final_huber_cost': float(fit.cost), + 'nfev': int(fit.nfev), 'optimality': float(fit.optimality), + 'gradient_norm': float(np.linalg.norm(fit.grad)), + 'initial_l_I_m': initial_parameters[:3], 'final_l_I_m': fit.x[:3], + 'l_step_norm_m': float(np.linalg.norm(fit.x[:3] - initial_parameters[:3])), + }, + "scope": "prior-free free base fit only; no mechanical factor/LOO/bootstrap/rotation sensitivity", + "rotation_source": "R2G_gravity_level_prior", + "translation_conditional_on_rotation": True, + "manual_reference_comparison_only": { + "manual_l_I_m": MANUAL_REFERENCE_L_I_M, + "free_minus_manual_l_I_m": summary.l_I_m - MANUAL_REFERENCE_L_I_M, + "euclidean_delta_m": float(np.linalg.norm(summary.l_I_m - MANUAL_REFERENCE_L_I_M)), + }, + "sample_period_s": args.sample_period_s, + "all_qualified_segment_count": len(all_candidates), + "selected_segment_count": len(selected), + "selected_segments": selected_entries, + "free_solution": _jsonable(summary), + "motion_category_information_contributions": _jsonable(contributions), + "axis_information_total_I": np.diag(summary.lever_marginal_information), + "marginal_additivity_error_fro": float(np.linalg.norm( + contribution_sum - summary.lever_marginal_information, ord="fro" + )), + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload), ensure_ascii=False, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(json.dumps({ + "selected_segment_count": len(selected), + "free_l_I_m": _jsonable(summary.l_I_m), + "free_l_I_std_m": _jsonable(summary.l_I_std_m), + "lever_information_singular_values": _jsonable(summary.lever_information_singular_values), + "condition_number": summary.lever_information_condition_number, + "precision_rank": summary.lever_precision_rank, + "bestnava_xyz_vector_rms_p95_m": [summary.bestnava_xyz_residual.vector_rms, summary.bestnava_xyz_residual.vector_p95], + "doppler_vector_rms_p95_m_s": [summary.doppler_velocity_residual.vector_rms, summary.doppler_velocity_residual.vector_p95], + }, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_mechanical_prior_branch.py b/tools/run_rtk_imu_mechanical_prior_branch.py new file mode 100644 index 0000000..2a292c4 --- /dev/null +++ b/tools/run_rtk_imu_mechanical_prior_branch.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +'''Run fixed-mechanical and soft-prior solutions on immutable 47-window baseline.''' +from __future__ import annotations +import argparse,hashlib,json,sys +from concurrent.futures import ThreadPoolExecutor +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import ( + NODE_DOF,build_problem,fit_states_at_fixed_lever,solve_free_lever_many, + summarize_fixed_state_values) +from tools.audit_rtk_imu_factor_consistency import MECHANICAL_L_I_M,_jsonable +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments + +MANUAL_STD_M=np.array([.02,.02,.03]) +MANUAL_COVARIANCE_M2=np.diag(MANUAL_STD_M**2) + + +def _factor_delta(candidate,baseline): + output={} + for key in ('best_position','doppler','hpr','imu_preintegration'): + left,right=candidate['residual_by_factor'][key],baseline['residual_by_factor'][key] + output[key]={'rms_delta':left['rms']-right['rms'], + 'p95_abs_delta':left['p95_abs']-right['p95_abs'], + 'nis_per_dof_delta':left['chi_square_per_dof']-right['chi_square_per_dof']} + return output + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--selection',type=Path,required=True) + parser.add_argument('--free-baseline',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--state-output',type=Path) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--fixed-max-nfev',type=int,default=120) + parser.add_argument('--prior-max-nfev',type=int,default=120) + parser.add_argument('--workers',type=int,default=4) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + args=parser.parse_args() + selection=json.loads(args.selection.read_text(encoding='utf-8')) + baseline=json.loads(args.free_baseline.read_text(encoding='utf-8')) + if len(selection['selected_windows'])!=47: + raise RuntimeError('engineering branch requires immutable 47-window selection') + ids={item['session_id'] for item in selection['selected_windows']} + sessions=load_unified_sessions(args.manifest,selected_session_ids=ids) + reference=_height_reference(sessions) + segments=_restore_segments(sessions,reference,selection['selected_windows'], + args.sample_period_s) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + mechanical=MECHANICAL_L_I_M.copy() + problems=[build_problem(segment,rotation,mechanical,args.hpr_direct_sigma_rad) + for segment in segments] + with ThreadPoolExecutor(max_workers=args.workers) as executor: + fitted=list(executor.map(lambda problem:fit_states_at_fixed_lever( + problem,mechanical,args.fixed_max_nfev),problems)) + fixed_states=[item[0] for item in fitted] + fixed_optimizer=[item[1] for item in fitted] + fixed_summary=summarize_fixed_state_values(problems,fixed_states,mechanical) + prior_result,prior_states=solve_free_lever_many( + problems,mechanical,args.prior_max_nfev,fixed_states, + lever_prior_mean_m=mechanical, + lever_prior_covariance_m2=MANUAL_COVARIANCE_M2, + return_state_values=True) + prior_result=asdict(prior_result) + prior_data=summarize_fixed_state_values( + problems,prior_states,np.asarray(prior_result['final_l_I_m'])) + bias_values={} + for segment,state in zip(segments,prior_states): + states=np.asarray(state).reshape(-1,NODE_DOF) + values=bias_values.setdefault(segment.session_id,{'bg':[],'ba':[]}) + values['bg'].extend(states[:,9:12]) + values['ba'].extend(states[:,12:15]) + calibration_bias={session_id:{ + 'gyro_bias_rad_s':np.median(values['bg'],axis=0), + 'accel_bias_m_s2':np.median(values['ba'],axis=0), + 'node_count':len(values['bg'])} + for session_id,values in bias_values.items()} + free=baseline['solutions']['mechanical'] + posterior_cov=np.asarray(prior_result['lever_covariance_m2']) + variance_ratio=np.diag(posterior_cov)/np.diag(MANUAL_COVARIANCE_M2) + prior_pull=(np.asarray(prior_result['final_l_I_m'])-mechanical)/MANUAL_STD_M + translation_refined=bool(np.all(variance_ratio<=.90)) + comparisons={'fixed_vs_free':{ + 'cost_delta':fixed_summary['cost']-free['final_cost'], + 'relative_cost_delta':fixed_summary['cost']/free['final_cost']-1., + 'factor_residual_delta':_factor_delta(fixed_summary,free)}, + 'prior_data_vs_free':{ + 'cost_delta':prior_data['cost']-free['final_cost'], + 'relative_cost_delta':prior_data['cost']/free['final_cost']-1., + 'factor_residual_delta':_factor_delta(prior_data,free)}} + baseline_hash=hashlib.sha256(args.free_baseline.read_bytes()).hexdigest() + payload={'scope':'47-window mechanical-prior engineering branch', + 'data_only_translation_accepted':False, + 'immutable_free_baseline':{'path':str(args.free_baseline), + 'sha256':baseline_hash,'solution':free}, + 'selected_window_count':len(segments),'selection_path':str(args.selection), + 'manual_l_I_m':mechanical,'manual_l_I_std_m':MANUAL_STD_M, + 'manual_l_I_covariance_m2':MANUAL_COVARIANCE_M2, + 'fixed_mechanical_solution':{'l_I_m':mechanical, + 'window_optimizer':fixed_optimizer,'data_summary':fixed_summary}, + 'prior_constrained_solution':{'result':prior_result, + 'data_only_summary_excluding_prior_factor':prior_data, + 'posterior_covariance_m2':posterior_cov, + 'posterior_prior_variance_ratio':variance_ratio, + 'prior_pull_sigma':prior_pull, + 'calibration_only_frozen_bias_by_session':calibration_bias}, + 'comparisons':comparisons, + 'translation_refinement_gate':{ + 'required_max_axis_variance_ratio':.90, + 'passed':translation_refined}, + 'translation_refined_by_data':translation_refined, + 'heldout_validation_called':False, + 'engineering_translation_accepted':False} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + if args.state_output is not None: + sizes=np.asarray([len(value) for value in prior_states],dtype=int) + args.state_output.parent.mkdir(parents=True,exist_ok=True) + np.savez_compressed(args.state_output, + states=np.concatenate(prior_states),offsets=np.cumsum(np.r_[0,sizes]), + candidate_ids=np.asarray( + [item['candidate_id'] for item in selection['selected_windows']])) + print(json.dumps(_jsonable({'fixed':fixed_summary, + 'prior_l_I_m':prior_result['final_l_I_m'], + 'prior_data_cost':prior_data['cost'],'prior_map_cost':prior_result['final_cost'], + 'variance_ratio':variance_ratio,'prior_pull_sigma':prior_pull, + 'translation_refined_by_data':translation_refined, + 'fixed_optimizer_failed_count':sum(not item['success'] for item in fixed_optimizer)}), + ensure_ascii=False,indent=2)) + return 0 + + +if __name__=='__main__': + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_mechanical_prior_heldout.py b/tools/run_rtk_imu_mechanical_prior_heldout.py new file mode 100644 index 0000000..4c7ce15 --- /dev/null +++ b/tools/run_rtk_imu_mechanical_prior_heldout.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +'''Validate an engineering lever on disjoint held-out windows.''' +from __future__ import annotations +import argparse,json,sys +from concurrent.futures import ProcessPoolExecutor,as_completed +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.geometry import make_transform +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem,fit_states_at_fixed_lever,residual +from tools.audit_rtk_imu_factor_consistency import _jsonable +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments + +FACTORS=('best_position','doppler','hpr','imu_preintegration') +PHYSICAL=('best_position_physical','doppler_physical','hpr_physical') + +def _fit_checkpoint(task): + index,problem,lever,max_nfev,path_text=task + path=Path(path_text) + if path.exists(): + data=np.load(path,allow_pickle=False) + return index,data['state'],json.loads(str(data['optimizer'])) + state,meta=fit_states_at_fixed_lever(problem,lever,max_nfev) + np.savez_compressed(path,state=state,optimizer=json.dumps(meta)) + return index,state,meta + +def _stats(values,dof=None): + a=np.asarray(values,dtype=float).reshape(-1) + if not a.size: + return {'count':0,'dof':0,'rms':np.nan,'p50_abs':np.nan, + 'p95_abs':np.nan,'p99_abs':np.nan,'nis':np.nan, + 'chi_square_per_dof':np.nan} + dof=len(a) if dof is None else max(int(dof),1); nis=float(a@a) + return {'count':len(a),'dof':dof,'rms':float(np.sqrt(np.mean(a*a))), + 'p50_abs':float(np.percentile(np.abs(a),50)), + 'p95_abs':float(np.percentile(np.abs(a),95)), + 'p99_abs':float(np.percentile(np.abs(a),99)), + 'nis':nis,'chi_square_per_dof':nis/dof} + +def _vectors(values): + a=np.asarray(values,dtype=float).reshape(-1,3) + if not a.size: + return {'count':0,'axis_rms':[np.nan]*3,'axis_p95_abs':[np.nan]*3, + 'vector_rms':np.nan,'vector_p95':np.nan} + norm=np.linalg.norm(a,axis=1) + return {'count':len(a),'axis_rms':np.sqrt(np.mean(a*a,axis=0)), + 'axis_p95_abs':np.percentile(np.abs(a),95,axis=0), + 'vector_rms':float(np.sqrt(np.mean(norm*norm))), + 'vector_p95':float(np.percentile(norm,95))} + +def _summarize(records): + factor={key:[] for key in FACTORS}; physical={key:[] for key in PHYSICAL} + joined=[]; state_dimension=0; other_effective=0 + for record in records: + joined.extend(record['all']); state_dimension+=record['state_dimension'] + other_effective+=record['other_effective'] + for key in FACTORS: factor[key].extend(record['factor'].get(key,[])) + for key in PHYSICAL: physical[key].extend(record['physical'].get(key,[])) + effective=sum(2*len(v)//3 if k=='hpr' else len(v) + for k,v in factor.items())+other_effective + dof=max(effective-state_dimension,1); a=np.asarray(joined,dtype=float) + converged=sum(bool(x['optimizer']['success']) for x in records) + return {'window_count':len(records),'optimizer_converged_count':converged, + 'optimizer_converged_fraction':converged/max(len(records),1), + 'total_residual_dimension':len(a),'state_dimension':state_dimension, + 'statistical_dof':dof,'total_nis':float(a@a), + 'global_chi_square_per_dof':float((a@a)/dof), + 'residual_by_factor':{key:_stats(value,2*len(value)//3 if key=='hpr' else None) + for key,value in factor.items()}, + 'best_position_physical_m':_vectors(physical['best_position_physical']), + 'doppler_physical_m_s':_vectors(physical['doppler_physical']), + 'hpr_physical_rad':_vectors(physical['hpr_physical'])} + +def _gate(summary): + factor=summary['residual_by_factor'] + checks={'optimizer_converged_fraction_ge_0p90': + summary['optimizer_converged_fraction']>=.90, + 'global_chi_square_per_dof_in_0p25_4': + .25<=summary['global_chi_square_per_dof']<=4., + 'best_vector_p95_le_0p20_m': + summary['best_position_physical_m']['vector_p95']<=.20, + 'doppler_vector_p95_le_0p50_m_s': + summary['doppler_physical_m_s']['vector_p95']<=.50, + 'hpr_normalized_p95_le_4':factor['hpr']['p95_abs']<=4., + 'preintegration_normalized_p95_le_3': + factor['imu_preintegration']['p95_abs']<=3.} + return {'uses_existing_P0p5_physical_and_statistical_health_gates':True, + 'checks':checks,'passed':bool(all(checks.values()))} + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--manifest',type=Path,required=True) + p.add_argument('--calibration-selection',type=Path,required=True) + p.add_argument('--all-selection',type=Path,required=True) + p.add_argument('--engineering-result',type=Path,required=True) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--checkpoint-dir',type=Path,required=True) + p.add_argument('--sample-period-s',type=float,default=1.) + p.add_argument('--max-nfev',type=int,default=120) + p.add_argument('--workers',type=int,default=4) + p.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + p.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + p.add_argument('--circle-session',default='0808_20260808_092827') + p.add_argument('--left-right-session',default='0808_20260808_082148') + p.add_argument('--slope-session',default='0815_20260812_123424') + args=p.parse_args() + engineering=json.loads(args.engineering_result.read_text(encoding='utf-8')) + calibration=json.loads(args.calibration_selection.read_text(encoding='utf-8')) + all_windows=json.loads(args.all_selection.read_text(encoding='utf-8')) + calibration_ids={x['candidate_id'] for x in calibration['selected_windows']} + heldout=[x for x in all_windows['selected_windows'] + if x['candidate_id'] not in calibration_ids] + if len(calibration_ids)!=47 or len(heldout)!=267: + raise RuntimeError(f'expected 47+267 windows, got {len(calibration_ids)}+{len(heldout)}') + shared=sum(x.get('shared_sample_count_with_previous',{}).get(k,0) + for x in all_windows['selected_windows'] for k in ('imu','gnss','hpr')) + if shared: raise RuntimeError(f'selection contains {shared} shared samples') + lever=np.asarray(engineering['prior_constrained_solution']['result']['final_l_I_m']) + sessions=load_unified_sessions( + args.manifest,selected_session_ids={x['session_id'] for x in heldout}) + reference=_height_reference(sessions) + segments=_restore_segments(sessions,reference,heldout,args.sample_period_s) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + problems=[build_problem(x,rotation,lever,args.hpr_direct_sigma_rad) for x in segments] + args.checkpoint_dir.mkdir(parents=True,exist_ok=True) + fitted=[None]*len(problems) + tasks=[(i,problem,lever,args.max_nfev, + str(args.checkpoint_dir/f'{i:04d}.npz')) + for i,problem in enumerate(problems)] + with ProcessPoolExecutor(max_workers=args.workers) as executor: + futures=[executor.submit(_fit_checkpoint,task) for task in tasks] + completed=0 + for future in as_completed(futures): + index,state,meta=future.result(); fitted[index]=(state,meta); completed+=1 + if completed%10==0: print(f'held-out checkpoint {completed}/{len(problems)}',flush=True) + mapping={args.circle_session:'circle',args.left_right_session:'left_right', + args.slope_session:'slope'} + records=[] + for item,problem,fit in zip(heldout,problems,fitted): + state,optimizer=fit; detail={} + all_residual=residual(problem,state,details=detail,lever_override=lever) + known=sum(len(detail.get(k,[])) for k in FACTORS) + records.append({'candidate_id':item['candidate_id'], + 'session_id':item['session_id'], + 'motion_class':mapping.get(item['session_id'],'other_recovered_dynamic'), + 'start_s':item['start_s'],'end_s':item['end_s'], + 'state_dimension':len(state),'optimizer':optimizer, + 'all':all_residual.tolist(),'other_effective':len(all_residual)-known, + 'factor':{k:detail.get(k,[]) for k in FACTORS}, + 'physical':{k:detail.get(k,[]) for k in PHYSICAL}}) + overall=_summarize(records) + by_session={key:_summarize([x for x in records if x['session_id']==key]) + for key in sorted({x['session_id'] for x in records})} + by_motion={key:_summarize([x for x in records if x['motion_class']==key]) + for key in sorted({x['motion_class'] for x in records})} + overall_gate=_gate(overall) + session_gates={k:_gate(v) for k,v in by_session.items()} + motion_gates={k:_gate(v) for k,v in by_motion.items()} + heldout_passed=bool(overall_gate['passed'] and + all(x['passed'] for x in session_gates.values()) and + all(x['passed'] for x in motion_gates.values())) + comparisons=engineering['comparisons'] + def nonconflicting(name): + c=comparisons[name] + return (c['relative_cost_delta']<=.05 and + all(x['p95_abs_delta']<=.25 for x in c['factor_residual_delta'].values())) + fit_nonconflict=nonconflicting('fixed_vs_free') and nonconflicting('prior_data_vs_free') + T_rtk_imu=make_transform(-rotation@lever,rotation) + T_imu_rtk=np.linalg.inv(T_rtk_imu) + accepted=bool(fit_nonconflict and heldout_passed) + payload={**engineering, + 'scope':'47-window mechanical-prior engineering branch + disjoint held-out validation', + 'heldout_validation_called':True, + 'engineering_lever_source':'prior_constrained_solution', + 'engineering_l_I_m':lever,'calibration_heldout_overlap_count':0, + 'heldout_window_count':len(heldout), + 'heldout_validation':{'lever_reoptimized':False, + 'nuisance_states_optimized_per_window':True, + 'motion_class_mapping':mapping, + 'other_sessions_class':'other_recovered_dynamic', + 'overall':overall,'by_session':by_session,'by_motion_class':by_motion, + 'overall_gate':overall_gate,'session_gates':session_gates, + 'motion_class_gates':motion_gates,'passed':heldout_passed}, + 'calibration_fit_nonconflict_gate':{'relative_cost_increase_max':.05, + 'per_factor_normalized_p95_increase_max':.25, + 'fixed_passed':nonconflicting('fixed_vs_free'), + 'prior_passed':nonconflicting('prior_data_vs_free'), + 'passed':fit_nonconflict}, + 'engineering_translation_accepted':accepted, + 'data_only_translation_accepted':False, + 'result_nature':'mechanical lever + dynamic-data consistency validation; not data-only translation calibration', + 'rotation_source':'R2G_gravity_level_prior', + 'translation_conditional_on_rotation':True, + 'candidate_T_RTK_IMU':T_rtk_imu,'candidate_T_IMU_RTK':T_imu_rtk, + 'T_RTK_IMU':T_rtk_imu if accepted else None, + 'T_IMU_RTK':T_imu_rtk if accepted else None, + 'transform_convention':('T_RTK_IMU maps IMU coordinates into ANT1 RTK frame; ' + 'l_I=p_ANT1^I; T_IMU_RTK translation equals l_I')} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'engineering_l_I_m':lever, + 'fit_nonconflict':fit_nonconflict,'heldout_overall':overall, + 'heldout_passed':heldout_passed, + 'engineering_translation_accepted':accepted}),ensure_ascii=False,indent=2)) + return 0 +if __name__=='__main__': raise SystemExit(main()) diff --git a/tools/run_rtk_imu_mechanical_prior_rotation_sensitivity.py b/tools/run_rtk_imu_mechanical_prior_rotation_sensitivity.py new file mode 100644 index 0000000..c365d1c --- /dev/null +++ b/tools/run_rtk_imu_mechanical_prior_rotation_sensitivity.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +'''Run 18 fixed-R2G perturbation prior-constrained node-graph solves.''' +from __future__ import annotations +import argparse,json,sys +from concurrent.futures import ProcessPoolExecutor,as_completed +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.geometry import make_transform +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import ( + build_problem,solve_free_lever_many,summarize_fixed_state_values) +from tools.audit_rtk_imu_factor_consistency import _jsonable +from tools.run_rtk_imu_node_graph_free_selected import _restore_segments + +_CONTEXT={} +MANUAL=np.array([-.45072,-.25682,.73208]) +STD=np.array([.02,.02,.03]) +COV=np.diag(STD**2) + +def _initialize_worker(segments,states,rpy,max_nfev,sigma,checkpoint_dir): + _CONTEXT.update(segments=segments,states=states,rpy=np.asarray(rpy), + max_nfev=max_nfev,sigma=sigma,checkpoint_dir=Path(checkpoint_dir)) + +def _solve(task): + axis,sign,angle=task + name=f'{axis}_{sign*angle:+.1f}deg' + path=_CONTEXT['checkpoint_dir']/f'{name}.json' + if path.exists(): return json.loads(path.read_text(encoding='utf-8')) + vector=np.zeros(3); vector['xyz'.index(axis)]=np.deg2rad(sign*angle) + nominal_R=Rotation.from_euler('xyz',_CONTEXT['rpy'],degrees=True).as_matrix() + perturbed_R=Rotation.from_rotvec(vector).as_matrix()@nominal_R + problems=[build_problem(segment,perturbed_R,MANUAL,_CONTEXT['sigma']) + for segment in _CONTEXT['segments']] + result,states=solve_free_lever_many(problems,MANUAL,_CONTEXT['max_nfev'], + _CONTEXT['states'],lever_prior_mean_m=MANUAL, + lever_prior_covariance_m2=COV,return_state_values=True) + result=asdict(result); lever=np.asarray(result['final_l_I_m']) + data=summarize_fixed_state_values(problems,states,lever) + transform=make_transform(-perturbed_R@lever,perturbed_R) + payload={'name':name,'axis':axis,'signed_angle_deg':sign*angle, + 'success':result['success'],'message':result['message'],'nfev':result['nfev'], + 'final_l_I_m':lever,'delta_l_from_nominal_m':None, + 'map_cost':result['final_cost'],'data_cost':data['cost'], + 'global_chi_square_per_dof':data['chi_square_per_dof'], + 'residual_by_factor':data['residual_by_factor'], + 'physical_residual':{ + 'BEST_position_m':data['best_position_physical_m'], + 'Doppler_m_s':data['doppler_physical_m_s'], + 'HPR_rad':data['hpr_physical_rad']}, + 'posterior_covariance_m2':result['lever_covariance_m2'], + 'posterior_std_m':np.sqrt(np.diag(result['lever_covariance_m2'])), + 'prior_pull_sigma':(lever-MANUAL)/STD, + 'T_RTK_IMU':transform} + path.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + return _jsonable(payload) + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--manifest',type=Path,required=True) + p.add_argument('--selection',type=Path,required=True) + p.add_argument('--engineering-result',type=Path,required=True) + p.add_argument('--nominal-states',type=Path,required=True) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--checkpoint-dir',type=Path,required=True) + p.add_argument('--workers',type=int,default=3) + p.add_argument('--max-nfev',type=int,default=120) + p.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + p.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + args=p.parse_args() + engineering=json.loads(args.engineering_result.read_text(encoding='utf-8')) + selection=json.loads(args.selection.read_text(encoding='utf-8')) + if len(selection['selected_windows'])!=47: + raise RuntimeError('sensitivity requires immutable 47-window selection') + sessions=load_unified_sessions(args.manifest, + selected_session_ids={x['session_id'] for x in selection['selected_windows']}) + segments=_restore_segments(sessions,_height_reference(sessions), + selection['selected_windows'],1.) + saved=np.load(args.nominal_states,allow_pickle=False) + offsets=saved['offsets']; flat=saved['states'] + states=[flat[offsets[i]:offsets[i+1]] for i in range(len(offsets)-1)] + if list(saved['candidate_ids'])!=[x['candidate_id'] for x in selection['selected_windows']]: + raise RuntimeError('nominal state checkpoint does not match selection order') + args.checkpoint_dir.mkdir(parents=True,exist_ok=True) + tasks=[(axis,sign,angle) for axis in 'xyz' + for angle in (.1,.3,.5) for sign in (-1.,1.)] + results=[] + with ProcessPoolExecutor(max_workers=args.workers,initializer=_initialize_worker, + initargs=(segments,states,args.rotation_rpy_deg,args.max_nfev, + args.hpr_direct_sigma_rad,str(args.checkpoint_dir))) as executor: + futures=[executor.submit(_solve,task) for task in tasks] + for future in as_completed(futures): + result=future.result(); results.append(result) + print('completed',result['name'],flush=True) + results.sort(key=lambda x:('xyz'.index(x['axis']),x['signed_angle_deg'])) + nominal=engineering['prior_constrained_solution'] + nominal_l=np.asarray(nominal['result']['final_l_I_m']) + nominal_data=nominal['data_only_summary_excluding_prior_factor'] + nominal_R=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + nominal_T=make_transform(-nominal_R@nominal_l,nominal_R) + for result in results: + result['delta_l_from_nominal_m']=np.asarray(result['final_l_I_m'])-nominal_l + result['delta_l_norm_m']=float(np.linalg.norm(result['delta_l_from_nominal_m'])) + result['transform_translation_delta_m']=( + np.asarray(result['T_RTK_IMU'])[:3,3]-nominal_T[:3,3]) + result['transform_translation_delta_norm_m']=float(np.linalg.norm( + result['transform_translation_delta_m'])) + result['transform_rotation_delta_deg']=abs(result['signed_angle_deg']) + result['relative_data_cost_delta']=result['data_cost']/nominal_data['cost']-1. + result['factor_p95_delta_sigma']={key: + result['residual_by_factor'][key]['p95_abs']- + nominal_data['residual_by_factor'][key]['p95_abs'] + for key in ('best_position','doppler','hpr','imu_preintegration')} + delta=np.asarray([x['delta_l_from_nominal_m'] for x in results]) + at_point3=[x for x in results if abs(x['signed_angle_deg'])==.3] + checks={'all_18_complete_and_converged': + len(results)==18 and all(x['success'] for x in results), + 'max_delta_norm_at_0p3deg_le_0p10m': + max(x['delta_l_norm_m'] for x in at_point3)<=.10, + 'max_delta_norm_all_le_0p15m': + max(x['delta_l_norm_m'] for x in results)<=.15, + 'relative_data_cost_increase_all_le_0p05': + max(x['relative_data_cost_delta'] for x in results)<=.05, + 'factor_normalized_p95_increase_all_le_0p5sigma': + max(v for x in results for v in x['factor_p95_delta_sigma'].values())<=.5} + passed=bool(all(checks.values())) + payload={'scope':'18 prior-constrained fixed-R2G perturbation solves', + 'data_only_free_called':False,'bootstrap_called':False,'loo_called':False, + 'parser_R0_covariance_modified':False,'calibration_window_count':47, + 'nominal_rotation_rpy_deg':args.rotation_rpy_deg, + 'nominal_l_I_m':nominal_l,'nominal_T_RTK_IMU':nominal_T, + 'perturbations':results,'summary':{ + 'max_abs_delta_l_xyz_m':np.max(np.abs(delta),axis=0), + 'max_delta_l_norm_m':float(np.max(np.linalg.norm(delta,axis=1))), + 'max_transform_translation_delta_norm_m':max( + x['transform_translation_delta_norm_m'] for x in results)}, + 'diagnostic_gate_thresholds_frozen_before_run':{ + 'max_delta_norm_at_0p3deg_m':.10,'max_delta_norm_all_m':.15, + 'relative_data_cost_increase':.05, + 'factor_normalized_p95_increase_sigma':.5}, + 'gate_checks':checks,'rotation_sensitivity_passed':passed} + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'summary':payload['summary'], + 'checks':checks,'rotation_sensitivity_passed':passed}),indent=2)) + return 0 +if __name__=='__main__': raise SystemExit(main()) diff --git a/tools/run_rtk_imu_multisource.py b/tools/run_rtk_imu_multisource.py new file mode 100644 index 0000000..0474acf --- /dev/null +++ b/tools/run_rtk_imu_multisource.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +"""Run R1b/R2V/R2G/R3 on unified native-time G90/HI13 exports.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_multisource import ( + load_unified_sessions, + result_to_jsonable, + solve_r3, +) + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--session", action="append") + parser.add_argument("--level-static", action="append", required=True) + args = parser.parse_args(argv) + selected = None if not args.session else set(args.session) | set(args.level_static) + sessions = load_unified_sessions(args.manifest, selected_session_ids=selected) + result = solve_r3(sessions, level_static_session_ids=set(args.level_static)) + payload = result_to_jsonable(result) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps(payload, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_node_graph_fixed_lever.py b/tools/run_rtk_imu_node_graph_fixed_lever.py new file mode 100644 index 0000000..0f3a143 --- /dev/null +++ b/tools/run_rtk_imu_node_graph_fixed_lever.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +'''Run one 10-20 s per-node state graph with a fixed mechanical lever.''' +from __future__ import annotations +import argparse, json, sys +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem, solve_fixed_lever +from tools.audit_rtk_imu_factor_consistency import ( + MECHANICAL_L_I_M, _build_segment, _jsonable, _qualified_runs) + +def _select_window(sessions,reference,period,target_duration): + best = None + for session,run,segment_id in _qualified_runs(sessions,reference,period): + for start in range(len(run)): + target_t = run[start].t_s+target_duration + end = int(np.searchsorted([node.t_s for node in run],target_t)) + if end >= len(run): continue + window = tuple(run[start:end+1]) + duration = window[-1].t_s-window[0].t_s + if not 10. <= duration <= 20.: continue + best_count = sum(node.source == 'BESTNAVA' for node in window) + doppler_count = sum(node.velocity_enu_m_s is not None for node in window) + hpr_count = sum(node.hpr_factor_valid for node in window) + gyro_score = sum(np.linalg.norm(node.gyro_rad_s) for node in window) + score = 10.*best_count+10.*doppler_count+2.*hpr_count+gyro_score + candidate = (score,session,window,f'{segment_id}:window_{start:03d}', + best_count,doppler_count,hpr_count) + if best is None or candidate[0] > best[0]: best = candidate + if best is None: raise RuntimeError('no 10-20 s qualified window') + score,session,window,segment_id,best_count,doppler_count,hpr_count = best + segment = _build_segment(session,window,segment_id) + if segment is None: raise RuntimeError('selected window preintegration failed') + return segment,{'score':score,'session_id':session.session_id, + 'segment_id':segment_id,'start_s':window[0].t_s,'end_s':window[-1].t_s, + 'duration_s':window[-1].t_s-window[0].t_s,'node_count':len(window), + 'best_count':best_count,'doppler_count':doppler_count,'hpr_factor_count':hpr_count} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--target-duration-s',type=float,default=15.) + parser.add_argument('--max-nfev',type=int,default=30) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + args = parser.parse_args() + sessions = load_unified_sessions(args.manifest,selected_session_ids={args.session}) + reference = _height_reference(sessions) + if reference is None: raise RuntimeError('no BEST reference') + segment,selection = _select_window( + sessions,reference,args.sample_period_s,args.target_duration_s) + rotation = Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + problem = build_problem(segment,rotation,np.asarray(args.mechanical_l_I_m)) + result = solve_fixed_lever(problem,args.max_nfev) + payload = {'scope':'single 10-20 s node-state graph; fixed mechanical lever', + 'translation_variable_enabled':False,'free_prior_loo_bootstrap_sensitivity_called':False, + 'selection':selection,'result':asdict(result)} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2)) + return 0 + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_node_graph_free_circle.py b/tools/run_rtk_imu_node_graph_free_circle.py new file mode 100644 index 0000000..bd6daa8 --- /dev/null +++ b/tools/run_rtk_imu_node_graph_free_circle.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +'''Three-start, no-prior free-lever solve on the frozen P0.5 circle window.''' +from __future__ import annotations +import argparse,json,sys +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem,solve_free_lever +from tools.audit_rtk_imu_factor_consistency import MECHANICAL_L_I_M,_jsonable +from tools.run_rtk_imu_node_graph_fixed_lever import _select_window + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--target-duration-s',type=float,default=15.) + parser.add_argument('--max-nfev',type=int,default=120) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--large-perturbation-m',nargs=3,type=float, + default=[.5,-.5,.5]) + args=parser.parse_args() + sessions=load_unified_sessions( + args.manifest,selected_session_ids={args.circle_session}) + reference=_height_reference(sessions) + segment,selection=_select_window( + sessions,reference,args.sample_period_s,args.target_duration_s) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + mechanical=np.asarray(args.mechanical_l_I_m,dtype=float) + problem=build_problem(segment,rotation,mechanical,args.hpr_direct_sigma_rad) + starts={'zero':np.zeros(3),'mechanical':mechanical, + 'mechanical_large_perturbation':mechanical+args.large_perturbation_m} + results={name:asdict(solve_free_lever(problem,value,args.max_nfev)) + for name,value in starts.items()} + for result in results.values(): + covariance=np.asarray(result['lever_covariance_m2']) + result['lever_std_m']=np.sqrt(np.maximum(np.diag(covariance),0.)) + result['delta_to_mechanical_m']=np.asarray(result['final_l_I_m'])-mechanical + result['xy_marginal_covariance_m2']=covariance[:2,:2] + result['xy_information_singular_values']=np.linalg.svd( + np.linalg.pinv(covariance[:2,:2],rcond=1e-9),compute_uv=False) + solutions=np.asarray([value['final_l_I_m'] for value in results.values()]) + spread=float(max(np.linalg.norm(a-b) for a in solutions for b in solutions)) + xy_spread=float(max(np.linalg.norm(a[:2]-b[:2]) for a in solutions for b in solutions)) + circle_xy_observable=bool(all(value['success'] and + np.all(np.asarray(value['lever_std_m'])[:2]<=.15) for value in results.values())) + payload={'scope':'circle single-segment no-prior three-start free lever', + 'translation_variable_enabled':True,'manual_prior_used':False, + 'loo_bootstrap_sensitivity_called':False,'selection':selection, + 'fixed_rotation_rpy_deg':args.rotation_rpy_deg, + 'hpr_direct_sigma_rad':args.hpr_direct_sigma_rad, + 'mechanical_reference_m':mechanical,'large_perturbation_m':args.large_perturbation_m, + 'solutions':results,'maximum_solution_spread_m':spread, + 'maximum_xy_solution_spread_m':xy_spread, + 'circle_only_observability_gate':{ + 'xy_marginal_std_max_m':.15,'passed':circle_xy_observable}, + 'circle_only_data_only_accepted':circle_xy_observable, + 'joint_free_blocked_by_circle_gate':False, + 'covariance_postfit_scaled':False} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2)) + return 0 + + +if __name__=='__main__': + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_node_graph_free_joint.py b/tools/run_rtk_imu_node_graph_free_joint.py new file mode 100644 index 0000000..2753bba --- /dev/null +++ b/tools/run_rtk_imu_node_graph_free_joint.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +'''Three-start, no-prior joint free-lever solve on fixed P0.5 motion windows.''' +from __future__ import annotations +import argparse,json,sys +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem,solve_free_lever_many +from tools.audit_rtk_imu_factor_consistency import MECHANICAL_L_I_M,_jsonable +from tools.run_rtk_imu_node_graph_fixed_lever import _select_window + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--left-right-session',required=True) + parser.add_argument('--slope-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--target-duration-s',type=float,default=15.) + parser.add_argument('--max-nfev',type=int,default=120) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--large-perturbation-m',nargs=3,type=float, + default=[.5,-.5,.5]) + args=parser.parse_args() + categories={'circle':args.circle_session,'left_right':args.left_right_session, + 'slope':args.slope_session} + sessions=load_unified_sessions( + args.manifest,selected_session_ids=set(categories.values())) + reference=_height_reference(sessions) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + mechanical=np.asarray(args.mechanical_l_I_m,dtype=float) + problems,selections=[],{} + for motion,session_id in categories.items(): + segment,selection=_select_window( + [s for s in sessions if s.session_id==session_id],reference, + args.sample_period_s,args.target_duration_s) + problems.append(build_problem( + segment,rotation,mechanical,args.hpr_direct_sigma_rad)) + selections[motion]=selection + starts={'zero':np.zeros(3),'mechanical':mechanical, + 'mechanical_large_perturbation':mechanical+args.large_perturbation_m} + results={name:asdict(solve_free_lever_many(problems,value,args.max_nfev)) + for name,value in starts.items()} + for result in results.values(): + covariance=np.asarray(result['lever_covariance_m2']) + result['lever_std_m']=np.sqrt(np.maximum(np.diag(covariance),0.)) + result['delta_to_mechanical_m']=np.asarray(result['final_l_I_m'])-mechanical + solutions=np.asarray([value['final_l_I_m'] for value in results.values()]) + spread=float(max(np.linalg.norm(a-b) for a in solutions for b in solutions)) + gates={name:{'optimizer_converged':value['success'], + 'lever_marginal_std':bool(np.all(np.asarray(value['lever_std_m'])<=[.15,.15,.20])), + 'lever_information_rank':value['lever_precision_rank']==3, + 'lever_information_condition':value['lever_information_condition_number']<=1e6, + 'lever_min_information':min(value['lever_information_singular_values'])>=1e-3} + for name,value in results.items()} + observable=bool(all(all(gate.values()) for gate in gates.values())) + payload={'scope':'three-motion joint no-prior three-start free lever', + 'translation_variable_enabled':True,'manual_prior_used':False, + 'loo_bootstrap_sensitivity_called':False,'selections':selections, + 'fixed_rotation_rpy_deg':args.rotation_rpy_deg, + 'hpr_direct_sigma_rad':args.hpr_direct_sigma_rad, + 'mechanical_reference_m':mechanical,'solutions':results, + 'maximum_solution_spread_m':spread,'observability_gates':gates, + 'joint_data_only_translation_observable':observable, + 'manual_prior_started':False,'covariance_postfit_scaled':False} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2)) + return 0 + + +if __name__=='__main__': + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_node_graph_free_selected.py b/tools/run_rtk_imu_node_graph_free_selected.py new file mode 100644 index 0000000..b9eb9fc --- /dev/null +++ b/tools/run_rtk_imu_node_graph_free_selected.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +'''Three-start joint free solve on information-selected windows.''' +from __future__ import annotations +import argparse,json,sys +from concurrent.futures import ThreadPoolExecutor +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import ( + build_problem,fit_states_at_fixed_lever,solve_free_lever_many) +from tools.audit_rtk_imu_factor_consistency import MECHANICAL_L_I_M,_build_segment,_jsonable,_qualified_runs + + +def _restore_segments(sessions,reference,selections,period_s): + runs=list(_qualified_runs(sessions,reference,period_s)); restored=[] + for item in selections: + match=None + for session,run,_ in runs: + if session.session_id!=item['session_id']: continue + contained=(run[0].t_s<=item['start_s']+1e-6 and + run[-1].t_s>=item['end_s']-1e-6) + if not contained: continue + nodes=tuple(node for node in run + if item['start_s']-1e-6<=node.t_s<=item['end_s']+1e-6) + if len(nodes)==item['node_count']: + match=_build_segment(session,nodes,item['candidate_id']); break + if match is None: raise RuntimeError('cannot restore '+item['candidate_id']) + restored.append(match) + return restored + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--selection',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--max-nfev',type=int,default=120) + parser.add_argument('--prefit-max-nfev',type=int,default=50) + parser.add_argument('--prefit-workers',type=int,default=4) + parser.add_argument('--start-name',choices=['all','zero','mechanical', + 'mechanical_large_perturbation'],default='all') + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--large-perturbation-m',nargs=3,type=float, + default=[.5,-.5,.5]) + args=parser.parse_args() + selection=json.loads(args.selection.read_text(encoding='utf-8')) + ids={item['session_id'] for item in selection['selected_windows']} + sessions=load_unified_sessions(args.manifest,selected_session_ids=ids) + reference=_height_reference(sessions) + segments=_restore_segments(sessions,reference,selection['selected_windows'], + args.sample_period_s) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + mechanical=np.asarray(args.mechanical_l_I_m,dtype=float) + problems=[build_problem(segment,rotation,mechanical,args.hpr_direct_sigma_rad) + for segment in segments] + starts={'zero':np.zeros(3),'mechanical':mechanical, + 'mechanical_large_perturbation':mechanical+args.large_perturbation_m} + if args.start_name!='all': starts={args.start_name:starts[args.start_name]} + results={}; prefit={} + for name,value in starts.items(): + with ThreadPoolExecutor(max_workers=args.prefit_workers) as executor: + fitted=list(executor.map( + lambda problem:fit_states_at_fixed_lever( + problem,value,args.prefit_max_nfev),problems)) + state_values=[item[0] for item in fitted] + prefit[name]=[item[1] for item in fitted] + results[name]=asdict(solve_free_lever_many( + problems,value,args.max_nfev,state_values)) + for result in results.values(): + covariance=np.asarray(result['lever_covariance_m2']) + result['lever_std_m']=np.sqrt(np.maximum(np.diag(covariance),0.)) + result['delta_to_mechanical_m']=np.asarray(result['final_l_I_m'])-mechanical + solutions=np.asarray([value['final_l_I_m'] for value in results.values()]) + spread=float(max(np.linalg.norm(a-b) for a in solutions for b in solutions)) + gates={name:{'optimizer_converged':value['success'], + 'lever_marginal_std':bool(np.all(np.asarray(value['lever_std_m'])<=[.15,.15,.20])), + 'lever_information_rank':value['lever_precision_rank']==3, + 'lever_information_condition':value['lever_information_condition_number']<=1e6, + 'lever_min_information':min(value['lever_information_singular_values'])>=1e-3} + for name,value in results.items()} + observable=bool(all(all(gate.values()) for gate in gates.values())) + payload={'scope':'information-selected multi-window no-prior three-start free lever', + 'translation_variable_enabled':True,'manual_prior_used':False, + 'loo_bootstrap_sensitivity_called':False, + 'selection_artifact':str(args.selection),'selected_window_count':len(segments), + 'start_name':args.start_name,'nuisance_prefit':prefit, + 'nuisance_prefit_is_not_lever_prior':True, + 'sample_overlap_audit':selection['sample_overlap_audit'], + 'information_curve':selection['lever_std_vs_information_curve'], + 'fixed_rotation_rpy_deg':args.rotation_rpy_deg, + 'hpr_direct_sigma_rad':args.hpr_direct_sigma_rad, + 'mechanical_reference_m':mechanical,'solutions':results, + 'maximum_solution_spread_m':spread,'observability_gates':gates, + 'data_only_translation_observable':observable, + 'manual_prior_started':False,'covariance_postfit_scaled':False} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'selected_window_count':len(segments), + 'solutions':{name:{'success':value['success'],'l_I_m':value['final_l_I_m'], + 'std_m':value['lever_std_m'],'cost':value['final_cost'], + 'chi_square_per_dof':value['chi_square_per_dof'], + 'singular_values':value['lever_information_singular_values']} + for name,value in results.items()},'maximum_solution_spread_m':spread, + 'data_only_translation_observable':observable}),ensure_ascii=False,indent=2)) + return 0 + + +if __name__=='__main__': + raise SystemExit(main()) diff --git a/tools/run_rtk_imu_node_graph_p05.py b/tools/run_rtk_imu_node_graph_p05.py new file mode 100644 index 0000000..83d42f9 --- /dev/null +++ b/tools/run_rtk_imu_node_graph_p05.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +'''P0.5 fixed-lever node-graph covariance and motion-window audit.''' +from __future__ import annotations +import argparse, json, sys +from dataclasses import asdict +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import ( + build_problem,solve_fixed_lever,solve_fixed_lever_many) +from tools.audit_rtk_imu_factor_consistency import MECHANICAL_L_I_M,_jsonable +from tools.run_rtk_imu_node_graph_fixed_lever import _select_window + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--left-right-session',required=True) + parser.add_argument('--slope-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--target-duration-s',type=float,default=15.) + parser.add_argument('--max-nfev',type=int,default=50) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + parser.add_argument('--mechanical-l-I-m',nargs=3,type=float, + default=MECHANICAL_L_I_M.tolist()) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + args = parser.parse_args() + categories = {'circle':args.circle_session,'left_right':args.left_right_session, + 'slope':args.slope_session} + sessions = load_unified_sessions(args.manifest,selected_session_ids=set(categories.values())) + reference = _height_reference(sessions) + rotation = Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + lever = np.asarray(args.mechanical_l_I_m) + problems, selections, separate = [], {}, {} + for category,session_id in categories.items(): + local = [session for session in sessions if session.session_id == session_id] + segment,selection = _select_window( + local,reference,args.sample_period_s,args.target_duration_s) + problem = build_problem(segment,rotation,lever,args.hpr_direct_sigma_rad) + problems.append(problem); selections[category] = selection + separate[category] = asdict(solve_fixed_lever(problem,args.max_nfev)) + joint = asdict(solve_fixed_lever_many(problems,args.max_nfev)) + fixed_pass = bool( + all(value['success'] for value in separate.values()) and joint['success'] + and joint['chi_square_per_dof'] >= .25 + and joint['chi_square_per_dof'] <= 4. + and joint['final_position_residual_m']['vector_p95'] <= .20 + and joint['final_velocity_residual_m_s']['vector_p95'] <= .50) + payload = {'scope':'P0.5 three-motion fixed-lever only', + 'translation_variable_enabled':False, + 'manual_prior_loo_bootstrap_sensitivity_called':False, + 'covariance_model':{ + 'source':'independent_innovation_audit_three_motion', + 'best_position_xyz_m':[.06,.06,.12], + 'doppler_xyz_m_s':[.15,.15,.30], + 'hpr_direct_angular_rad':args.hpr_direct_sigma_rad, + 'hpr_bridge_rule':'sqrt(direct_sigma^2 + dropout_extra_variance)', + 'imu_preintegration':'unchanged physical covariance', + 'bias_random_walk':'unchanged static/Allan/device model', + 'postfit_global_scale_applied':False}, + 'fixed_l_I_m':lever,'selections':selections, + 'separate_fixed_lever':separate,'joint_fixed_lever':joint, + 'fixed_lever_covariance_gate':{ + 'chi_square_per_dof_range':[.25,4.], + 'position_vector_p95_max_m':.20, + 'velocity_vector_p95_max_m_s':.50, + 'passed':fixed_pass}, + 'whitening_diagnosis':{ + 'normalized_scale_consistent':joint['chi_square_per_dof'] >= .25, + 'joint_preintegration_nis_per_dof': + joint['final_residual_by_factor']['imu_preintegration']['chi_square_per_dof'], + 'joint_preintegration_normalized_p95': + joint['final_residual_by_factor']['imu_preintegration']['p95_abs'], + 'preintegration_sigma_distribution':joint['preintegration_covariance_sigma'], + 'interpretation':( + 'absolute preintegration sigma is small, but per-node states satisfy process ' + 'factors almost exactly while BEST/Doppler/HPR normalized residuals are also ' + 'well below one; current factor covariance set is collectively overconservative' + )}, + 'free_lever_unlocked':fixed_pass} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + compact = {'selections':selections, + 'separate':{key:{'success':value['success'], + 'chi_square_per_dof':value['chi_square_per_dof'], + 'position_p95_m':value['final_position_residual_m']['vector_p95'], + 'velocity_p95_m_s':value['final_velocity_residual_m_s']['vector_p95'], + 'factor_stats':value['final_residual_by_factor']} + for key,value in separate.items()}, + 'joint':{'success':joint['success'],'chi_square_per_dof':joint['chi_square_per_dof'], + 'position_p95_m':joint['final_position_residual_m']['vector_p95'], + 'velocity_p95_m_s':joint['final_velocity_residual_m_s']['vector_p95'], + 'factor_stats':joint['final_residual_by_factor']}, + 'free_lever_unlocked':fixed_pass} + print(json.dumps(_jsonable(compact),ensure_ascii=False,indent=2)) + return 0 + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tools/select_rtk_imu_windows_by_lever_information.py b/tools/select_rtk_imu_windows_by_lever_information.py new file mode 100644 index 0000000..5413a4f --- /dev/null +++ b/tools/select_rtk_imu_windows_by_lever_information.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +'''Select non-overlapping qualified windows by incremental lever information.''' +from __future__ import annotations +import argparse,json,sys +from pathlib import Path +import numpy as np +from scipy.spatial.transform import Rotation +ROOT=Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: sys.path.insert(0,str(ROOT)) +from imu_lidar.rtk_imu_engineering import _all_hpr,_height_reference +from imu_lidar.rtk_imu_multisource import load_unified_sessions +from imu_lidar.rtk_imu_node_graph import build_problem,linearized_lever_information +from tools.audit_rtk_imu_factor_consistency import _build_segment,_jsonable,_qualified_runs +from tools.run_rtk_imu_node_graph_fixed_lever import _select_window + + +STD_GATE=np.array([.15,.15,.20]) + + +def _overlap(left,right): + return left['session_id']==right['session_id'] and not ( + left['end_s']0 else -np.inf} + + +def _window_candidates(sessions,reference,period_s,duration_s): + candidates=[] + for session,run,segment_id in _qualified_runs(sessions,reference,period_s): + times=np.asarray([node.t_s for node in run]) + start=0 + while start=len(run): break + window=tuple(run[start:end+1]) + if 10.<=window[-1].t_s-window[0].t_s<=20.: + candidate_id=f'{segment_id}:info_window_{start:04d}' + segment=_build_segment(session,window,candidate_id) + if segment is not None: + candidates.append({'candidate_id':candidate_id, + 'session_id':session.session_id,'start_s':window[0].t_s, + 'end_s':window[-1].t_s,'duration_s':window[-1].t_s-window[0].t_s, + 'node_count':len(window),'segment':segment,'session':session}) + start=end+1 + return candidates + + +def _sample_keys(entry): + session=entry['session']; start,end=entry['start_s'],entry['end_s'] + imu=set((session.session_id,int(round(t*1e6))) for t in session.imu.t_s + if start<=t<=end) + gnss=set((session.session_id,int(round(node.t_s*1e6)),node.source) + for node in entry['segment'].nodes) + hpr=_all_hpr(session) + hpr_keys=set((session.session_id,int(round(hpr.t_s[i]*1e6))) + for i in hpr.valid_indices if start<=hpr.t_s[i]<=end) + return {'imu':imu,'gnss':gnss,'hpr':hpr_keys} + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--manifest',type=Path,required=True) + parser.add_argument('--output',type=Path,required=True) + parser.add_argument('--circle-session',required=True) + parser.add_argument('--left-right-session',required=True) + parser.add_argument('--slope-session',required=True) + parser.add_argument('--sample-period-s',type=float,default=1.) + parser.add_argument('--window-duration-s',type=float,default=15.) + parser.add_argument('--max-additional-windows',type=int,default=100) + parser.add_argument('--hpr-direct-sigma-rad',type=float,default=.006) + parser.add_argument('--linearization-l-I-m',nargs=3,type=float, + default=[-.53243,-.42662,.74660]) + parser.add_argument('--rotation-rpy-deg',nargs=3,type=float, + default=[.4543066225,-.0026392019,.0122384129]) + args=parser.parse_args() + sessions=load_unified_sessions(args.manifest) + reference=_height_reference(sessions) + rotation=Rotation.from_euler('xyz',args.rotation_rpy_deg,degrees=True).as_matrix() + lever=np.asarray(args.linearization_l_I_m,dtype=float) + categories={'circle':args.circle_session,'left_right':args.left_right_session, + 'slope':args.slope_session} + selected=[] + for motion,session_id in categories.items(): + session=[s for s in sessions if s.session_id==session_id] + segment,meta=_select_window( + session,reference,args.sample_period_s,args.window_duration_s) + selected.append({**meta,'candidate_id':meta['segment_id'],'motion':motion, + 'segment':segment,'session':session[0],'seed_window':True}) + candidates=[entry for entry in _window_candidates( + sessions,reference,args.sample_period_s,args.window_duration_s) + if not any(_overlap(entry,seed) for seed in selected)] + for entry in [*selected,*candidates]: + problem=build_problem(entry['segment'],rotation,lever,args.hpr_direct_sigma_rad) + information,_,singular,weak=linearized_lever_information(problem,lever) + entry['problem']=problem; entry['information']=information + entry['single_window_singular_values']=singular + entry['single_window_weakest_direction_I']=weak + total=sum((entry['information'] for entry in selected),np.zeros((3,3))) + curve=[{'window_count':len(selected),'added_candidate_id':'seed_three_motion', + **_summary(total)}] + remaining=list(candidates) + saturation_count=0 + stop_reason='candidate_exhausted' + while remaining and len(selected)<3+args.max_additional_windows: + feasible=[entry for entry in remaining + if not any(_overlap(entry,item) for item in selected)] + if not feasible: break + ranked=[] + for entry in feasible: + summary=_summary(total+entry['information']) + ranked.append((summary['lambda_min'],summary['logdet'],entry,summary)) + _,_,choice,summary=max(ranked,key=lambda item:(item[0],item[1])) + gain=summary['lambda_min']-curve[-1]['lambda_min'] + selected.append(choice); remaining.remove(choice); total+=choice['information'] + curve.append({'window_count':len(selected),'added_candidate_id':choice['candidate_id'], + 'delta_lambda_min':gain,**summary}) + saturation_count=saturation_count+1 if gain<.01 else 0 + if np.all(np.asarray(summary['std_m'])<=STD_GATE): + stop_reason='linearized_observability_gate_reached'; break + if saturation_count>=3: + stop_reason='incremental_lambda_min_gain_saturated'; break + else: + if len(selected)>=3+args.max_additional_windows: + stop_reason='max_additional_windows_reached' + seen={'imu':set(),'gnss':set(),'hpr':set()} + duplicate={'imu':0,'gnss':0,'hpr':0} + selected_output=[] + for order,entry in enumerate(selected): + keys=_sample_keys(entry) + shared={name:len(value&seen[name]) for name,value in keys.items()} + for name,value in keys.items(): + duplicate[name]+=shared[name]; seen[name].update(value) + selected_output.append({'selection_order':order, + 'candidate_id':entry['candidate_id'],'session_id':entry['session_id'], + 'start_s':entry['start_s'],'end_s':entry['end_s'], + 'duration_s':entry['duration_s'],'node_count':entry['node_count'], + 'seed_window':entry.get('seed_window',False), + 'single_window_information':entry['information'], + 'single_window_singular_values':entry['single_window_singular_values'], + 'single_window_weakest_direction_I':entry['single_window_weakest_direction_I'], + 'sample_count':{name:len(value) for name,value in keys.items()}, + 'shared_sample_count_with_previous':shared}) + payload={'scope':'all recovered qualified dynamics; information-based window selection', + 'manual_prior_used':False,'linearization_l_I_m':lever, + 'linearization_point_is_not_prior_factor':True, + 'candidate_count':len(candidates),'selected_window_count':len(selected), + 'selection_objective':'maximize lambda_min(H_l); break ties by logdet(H_l)', + 'std_gate_m':STD_GATE,'stop_reason':stop_reason, + 'selected_windows':selected_output,'lever_std_vs_information_curve':curve, + 'sample_overlap_audit':{'duplicate_sample_count':duplicate, + 'all_selected_windows_time_nonoverlapping_within_session':not any( + _overlap(a,b) for i,a in enumerate(selected) for b in selected[i+1:]), + 'unique_sample_count':{name:len(value) for name,value in seen.items()}}, + 'final_linearized_observable':bool(np.all( + np.asarray(curve[-1]['std_m'])<=STD_GATE))} + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload),ensure_ascii=False,indent=2, + allow_nan=False)+'\n',encoding='utf-8') + print(json.dumps(_jsonable({'candidate_count':len(candidates), + 'selected_window_count':len(selected),'stop_reason':stop_reason, + 'final_curve':curve[-1],'sample_overlap_audit':payload['sample_overlap_audit']}), + ensure_ascii=False,indent=2)) + return 0 + + +if __name__=='__main__': + raise SystemExit(main()) diff --git a/tools/validate_rtk_hpr_dropout_bridge.py b/tools/validate_rtk_hpr_dropout_bridge.py new file mode 100644 index 0000000..0db9dea --- /dev/null +++ b/tools/validate_rtk_hpr_dropout_bridge.py @@ -0,0 +1,182 @@ +#!/usr/bin/env python3 +"""Read-only artificial GNHPR dropout validation for the engineering R0 bridge. + +For each requested gap, Q4 HPR samples inside an otherwise 0.1 s contiguous +run are withheld. Their true baseline directions are compared with normalized +linear interpolation and short-horizon HI13 gyro propagation. No lever-arm +solve, prior, bootstrap, sensitivity run, or threshold update is performed. +""" +from __future__ import annotations + +import argparse +import json +import math +import sys +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from imu_lidar.rtk_imu_engineering import _all_hpr, _baseline_angle_deg, _interpolate_baseline, _world_rtk +from imu_lidar.rtk_imu_multisource import load_unified_sessions + +GAPS_S = (0.2, 0.3, 0.4, 0.5, 0.6, 0.8) +R2G_RPY_DEG = (0.4543066225, -0.0026392019, 0.0122384129) +BASELINE_FACTOR_SIGMA_DEG = float(np.degrees(0.035)) + + +def _jsonable(value): + if isinstance(value, np.ndarray): + return _jsonable(value.tolist()) + if isinstance(value, np.generic): + return _jsonable(value.item()) + if isinstance(value, float): + return value if math.isfinite(value) else None + if isinstance(value, dict): + return {str(k): _jsonable(v) for k, v in value.items()} + if isinstance(value, (tuple, list)): + return [_jsonable(v) for v in value] + return value + + +def _contiguous_runs(times: np.ndarray, valid: np.ndarray) -> list[np.ndarray]: + indices = np.flatnonzero(valid) + if indices.size == 0: + return [] + runs: list[list[int]] = [[int(indices[0])]] + for previous, index in zip(indices[:-1], indices[1:]): + dt = float(times[index] - times[previous]) + if 0.075 <= dt <= 0.125: + runs[-1].append(int(index)) + else: + runs.append([int(index)]) + return [np.asarray(run, dtype=int) for run in runs if len(run) >= 3] + + +def _propagate_baseline(session, R_WI_start: np.ndarray, baseline_I: np.ndarray, + start_s: float, targets_s: np.ndarray) -> list[np.ndarray]: + """Propagate with vectorized gyro interpolation over the short withheld gap.""" + targets = np.asarray(targets_s, dtype=float) + if targets.size == 0: + return [] + grid = np.unique(np.concatenate(([start_s], session.imu.t_s[ + (session.imu.t_s > start_s) & (session.imu.t_s < targets[-1])], targets))) + gyro = np.column_stack([ + np.interp(grid, session.imu.t_s, session.imu.gyro_rad_s[:, axis]) for axis in range(3) + ]) + R_WI = np.asarray(R_WI_start, dtype=float).copy() + predicted: list[np.ndarray] = [] + target_index = 0 + for index, (left, right) in enumerate(zip(grid[:-1], grid[1:])): + omega = 0.5 * (gyro[index] + gyro[index + 1]) + R_WI = R_WI @ Rotation.from_rotvec(omega * float(right - left)).as_matrix() + while target_index < targets.size and abs(targets[target_index] - right) < 1e-9: + predicted.append(R_WI @ baseline_I) + target_index += 1 + return predicted + +def _session_validation(session, rotation: np.ndarray, max_windows: int) -> dict[str, object]: + hpr = _all_hpr(session) + runs = _contiguous_runs(hpr.t_s, hpr.valid) + baseline_I = rotation.T[:, 0] + result: dict[str, object] = {"session_id": session.session_id, "q4_runs": len(runs), "gaps": {}} + for requested_gap in GAPS_S: + interpolation_errors: list[float] = [] + propagation_errors: list[float] = [] + actual_gaps: list[float] = [] + windows = 0 + for run in runs: + nominal_dt = float(np.median(np.diff(hpr.t_s[run]))) + step_count = max(2, int(round(requested_gap / nominal_dt))) + # The endpoints remain observed, every internal Q4 point is withheld. + for start in range(0, run.size - step_count, max(1, step_count)): + subset = run[start:start + step_count + 1] + if subset.size != step_count + 1: + continue + left, right = int(subset[0]), int(subset[-1]) + actual_gap = float(hpr.t_s[right] - hpr.t_s[left]) + if abs(actual_gap - requested_gap) > 0.08: + continue + withheld = subset[1:-1] + if withheld.size == 0: + continue + left_b, right_b = hpr.baseline_enu[left], hpr.baseline_enu[right] + targets = hpr.t_s[withheld] + predicted_prop = _propagate_baseline( + session, _world_rtk(left_b) @ rotation, baseline_I, float(hpr.t_s[left]), targets + ) + for index, predicted in zip(withheld, predicted_prop): + fraction = float((hpr.t_s[index] - hpr.t_s[left]) / actual_gap) + interpolation_errors.append(_baseline_angle_deg( + _interpolate_baseline(left_b, right_b, fraction), hpr.baseline_enu[index] + )) + propagation_errors.append(_baseline_angle_deg(predicted, hpr.baseline_enu[index])) + actual_gaps.append(actual_gap) + windows += 1 + if windows >= max_windows: + break + if windows >= max_windows: + break + def summary(errors: list[float]) -> dict[str, float | int | None]: + finite = np.asarray([e for e in errors if np.isfinite(e)], dtype=float) + if finite.size == 0: + return {"count": 0, "p50_deg": None, "p95_deg": None, "max_deg": None} + return {"count": int(finite.size), "p50_deg": float(np.percentile(finite, 50)), + "p95_deg": float(np.percentile(finite, 95)), "max_deg": float(np.max(finite))} + interp, prop = summary(interpolation_errors), summary(propagation_errors) + choice = "linear_baseline_interpolation" if (interp["p95_deg"] or np.inf) <= (prop["p95_deg"] or np.inf) else "imu_gyro_propagation" + selected_p95 = interp["p95_deg"] if choice.startswith("linear") else prop["p95_deg"] + result["gaps"][f"{requested_gap:.1f}"] = { + "requested_gap_s": requested_gap, + "actual_gap_p50_s": float(np.median(actual_gaps)) if actual_gaps else None, + "window_count": windows, + "linear_baseline_interpolation": interp, + "imu_gyro_propagation": prop, + "preferred_method_by_p95": choice, + "selected_p95_deg": selected_p95, + "within_baseline_factor_sigma": bool(selected_p95 is not None and selected_p95 <= BASELINE_FACTOR_SIGMA_DEG), + } + return result + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--session", action="append") + parser.add_argument("--max-windows", type=int, default=500) + args = parser.parse_args() + sessions = load_unified_sessions(args.manifest, selected_session_ids=None if args.session is None else set(args.session)) + rotation = Rotation.from_euler("xyz", R2G_RPY_DEG, degrees=True).as_matrix() + session_results = [_session_validation(session, rotation, args.max_windows) for session in sessions] + recommendation: dict[str, object] = {} + for gap in GAPS_S: + entries = [item["gaps"][f"{gap:.1f}"] for item in session_results if item["gaps"].get(f"{gap:.1f}")] + p95 = [entry["selected_p95_deg"] for entry in entries if entry["selected_p95_deg"] is not None] + recommendation[f"{gap:.1f}"] = { + "sessions_with_samples": len(p95), + "worst_session_selected_p95_deg": float(max(p95)) if p95 else None, + "passes_all_sessions_factor_sigma": bool(p95 and max(p95) <= BASELINE_FACTOR_SIGMA_DEG), + } + passing = [float(key) for key, value in recommendation.items() if value["passes_all_sessions_factor_sigma"]] + payload = { + "scope": "artificial HPR dropout validation only; no solve/prior/bootstrap/sensitivity/threshold change", + "r2g_rotation_rpy_deg": R2G_RPY_DEG, + "baseline_factor_sigma_deg": BASELINE_FACTOR_SIGMA_DEG, + "sessions": session_results, + "bridge_recommendation": recommendation, + "largest_gap_passing_all_sessions_s": max(passing) if passing else None, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(_jsonable(payload), ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({"largest_gap_passing_all_sessions_s": payload["largest_gap_passing_all_sessions_s"], + "bridge_recommendation": recommendation}, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file