commit 62c7ab2e98eb0d6a92b8796fcce454949d347b71 Author: lichun.qu <16975270+zzqlc@user.noreply.gitee.com> Date: Wed Jul 22 09:31:44 2026 +0800 Add LiDAR RTK hand-eye calibration workflow and results diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..00f2d38 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +__pycache__/ +*.pyc +.venv/ diff --git a/CONSENSUS_SELECTION.md b/CONSENSUS_SELECTION.md new file mode 100644 index 0000000..eb46fba --- /dev/null +++ b/CONSENSUS_SELECTION.md @@ -0,0 +1,12 @@ +# 最终推荐值补充说明 + +两套后端各自结果均保留。最终推荐值又增加了一层不依赖外参X的交叉检查：只有同一运动对的Open3D B与small_gicp B相差不超过5 cm、0.5°时才进入最终求解；最终B数为39对。 + +最终推荐： + +- 平移 `[1.297760, -0.000067, 0.720498] m` +- RPY `[-0.785151, 1.202661, -0.835510] deg` +- 第二批AX RMS：`0.07985 m / 0.96118°` +- 第一批22对辅助复核：`0.06067 m / 1.00298°` + +完整结果见 `results/final_extrinsic_recommended.json`，选择摘要见 `results/final_summary.json`。执行完整 `run_all.ps1` 后，再执行 `run_consensus_finish.ps1` 可重建最终推荐结果。 diff --git a/PAIR_DIAGNOSTICS.md b/PAIR_DIAGNOSTICS.md new file mode 100644 index 0000000..70e7928 --- /dev/null +++ b/PAIR_DIAGNOSTICS.md @@ -0,0 +1,85 @@ +# Pair 局部诊断与全局灵敏度扫描 + +本说明补充主 README。所有变换均采用： + +```text +X = T_body_lidar +A_ij X = X B_ij +B_ij = T_Li_Lj（把站点 j 点云变到站点 i） +``` + +模式 4 相对模式 3 的数值差定义为： + +```text +Delta_ij = B_ij^-1 * (X^-1 * A_ij * X) +``` + +打印的平移 xyz 和旋转 RPY 是 `Delta_ij` 在站点 j 雷达局部坐标系中的分量，不是屏幕坐标。3D 相机视角会改变画面中的“横向”，所以不能仅凭屏幕左右判断车体系 Y 或 yaw。 + +## Pair 0 当前诊断 + +Open3D 精筛 Pair 0（station 0 <- 1）的当前结果为： + +```text +translation xyz = [-1.2535, +1.4157, +7.7441] cm +rotation RPY xyz = [-0.5355, -0.1093, +0.0247] deg +norm = 7.9716 cm / 0.547109 deg +``` + +因此这对主要表现为 roll/pitch 相关姿态差和相对 Z 差，yaw 仅约 `0.025 deg`，不应优先调 yaw。 + +## 可视化试验 + +查看原结果： + +```powershell +$Repo = "D:\Outdoor Ackerman Cart Sensor Adaptation\LiDAR_RTK_Calibration_Rigorous_20260721" +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_open3d_result.ps1" -PairIndex 0 +``` + +试验车体系左乘 pitch `+0.2 deg`： + +```powershell +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_open3d_result.ps1" -PairIndex 0 -LeftPitchDeg 0.2 +``` + +此时按键含义：`3` 为 GICP 的 B，`4` 为当前最终 X 预测，`5` 为试验修正后的 X 预测。试验使用： + +```text +X_test = DeltaR_body * X +``` + +这是真正的车体系左乘，会同时旋转 X 的旋转部分和平移向量；小角度下才近似等价于直接给 JSON 的 pitch 加相同角度。 + +## 全部运动对扫描 + +运行： + +```powershell +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_sensitivity_scan.ps1" -PairIndex 0 +``` + +程序扫描： + +- pitch：`+0.1/+0.2/+0.3 deg`； +- 在这些 pitch 及零 pitch 附近扫描 roll：`-0.2/-0.1/+0.1 deg`； +- yaw `-0.2/+0.2 deg` 只作为低灵敏度对照； +- Open3D 全部精筛对、small_gicp 全部精筛对、跨后端共识对分别计算； +- 同时报告地面法向与高度残差； +- 使用 `0.05 m / 0.5 deg` 作为透明的归一化尺度，统计全局 RMS、改善对数和恶化对数。 + +输出位于 `results/diagnostics/`。JSON 保存逐对结果，CSV 便于横向比较。 + +## 接受规则 + +局部扫描只用于定位，程序不会覆盖 `final_extrinsic_recommended.json`。候选至少需要满足： + +1. 不能只改善 Pair 0；全部精筛对的归一化 RMS 应下降； +2. 改善的运动对数量应多于恶化数量； +3. Open3D、small_gicp 与共识集合应给出相同方向的趋势； +4. 地面法向和高度约束不能明显恶化； +5. 多个代表性运动对的模式 4/5 可视化应同步改善。 + +如果只有 Pair 0 改善，应把它视为局部配准或场景问题，不修改全局 X。 + +对于当前仅含 yaw 的 RTK A，车体系 Z 轴平移在 AX=XB 中不可观。扫描程序会用 `X.z + 0.10 m` 做数值检查，但不会扫描或修改 z；z 必须由地面高度约束或外部量测确定。 diff --git a/README.md b/README.md new file mode 100644 index 0000000..56d6c08 --- /dev/null +++ b/README.md @@ -0,0 +1,228 @@ +# 双天线 RTK—3D 激光雷达严谨手眼标定 + +本仓库是当前建议提交和继续维护的正式版本。第二批 38 站高频 RTK 用于主要求解；第一批 38 站稀疏 RTK 只作辅助复核。Open3D GICP 和 small_gicp 分别生成雷达运动 (B)，两套独立结果及跨后端共识结果均保留。 + +实际部署只读取 [`results/final_extrinsic_deployment.json`](results/final_extrinsic_deployment.json)。算法原始求解、残差、Bootstrap 和筛选记录保留在其他结果文件中，不应与部署文件混用。 + +## 标定总体流程 + +1. **准备静止站点数据**：每站车辆静止，保存一帧原始雷达点云和该站 RTK；程序只使用雷达传感器坐标系下的 `points_raw`。 +2. **由 RTK 生成车体运动 (A)**：将双天线 RTK 的位置、水平航向、杆臂和 heading offset 转为车体位姿 (T_{WB_i})，再计算 + + \[ + A_{ij}=T_{WB_i}^{-1}T_{WB_j}. + \] +3. **由点云配准生成雷达运动 (B)**：Open3D GICP 和 small_gicp 分别把第 j 站点云配准到第 i 站，得到 + + \[ + B_{ij}=T_{L_iL_j}. + \] +4. **先验证和筛选 B，再求 X**：使用对称留出点、重叠率、留出点 RMSE、海森矩阵、正反向一致性、多初值稳定性、旋转共轭不变量和闭环误差检查 B。这个阶段不读取待求外参 X，也不使用 AX 残差筛选 B。 +5. **建立跨后端共识 B**：只保留 Open3D 与 small_gicp 对同一运动对的 B 相差不超过 `0.05 m / 0.50°` 的边；最终使用 Open3D B，small_gicp 作为独立一致性门控。 +6. **求解手眼外参 X**：固定 A 和通过筛选的 B 后，求解 + + \[ + A_{ij}X=XB_{ij},\qquad X=T_{body\leftarrow lidar}. + \] + + 使用 Huber 鲁棒最小二乘、12 个初值、地面平面约束和 100 次 Bootstrap。 +7. **结果复核**：第二批共识对用于主要求解和整体残差统计；第一批重新独立生成 B，只作为稀疏 RTK 辅助复核；3D 可视化分别比较模式 3 的 GICP B 与模式 4 的 (X^{-1}AX)。 +8. **形成部署结果**：第二批跨后端共识结果给出完整的 `[x,y,z,roll,pitch,yaw]`；部署 JSON 直接采用该算法结果。。 + +### 验证逻辑 + +顺序固定为： + +```text +先生成 B + -> 用不依赖 X 的点云与几何指标筛 B + -> 用另一个 GICP 后端复核同一 B + -> 固定共识 B + -> 最后才求 X + -> AX 残差只用于结果评价，不反过来选择 B +``` + +代码证据： + +- [`code/refine_pairs.py`](code/refine_pairs.py) 不读取外参文件，只使用留出点、正反向和旋转共轭不变量等指标； +- [`code/cross_backend_filter.py`](code/cross_backend_filter.py) 只比较两个后端的 B，不读取 X； +- [`code/rigorous_calibration.py`](code/rigorous_calibration.py) 在 B 集合固定后才执行 `calibrate` 求 X； +- 第一批辅助复核的 B 也重新独立生成，且不使用 AX 残差筛选。 + +但这些检查仍不能把 B 变成绝对真值：重复结构、动态物体或错误局部最优仍可能让两个后端同时出错，所以必须结合模式 3 点云质量、留出集指标和多运动对整体统计判断。 + +## 最终部署外参 + +坐标变换定义： + +\[ +X=T_{body\leftarrow lidar} +\] + +即将原始雷达点从雷达坐标系变换到以后轮轴中心为原点、X 前/Y 左/Z 上的车体坐标系。 + +部署值： + +- 平移 `[x,y,z] = [1.297760, -0.000067, 0.720498] m` +- RPY `[roll,pitch,yaw] = [-0.785151, 1.202661, -0.835510] deg` +- 唯一部署文件：[`results/final_extrinsic_deployment.json`](results/final_extrinsic_deployment.json) + +该值来自第二批 39 对跨后端共识、完整地面平面约束和 Bootstrap 稳定性检查。 + +算法在完整地面平面模型下的原始结果为： + +- 平移 `[1.297760, -0.000067, 0.720498] m` +- RPY `[-0.785151, 1.202661, -0.835510] deg` +- 文件：[`results/final_extrinsic_recommended.json`](results/final_extrinsic_recommended.json) + +`final_extrinsic_recommended.json` 保留完整求解、残差和 Bootstrap 记录，用于复现和审计；`final_extrinsic_deployment.json` 是唯一部署入口。 + +共识结果统计： + +- 第二批 39 对 AX RMS：`0.07985 m / 0.96118°` +- 第一批 22 对辅助复核：`0.06067 m / 1.00298°` +- 结果摘要：[`results/final_summary.json`](results/final_summary.json) + +两套后端分别求得： + +| 后端 | 平移 `[x,y,z]` m | RPY deg | 第二批 AX RMS | +|---|---|---|---| +| Open3D GICP | `[1.297883,-0.003100,0.721789]` | `[-0.757207,1.146042,-0.879781]` | `0.1002 m / 1.0115°` | +| small_gicp | `[1.299630,-0.003565,0.721886]` | `[-0.787156,1.141456,-0.935757]` | `0.1187 m / 1.1351°` | + +两后端单独求出的 X 相差 1.81 mm、0.063°。共识求解的 100 次 Bootstrap 标准差 `[x,y,z,roll,pitch,yaw]` 为： + +```text +[0.00322 m, 0.00333 m, 0.00169 m, 0.0980°, 0.0746°, 0.1189°] +``` + +这里的 Z Bootstrap 只描述当前地面模型条件下的内部稳定性，不代表机械高度的绝对精度。 + +重要限制：旋转 RMS 仍约 1°，所以这不是厘米级绝对精度认证。远距离点云仍可能看到角度错层；Bootstrap 也不包含 RTK 参考点、航向偏置或杆臂定义错误等系统误差。 + +## 坐标与公式 + +\[ +A_{ij}=T_{WB_i}^{-1}T_{WB_j},\qquad +B_{ij}=T_{L_iL_j},\qquad +A_{ij}X=XB_{ij} +\] + +`B_ij` 把第 j 站雷达点变换到第 i 站雷达坐标系。程序只读取 `points_raw`，禁止使用已经变到车体坐标系的点。 + +RTK 轨迹使用 `body_poses_rear_gga_raw_rear_to_front.csv`，对应后天线 GGA 位置和双天线后到前方向。 + +地面约束采用完整平面变换： + +\[ +n_B=R_Xn_L,\qquad r_h=d_L-n_B^Tt_X-h_{body} +\] + +不再使用旧近似式 `d_L-(t_z+h_body)`。 + +## 详细求解流程 + +1. 第二批高频 RTK 生成 A；第一批稀疏 RTK 不参与主要求解。 +2. 不使用手量外参作初值：生成 B 时 (X_0=I)，所以 (B_0=A)；求 X 也从单位变换和随机初值开始。 +3. 两后端均采用三级粗到细：体素 0.30/0.15/0.08 m，对应距离 1.20/0.50/0.25 m。 +4. 逐对检查对称留出点、海森矩阵、正反向一致性、多初值稳定性和 B 三角闭环。 +5. 使用重叠率、留出点 RMSE 和旋转共轭不变量筛选 B；不使用 AX 残差筛选。 +6. 两后端分别求 X；随后使用跨后端 B 一致性得到最终 39 对共识集合。 +7. 使用 Huber 鲁棒最小二乘、12 个随机初值、完整地面约束和 100 次 Bootstrap 求算法 X。 +8. 第一批重新独立生成 B，仅作辅助复核，不参与第二批求解。 +9. 使用共识算法完整解形成精简部署 JSON，不覆盖算法原始结果。 + +## 目录 + +```text +code/ + rigorous_calibration.py + refine_pairs.py + cross_backend_filter.py + summarize_results.py + finalize_consensus.py + scan_extrinsic_sensitivity.py + visualize_pair_3d.py +run/ + run_all.ps1 + run_consensus_finish.ps1 + run_sensitivity_scan.ps1 + view_open3d_result.ps1 + view_small_gicp_result.ps1 +results/ + common/ + open3d_gicp/ + small_gicp/ + consensus/ + diagnostics/ + final_extrinsic_deployment.json # 实际部署只读取这个文件 + final_extrinsic_recommended.json # 算法原始结果及详细诊断 + final_summary.json +``` + +## 安装与完整重跑 + +```powershell +$Repo = "D:\Outdoor Ackerman Cart Sensor Adaptation\LiDAR_RTK_Calibration_Rigorous_20260721" +python -m pip install -r "$Repo\requirements.txt" +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_all.ps1" +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_consensus_finish.ps1" +``` + +如果 prepared 数据移动了： + +```powershell +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_all.ps1" -Batch1Prepared "你的第一批prepared目录" -Batch2Prepared "你的第二批prepared目录" +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_consensus_finish.ps1" +``` + +重跑 `run_consensus_finish.ps1` 会重新生成算法原始结果，但不会自动生成或覆盖已经人工确认的 `final_extrinsic_deployment.json`。 + +## 3D 可视化 + +Open3D 单后端结果： + +```powershell +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_open3d_result.ps1" -PairIndex 0 +``` + +small_gicp 单后端结果： + +```powershell +powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_small_gicp_result.ps1" -PairIndex 0 +``` + +蓝色是目标站 i，橙色是源站 j。按键：`1` 原始；`2` RTK 初始 A；`3` GICP B；`4` 外参预测的 (X^{-1}AX)；指定左乘 RPY 试验量后，`5` 为试验结果。 + +程序打印的差值为： + +\[ +B^{-1}(X^{-1}AX) +\] + +其 xyz/RPY 分量位于站点 j 的雷达局部坐标系，不是屏幕坐标。模式 3 好而模式 4 差，说明 A/X 一致性不足；模式 3 本身差，说明该 B 不能用于判断外参。 + +共识运动对可直接指定部署外参： + +```powershell +python "$Repo\code\visualize_pair_3d.py" --frames "C:\Users\admin\Documents\Codex\2026-07-15\wo\outputs\calibration_data1_20260720\prepared\frames_all" --pairs "$Repo\results\consensus\B_batch2_consensus.npz" --extrinsic "$Repo\results\final_extrinsic_deployment.json" --pair-index 0 +``` + +局部灵敏度扫描和判断规则见 [`PAIR_DIAGNOSTICS.md`](PAIR_DIAGNOSTICS.md)。 + +## 关键质量文件 + +- [`results/open3d_gicp/B_batch2_quality.json`](results/open3d_gicp/B_batch2_quality.json)：海森矩阵、留出点、正反向和多初值信息； +- [`results/open3d_gicp/B_batch2_refined.refinement.json`](results/open3d_gicp/B_batch2_refined.refinement.json)：二级筛选原因； +- [`results/consensus/B_batch2_consensus.consensus.json`](results/consensus/B_batch2_consensus.consensus.json)：跨后端一致性筛选； +- [`results/consensus/batch1_auxiliary_check.json`](results/consensus/batch1_auxiliary_check.json)：第一批辅助复核； +- [`results/final_summary.json`](results/final_summary.json)：算法结果摘要； +- [`results/diagnostics/`](results/diagnostics/)：局部修正对单对及全体运动对的影响。 + +## 必须外部确认的假设 + +- `lat/lon/h` 确为后天线相位中心； +- `rawHeading` 确为后天线指向前天线； +- heading offset 数值和正负号正确； + +任何一项变化，都必须更新相应约束；RTK 定义变化时需要重新生成 A 并重跑。 diff --git a/code/cross_backend_filter.py b/code/cross_backend_filter.py new file mode 100644 index 0000000..95436ad --- /dev/null +++ b/code/cross_backend_filter.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +"""Keep common A/B edges on which Open3D and small_gicp agree, without using X.""" +import argparse +import json +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + + +def key(meta): + return int(meta[0]), int(meta[1]) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--open3d-pairs", required=True) + parser.add_argument("--small-pairs", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--audit") + parser.add_argument("--max-translation", type=float, default=0.05) + parser.add_argument("--max-rotation", type=float, default=0.50) + parser.add_argument("--min-pairs", type=int, default=25) + args = parser.parse_args() + + with np.load(args.open3d_pairs, allow_pickle=False) as source: + open_a = np.asarray(source["A"], float) + open_b = np.asarray(source["B"], float) + open_meta = np.asarray(source["meta"], float) + station_times = np.asarray(source["station_times"]) + rtk_dt = np.asarray(source["rtk_nearest_dt_s"]) + with np.load(args.small_pairs, allow_pickle=False) as source: + small = {key(meta): np.asarray(b, float) + for meta, b in zip(source["meta"], source["B"])} + + keep, audit = [], [] + for meta, b_open in zip(open_meta, open_b): + edge = key(meta) + if edge not in small: + audit.append({"i": edge[0], "j": edge[1], "accepted": False, + "reason": "not_in_small_gicp_refined"}) + keep.append(False) + continue + delta = np.linalg.inv(b_open) @ small[edge] + translation = float(np.linalg.norm(delta[:3, 3])) + rotation = float(np.rad2deg(Rotation.from_matrix(delta[:3, :3]).magnitude())) + accepted = translation <= args.max_translation and rotation <= args.max_rotation + keep.append(accepted) + audit.append({ + "i": edge[0], "j": edge[1], + "open3d_small_translation_m": translation, + "open3d_small_rotation_deg": rotation, + "accepted": accepted, + "reason": "" if accepted else "backend_disagreement", + }) + keep = np.asarray(keep, bool) + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + output, A=open_a[keep], B=open_b[keep], meta=open_meta[keep], + station_times=station_times, rtk_nearest_dt_s=rtk_dt, + backend=np.asarray("open3d_gicp_cross_backend_consensus"), + ) + audit_path = Path(args.audit or output.with_suffix(".consensus.json")) + audit_path.write_text(json.dumps({ + "selection_is_X_independent": True, + "B_source": "Open3D; small_gicp is used only as an agreement gate", + "max_translation_m": args.max_translation, + "max_rotation_deg": args.max_rotation, + "input_open3d_pairs": len(open_b), + "accepted_pairs": int(np.count_nonzero(keep)), + "pairs": audit, + }, ensure_ascii=False, indent=2), encoding="utf-8") + if np.count_nonzero(keep) < args.min_pairs: + raise RuntimeError(f"only {np.count_nonzero(keep)} consensus pairs") + print(json.dumps({"accepted_pairs": int(np.count_nonzero(keep)), + "output": str(output.resolve()), "audit": str(audit_path.resolve())}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/finalize_consensus.py b/code/finalize_consensus.py new file mode 100644 index 0000000..c38f037 --- /dev/null +++ b/code/finalize_consensus.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""Publish the cross-backend-consensus result as the recommended deliverable.""" +import argparse +import json +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--consensus-extrinsic", required=True) + parser.add_argument("--consensus-check", required=True) + parser.add_argument("--open3d-extrinsic", required=True) + parser.add_argument("--small-extrinsic", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--summary", required=True) + args = parser.parse_args() + consensus = json.loads(Path(args.consensus_extrinsic).read_text(encoding="utf-8-sig")) + check = json.loads(Path(args.consensus_check).read_text(encoding="utf-8-sig")) + open3d = json.loads(Path(args.open3d_extrinsic).read_text(encoding="utf-8-sig")) + small = json.loads(Path(args.small_extrinsic).read_text(encoding="utf-8-sig")) + summary = { + "recommended_method": "Open3D B gated by Open3D-small_gicp cross-backend agreement", + "selection_is_X_independent": True, + "second_batch_role": "estimation (dense RTK)", + "first_batch_role": "auxiliary check only (sparse RTK)", + "consensus": { + "translation_m": consensus["translation_m"], + "rotation_rpy_deg_xyz": consensus["rotation_rpy_deg_xyz"], + "estimation": consensus["estimation"]["residuals"], + "bootstrap_std": consensus["bootstrap"]["std"], + "batch1_auxiliary": check["metrics"], + }, + "separate_backend_results": { + "open3d_gicp": { + "translation_m": open3d["translation_m"], + "rotation_rpy_deg_xyz": open3d["rotation_rpy_deg_xyz"], + }, + "small_gicp": { + "translation_m": small["translation_m"], + "rotation_rpy_deg_xyz": small["rotation_rpy_deg_xyz"], + }, + }, + "warning": "AX rotation RMS remains about one degree; this is not centimetre-grade absolute certification.", + } + published = dict(consensus) + published["selection"] = { + "method": summary["recommended_method"], + "selection_is_X_independent": True, + "consensus_pair_threshold": "Open3D-small_gicp B delta <= 0.05 m and <= 0.50 deg", + "warning": summary["warning"], + } + Path(args.output).write_text(json.dumps(published, ensure_ascii=False, indent=2), encoding="utf-8") + Path(args.summary).write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(summary, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/refine_pairs.py b/code/refine_pairs.py new file mode 100644 index 0000000..f460d36 --- /dev/null +++ b/code/refine_pairs.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""X-independent second-stage filter for stationary A/B pairs.""" +import argparse +import json +from pathlib import Path + +import numpy as np + +from rigorous_calibration import read_pairs, rotation_angle_deg + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--pairs", required=True) + parser.add_argument("--quality-json", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--audit") + parser.add_argument("--min-pairs", type=int, default=25) + parser.add_argument("--min-inlier-ratio", type=float, default=0.70) + parser.add_argument("--max-inlier-rmse", type=float, default=0.13) + parser.add_argument("--max-rotation-invariant-error", type=float, default=0.75) + parser.add_argument("--reverse-translation-tolerance", type=float, default=0.05) + parser.add_argument("--reverse-rotation-tolerance", type=float, default=0.50) + args = parser.parse_args() + + a_array, b_array, meta, _ = read_pairs(args.pairs) + quality = json.loads(Path(args.quality_json).read_text(encoding="utf-8-sig")) + reports = {(int(item["i"]), int(item["j"])): item for item in quality["pairs"]} + keep, audit = [], [] + for a_ij, b_ij, item_meta in zip(a_array, b_array, meta): + key = (int(item_meta[0]), int(item_meta[1])) + report = reports[key] + heldout = report["heldout_symmetric"] + reverse = report["forward_reverse"] + invariant = abs(rotation_angle_deg(a_ij[:3, :3]) - rotation_angle_deg(b_ij[:3, :3])) + reasons = [] + if heldout["inlier_ratio"] < args.min_inlier_ratio: + reasons.append("overlap_ratio") + if heldout["inlier_rmse_m"] is None or heldout["inlier_rmse_m"] > args.max_inlier_rmse: + reasons.append("heldout_rmse") + if invariant > args.max_rotation_invariant_error: + reasons.append("rotation_conjugacy_invariant") + if reverse["translation_m"] > args.reverse_translation_tolerance: + reasons.append("forward_reverse_translation") + if reverse["rotation_deg"] > args.reverse_rotation_tolerance: + reasons.append("forward_reverse_rotation") + accepted = not reasons + keep.append(accepted) + audit.append({ + "i": key[0], "j": key[1], "heldout_inlier_ratio": heldout["inlier_ratio"], + "heldout_inlier_rmse_m": heldout["inlier_rmse_m"], + "rotation_invariant_error_deg": invariant, + "reverse_translation_m": reverse["translation_m"], + "reverse_rotation_deg": reverse["rotation_deg"], + "accepted": accepted, "rejection_reasons": reasons, + }) + keep = np.asarray(keep, bool) + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + with np.load(args.pairs, allow_pickle=False) as source: + np.savez_compressed( + output, A=a_array[keep], B=b_array[keep], meta=meta[keep], + station_times=np.asarray(source["station_times"]), + rtk_nearest_dt_s=np.asarray(source["rtk_nearest_dt_s"]), + backend=np.asarray(source["backend"]), + ) + audit_path = Path(args.audit or output.with_suffix(".refinement.json")) + audit_path.write_text(json.dumps({ + "selection_is_X_independent": True, + "criteria": { + "min_inlier_ratio": args.min_inlier_ratio, + "max_inlier_rmse_m": args.max_inlier_rmse, + "max_rotation_invariant_error_deg": args.max_rotation_invariant_error, + "reverse_translation_tolerance_m": args.reverse_translation_tolerance, + "reverse_rotation_tolerance_deg": args.reverse_rotation_tolerance, + }, + "input_pairs": len(keep), "accepted_pairs": int(np.count_nonzero(keep)), + "pairs": audit, + }, ensure_ascii=False, indent=2), encoding="utf-8") + if np.count_nonzero(keep) < args.min_pairs: + raise RuntimeError(f"only {np.count_nonzero(keep)} refined pairs; need {args.min_pairs}") + print(json.dumps({"input_pairs": len(keep), "accepted_pairs": int(np.count_nonzero(keep)), + "output": str(output.resolve()), "audit": str(audit_path.resolve())}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/rigorous_calibration.py b/code/rigorous_calibration.py new file mode 100644 index 0000000..4732b0d --- /dev/null +++ b/code/rigorous_calibration.py @@ -0,0 +1,769 @@ +#!/usr/bin/env python3 +"""Rigorous stationary LiDAR / dual-antenna RTK hand-eye calibration. + +Convention: T_A_B maps points from frame B into frame A. +X = T_body_lidar, A_ij = T_W_Bi^-1 T_W_Bj, B_ij = T_Li_Lj, +therefore A_ij X = X B_ij. Raw sensor-frame points_raw are used. +""" +from __future__ import annotations + +import argparse +import csv +import json +import math +import time +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +from scipy.optimize import least_squares +from scipy.spatial import cKDTree + + +def skew(v): + x, y, z = v + return np.array([[0.0, -z, y], [z, 0.0, -x], [-y, x, 0.0]]) + + +def so3_exp(v): + angle = float(np.linalg.norm(v)) + if angle < 1e-12: + return np.eye(3) + skew(v) + k = skew(np.asarray(v, float) / angle) + return np.eye(3) + math.sin(angle) * k + (1.0 - math.cos(angle)) * k @ k + + +def so3_log(rotation): + cosine = float(np.clip((np.trace(rotation) - 1.0) / 2.0, -1.0, 1.0)) + angle = math.acos(cosine) + vee = np.array([ + rotation[2, 1] - rotation[1, 2], + rotation[0, 2] - rotation[2, 0], + rotation[1, 0] - rotation[0, 1], + ]) + if angle < 1e-9: + return vee / 2.0 + if abs(math.pi - angle) < 1e-5: + values, vectors = np.linalg.eigh((rotation + np.eye(3)) / 2.0) + return vectors[:, int(np.argmax(values))] * angle + return vee * angle / (2.0 * math.sin(angle)) + + +def quat_to_rotation(q): + x, y, z, w = np.asarray(q, float) / np.linalg.norm(q) + return np.array([ + [1-2*(y*y+z*z), 2*(x*y-z*w), 2*(x*z+y*w)], + [2*(x*y+z*w), 1-2*(x*x+z*z), 2*(y*z-x*w)], + [2*(x*z-y*w), 2*(y*z+x*w), 1-2*(x*x+y*y)], + ]) + + +def rotation_to_quat(rotation): + from scipy.spatial.transform import Rotation + return Rotation.from_matrix(rotation).as_quat() + + +def rpy_deg(rotation): + from scipy.spatial.transform import Rotation + return Rotation.from_matrix(rotation).as_euler("xyz", degrees=True).tolist() + + +def make_transform(translation, rotation): + transform = np.eye(4) + transform[:3, :3] = rotation + transform[:3, 3] = translation + return transform + + +def params_transform(params): + return make_transform(params[:3], so3_exp(params[3:])) + + +def inverse_transform(transform): + answer = np.eye(4) + answer[:3, :3] = transform[:3, :3].T + answer[:3, 3] = -answer[:3, :3] @ transform[:3, 3] + return answer + + +def transform_points(points, transform): + return points @ transform[:3, :3].T + transform[:3, 3] + + +def rotation_angle_deg(rotation): + return math.degrees(np.linalg.norm(so3_log(rotation))) + + +@dataclass +class PoseSeries: + time: np.ndarray + transforms: np.ndarray + + +def read_poses(path): + timestamps, transforms = [], [] + with Path(path).open(encoding="utf-8-sig", newline="") as stream: + reader = csv.DictReader(stream) + required = ("time", "x", "y", "z", "qx", "qy", "qz", "qw") + missing = [key for key in required if key not in (reader.fieldnames or [])] + if missing: + raise ValueError(f"{path}: missing pose fields {missing}") + for row in reader: + timestamps.append(float(row["time"])) + translation = np.array([float(row[k]) for k in ("x", "y", "z")]) + quaternion = np.array([float(row[k]) for k in ("qx", "qy", "qz", "qw")]) + transforms.append(make_transform(translation, quat_to_rotation(quaternion))) + order = np.argsort(timestamps) + return PoseSeries(np.asarray(timestamps)[order], np.asarray(transforms)[order]) + + +def nearest_pose(series, timestamp): + index = int(np.argmin(np.abs(series.time - timestamp))) + return series.transforms[index], float(abs(series.time[index] - timestamp)) + + +def npz_files(root): + files = sorted(Path(root).rglob("*.npz")) + if not files: + raise FileNotFoundError(f"no NPZ files under {root}") + return files + + +def load_npz_xyz(path, min_range=1.0, max_range=50.0): + with np.load(path, allow_pickle=False) as data: + if "points_raw" not in data: + raise ValueError(f"{path}: points_raw is required; cart-frame points are forbidden") + raw = np.asarray(data["points_raw"], dtype=np.float64) + timestamp = float(np.ravel(data["unix_time_ns"])[0]) / 1e9 + counter = int(np.ravel(data["frame_counter"])[0]) + distance = raw[:, 0] * 0.001 + azimuth = np.deg2rad(raw[:, 1]) + altitude = np.deg2rad(raw[:, 2]) + valid = ( + np.isfinite(distance + azimuth + altitude) + & (distance >= min_range) + & (distance <= max_range) + ) + distance, azimuth, altitude = distance[valid], azimuth[valid], altitude[valid] + xyz = np.column_stack(( + distance * np.cos(altitude) * np.cos(azimuth), + distance * np.cos(altitude) * np.sin(azimuth), + distance * np.sin(altitude), + )) + return timestamp, counter, xyz + + +def load_stations(root, min_range, max_range, z_min=None, z_max=None): + stations = [] + for path in npz_files(root): + timestamp, counter, xyz = load_npz_xyz(path, min_range, max_range) + if z_min is not None: + xyz = xyz[(xyz[:, 2] >= z_min) & (xyz[:, 2] <= z_max)] + stations.append((timestamp, counter, path, xyz)) + stations.sort(key=lambda item: item[0]) + return stations + + +def split_holdout(points, fraction, phase): + stride = max(int(round(1.0 / fraction)), 2) + index = np.arange(len(points)) + holdout = ((index + phase) % stride) == 0 + return points[~holdout], points[holdout] + + +def make_o3d_cloud(points, voxel): + import open3d as o3d + cloud = o3d.geometry.PointCloud() + cloud.points = o3d.utility.Vector3dVector(np.asarray(points, float)) + return cloud.voxel_down_sample(voxel) + + +def align_open3d(target, source, initial, voxels, correspondences, iterations): + import open3d as o3d + registration = o3d.pipelines.registration + estimate = registration.TransformationEstimationForGeneralizedICP() + criteria = registration.ICPConvergenceCriteria(max_iteration=iterations) + transform, stages = np.asarray(initial, float), [] + final_target = final_source = final_answer = None + started = time.perf_counter() + for voxel, correspondence in zip(voxels, correspondences): + target_cloud = make_o3d_cloud(target, voxel) + source_cloud = make_o3d_cloud(source, voxel) + answer = registration.registration_generalized_icp( + source_cloud, target_cloud, correspondence, transform, estimate, criteria + ) + transform = np.asarray(answer.transformation, float) + stages.append({ + "voxel_m": voxel, + "max_correspondence_m": correspondence, + "fitness": float(answer.fitness), + "inlier_rmse_m": float(answer.inlier_rmse), + "target_points": len(target_cloud.points), + "source_points": len(source_cloud.points), + }) + final_target, final_source, final_answer = target_cloud, source_cloud, answer + information = registration.get_information_matrix_from_point_clouds( + final_source, final_target, correspondences[-1], transform + ) + inliers = int(round(float(final_answer.fitness) * len(final_source.points))) + return { + "transform": transform, + "hessian": np.asarray(information, float), + "converged": None, + "iterations": None, + "num_inliers": inliers, + "objective": float(final_answer.inlier_rmse ** 2 * max(inliers, 1)), + "elapsed_sec": time.perf_counter() - started, + "stages": stages, + } + + +def align_small_gicp(target, source, initial, voxels, correspondences, iterations, threads): + import small_gicp + transform, stages, result = np.asarray(initial, float), [], None + started = time.perf_counter() + for voxel, correspondence in zip(voxels, correspondences): + result = small_gicp.align( + np.ascontiguousarray(target), + np.ascontiguousarray(source), + transform, + registration_type="GICP", + downsampling_resolution=voxel, + max_correspondence_distance=correspondence, + num_threads=threads, + max_iterations=iterations, + rotation_epsilon=math.radians(0.005), + translation_epsilon=0.0005, + verbose=False, + ) + transform = np.asarray(result.T_target_source, float) + stages.append({ + "voxel_m": voxel, + "max_correspondence_m": correspondence, + "converged": bool(result.converged), + "iterations": int(result.iterations), + "num_inliers": int(result.num_inliers), + "objective": float(result.error), + }) + return { + "transform": transform, + "hessian": np.asarray(result.H, float), + "converged": bool(result.converged), + "iterations": int(result.iterations), + "num_inliers": int(result.num_inliers), + "objective": float(result.error), + "elapsed_sec": time.perf_counter() - started, + "stages": stages, + } + + +def align_backend(backend, target, source, initial, args): + if backend == "open3d": + return align_open3d( + target, source, initial, args.voxels, args.correspondences, args.iterations + ) + return align_small_gicp( + target, source, initial, args.voxels, args.correspondences, + args.iterations, args.threads + ) + + +def symmetric_heldout_metrics(target_fit, target_holdout, source_fit, source_holdout, + transform, threshold): + transformed_source_fit = transform_points(source_fit, transform) + transformed_source_holdout = transform_points(source_holdout, transform) + forward = cKDTree(target_fit).query(transformed_source_holdout, workers=-1)[0] + reverse = cKDTree(transformed_source_fit).query(target_holdout, workers=-1)[0] + distances = np.concatenate((forward, reverse)) + inliers = distances[distances <= threshold] + return { + "evaluated": int(len(distances)), + "inliers": int(len(inliers)), + "inlier_ratio": float(len(inliers) / max(len(distances), 1)), + "inlier_rmse_m": float(np.sqrt(np.mean(inliers**2))) if len(inliers) else None, + "median_m": float(np.median(distances)), + "p90_m": float(np.quantile(distances, 0.90)), + "p95_m": float(np.quantile(distances, 0.95)), + } + + +def hessian_metrics(hessian, characteristic_length=10.0): + hessian = 0.5 * (np.asarray(hessian, float) + np.asarray(hessian, float).T) + scale = np.diag([1.0 / characteristic_length] * 3 + [1.0] * 3) + scaled = scale.T @ hessian @ scale + values, vectors = np.linalg.eigh(scaled) + largest = max(float(np.max(np.abs(values))), np.finfo(float).eps) + positive = values[values > largest * 1e-9] + condition = float(positive[-1] / positive[0]) if len(positive) else float("inf") + return { + "native_order": ["rx_rad", "ry_rad", "rz_rad", "tx_m", "ty_m", "tz_m"], + "scaled_eigenvalues": values.tolist(), + "effective_rank": int(len(positive)), + "scaled_condition_number": condition, + "weakest_scaled_direction": vectors[:, int(np.argmin(values))].tolist(), + } + + +def transform_difference(reference, candidate): + delta = inverse_transform(reference) @ candidate + return { + "translation_m": float(np.linalg.norm(delta[:3, 3])), + "rotation_deg": rotation_angle_deg(delta[:3, :3]), + } + + +def loop_metrics(transforms): + loops = [] + for (i, j), b_ij in transforms.items(): + for (j2, k), b_jk in transforms.items(): + if j2 != j or (i, k) not in transforms: + continue + loops.append(transform_difference(transforms[(i, k)], b_ij @ b_jk)) + if not loops: + return {"count": 0} + translation = np.array([item["translation_m"] for item in loops]) + rotation = np.array([item["rotation_deg"] for item in loops]) + return { + "count": len(loops), + "translation_rms_m": float(np.sqrt(np.mean(translation**2))), + "translation_p95_m": float(np.quantile(translation, 0.95)), + "rotation_rms_deg": float(np.sqrt(np.mean(rotation**2))), + "rotation_p95_deg": float(np.quantile(rotation, 0.95)), + } + + +def cmd_ground(args): + stations = load_stations(args.frames, args.min_range, args.max_range) + rows = [] + for timestamp, counter, _, xyz in stations: + roi = xyz[(xyz[:, 2] >= args.z_min) & (xyz[:, 2] <= args.z_max)] + if len(roi) < args.min_inliers: + continue + cloud = make_o3d_cloud(roi, args.voxel) + plane, indexes = cloud.segment_plane( + args.distance_threshold, 3, args.ransac_iterations + ) + normal = np.asarray(plane[:3], float) + norm = np.linalg.norm(normal) + normal, distance = normal / norm, float(plane[3] / norm) + if distance < 0: + normal, distance = -normal, -distance + points = np.asarray(cloud.points)[indexes] + rms = float(np.sqrt(np.mean((points @ normal + distance) ** 2))) + if len(indexes) >= args.min_inliers and rms <= args.max_rms: + rows.append([timestamp, *normal, distance, len(indexes), rms, counter]) + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + with output.open("w", encoding="utf-8", newline="") as stream: + writer = csv.writer(stream) + writer.writerow(["time", "nx", "ny", "nz", "d", "inliers", "rms_m", "frame_counter"]) + writer.writerows(rows) + print(json.dumps({"planes": len(rows), "output": str(output.resolve())}, indent=2)) + + +def cmd_pairs(args): + if len(args.voxels) != len(args.correspondences): + raise ValueError("--voxels and --correspondences must have equal lengths") + stations = load_stations( + args.frames, args.min_range, args.max_range, args.z_min, args.z_max + ) + body = read_poses(args.body) + if len(stations) < args.min_stations: + raise ValueError(f"need at least {args.min_stations} stations, got {len(stations)}") + body_poses, body_dt = [], [] + for timestamp, _, _, xyz in stations: + if len(xyz) < args.min_roi_points: + raise ValueError(f"station at {timestamp} has only {len(xyz)} ROI points") + pose, dt = nearest_pose(body, timestamp + args.time_offset) + body_poses.append(pose) + body_dt.append(dt) + body_poses = np.asarray(body_poses) + split = [split_holdout(station[3], args.holdout_fraction, i) + for i, station in enumerate(stations)] + rng = np.random.default_rng(args.seed) + accepted_a, accepted_b, accepted_meta, reports = [], [], [], [] + accepted_transforms = {} + for i in range(len(stations)): + for j in range(i + args.min_gap, min(len(stations), i + args.max_gap + 1)): + a_ij = inverse_transform(body_poses[i]) @ body_poses[j] + translation = float(np.linalg.norm(a_ij[:2, 3])) + rotation = rotation_angle_deg(a_ij[:3, :3]) + if translation < args.min_translation and rotation < args.min_rotation: + continue + initial_b = a_ij.copy() # X0=I; no measured extrinsic. + target_fit, target_holdout = split[i] + source_fit, source_holdout = split[j] + forward = align_backend(args.backend, target_fit, source_fit, initial_b, args) + heldout = symmetric_heldout_metrics( + target_fit, target_holdout, source_fit, source_holdout, + forward["transform"], args.evaluation_distance + ) + hessian = hessian_metrics(forward["hessian"]) + reverse_answer = align_backend( + args.backend, source_fit, target_fit, inverse_transform(initial_b), args + ) + reverse = transform_difference( + forward["transform"], inverse_transform(reverse_answer["transform"]) + ) + multistart = [] + for _ in range(args.multistart): + perturb = np.r_[ + rng.normal(0.0, args.multistart_translation_sigma, 3), + np.deg2rad(rng.normal(0.0, args.multistart_rotation_sigma, 3)), + ] + candidate = align_backend( + args.backend, target_fit, source_fit, + params_transform(perturb) @ initial_b, args + ) + multistart.append(transform_difference(forward["transform"], candidate["transform"])) + stable = [ + item["translation_m"] <= args.multistart_translation_tolerance + and item["rotation_deg"] <= args.multistart_rotation_tolerance + for item in multistart + ] + success_rate = float(np.mean(stable)) if stable else 1.0 + reasons = [] + if forward["converged"] is False: + reasons.append("backend_not_converged") + if heldout["inlier_ratio"] < args.min_inlier_ratio: + reasons.append("heldout_inlier_ratio") + if heldout["inlier_rmse_m"] is None or heldout["inlier_rmse_m"] > args.max_inlier_rmse: + reasons.append("heldout_inlier_rmse") + if hessian["effective_rank"] < 6: + reasons.append("hessian_rank") + if hessian["scaled_condition_number"] > args.max_hessian_condition: + reasons.append("hessian_condition") + if reverse["translation_m"] > args.reverse_translation_tolerance: + reasons.append("forward_reverse_translation") + if reverse["rotation_deg"] > args.reverse_rotation_tolerance: + reasons.append("forward_reverse_rotation") + if success_rate < args.min_multistart_success: + reasons.append("multistart_instability") + accepted = not reasons + report = { + "i": i, "j": j, + "lidar_time_i": stations[i][0], "lidar_time_j": stations[j][0], + "frame_counter_i": stations[i][1], "frame_counter_j": stations[j][1], + "rtk_translation_m": translation, "rtk_rotation_deg": rotation, + "nearest_rtk_dt_i_s": body_dt[i], "nearest_rtk_dt_j_s": body_dt[j], + "initial_B_source": "X0=identity; B0=A (no measured extrinsic)", + "B_ij_4x4": forward["transform"].tolist(), + "backend": args.backend, "backend_converged": forward["converged"], + "backend_iterations": forward["iterations"], + "backend_num_inliers": forward["num_inliers"], + "backend_objective": forward["objective"], + "backend_elapsed_sec": forward["elapsed_sec"], + "multiscale_stages": forward["stages"], + "heldout_symmetric": heldout, "hessian": hessian, + "forward_reverse": reverse, + "multistart": {"runs": len(multistart), "success_rate": success_rate, + "deltas": multistart}, + "accepted": accepted, "rejection_reasons": reasons, + } + reports.append(report) + print(f"{args.backend} {i:02d}->{j:02d} rmse={heldout['inlier_rmse_m']} " + f"ratio={heldout['inlier_ratio']:.3f} accepted={accepted}") + if accepted: + accepted_a.append(a_ij) + accepted_b.append(forward["transform"]) + accepted_meta.append([i, j, stations[i][0], stations[j][0]]) + accepted_transforms[(i, j)] = forward["transform"] + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + output, A=np.asarray(accepted_a), B=np.asarray(accepted_b), + meta=np.asarray(accepted_meta), + station_times=np.asarray([item[0] for item in stations]), + rtk_nearest_dt_s=np.asarray(body_dt), backend=np.asarray(args.backend), + ) + quality = { + "schema_version": 2, + "backend": args.backend, + "transform_convention": "B_ij=T_Li_Lj maps station j points into station i", + "raw_point_field": "points_raw", + "measured_extrinsic_used_as_initial": False, + "stations": len(stations), "candidate_pairs": len(reports), + "accepted_pairs": len(accepted_a), + "parameters": vars(args), + "accepted_loop_closure": loop_metrics(accepted_transforms), + "pairs": reports, + } + quality["parameters"].pop("func", None) + quality_path = Path(args.quality_json or output.with_suffix(".quality.json")) + quality_path.write_text(json.dumps(quality, ensure_ascii=False, indent=2), encoding="utf-8") + csv_path = Path(args.quality_csv or output.with_suffix(".quality.csv")) + with csv_path.open("w", encoding="utf-8", newline="") as stream: + fields = ["i", "j", "rtk_translation_m", "rtk_rotation_deg", + "heldout_inlier_ratio", "heldout_inlier_rmse_m", + "hessian_rank", "hessian_condition", "reverse_translation_m", + "reverse_rotation_deg", "multistart_success_rate", "accepted", + "rejection_reasons"] + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + for item in reports: + writer.writerow({ + "i": item["i"], "j": item["j"], + "rtk_translation_m": item["rtk_translation_m"], + "rtk_rotation_deg": item["rtk_rotation_deg"], + "heldout_inlier_ratio": item["heldout_symmetric"]["inlier_ratio"], + "heldout_inlier_rmse_m": item["heldout_symmetric"]["inlier_rmse_m"], + "hessian_rank": item["hessian"]["effective_rank"], + "hessian_condition": item["hessian"]["scaled_condition_number"], + "reverse_translation_m": item["forward_reverse"]["translation_m"], + "reverse_rotation_deg": item["forward_reverse"]["rotation_deg"], + "multistart_success_rate": item["multistart"]["success_rate"], + "accepted": item["accepted"], + "rejection_reasons": ";".join(item["rejection_reasons"]), + }) + if len(accepted_a) < args.min_pairs: + raise RuntimeError(f"only {len(accepted_a)} accepted pairs; need {args.min_pairs}") + print(json.dumps({ + "backend": args.backend, "stations": len(stations), + "candidate_pairs": len(reports), "accepted_pairs": len(accepted_a), + "output": str(output.resolve()), "quality_json": str(quality_path.resolve()), + "loop": quality["accepted_loop_closure"], + }, indent=2)) + + +def read_planes(path): + planes = [] + with Path(path).open(encoding="utf-8-sig", newline="") as stream: + for row in csv.DictReader(stream): + normal = np.array([float(row[k]) for k in ("nx", "ny", "nz")]) + norm = np.linalg.norm(normal) + normal, distance = normal / norm, float(row["d"]) / norm + if distance < 0: + normal, distance = -normal, -distance + planes.append([*normal, distance]) + return np.asarray(planes) + + +def read_pairs(path): + with np.load(path, allow_pickle=False) as data: + return (np.asarray(data["A"], float), np.asarray(data["B"], float), + np.asarray(data["meta"], float), len(data["station_times"])) + + +def calibration_residual(params, a_array, b_array, planes, args): + x = params_transform(params) + values = [] + for a_ij, b_ij in zip(a_array, b_array): + error = inverse_transform(a_ij @ x) @ x @ b_ij + values.extend((error[:3, 3] / args.translation_sigma).tolist()) + values.extend((so3_log(error[:3, :3]) / math.radians(args.rotation_sigma)).tolist()) + body_up = np.array([0.0, 0.0, 1.0]) + for plane in planes: + normal_body = x[:3, :3] @ plane[:3] + values.extend((np.cross(normal_body, body_up) / args.plane_normal_sigma).tolist()) + body_distance = plane[3] - float(normal_body @ x[:3, 3]) + values.append((body_distance - args.body_height) / args.plane_height_sigma) + return np.asarray(values) + + +def pair_metrics(a_array, b_array, x): + translation, rotation, rows = [], [], [] + for index, (a_ij, b_ij) in enumerate(zip(a_array, b_array)): + predicted = inverse_transform(x) @ a_ij @ x + delta = inverse_transform(b_ij) @ predicted + t = float(np.linalg.norm(delta[:3, 3])) + r = rotation_angle_deg(delta[:3, :3]) + translation.append(t); rotation.append(r) + rows.append({"pair_index": index, "translation_m": t, "rotation_deg": r}) + translation, rotation = np.asarray(translation), np.asarray(rotation) + def stats(values): + return { + "rms": float(np.sqrt(np.mean(values**2))), + "median": float(np.median(values)), + "p90": float(np.quantile(values, 0.90)), + "p95": float(np.quantile(values, 0.95)), + "max": float(np.max(values)), + } + return {"pairs": len(rows), "translation_m": stats(translation), + "rotation_deg": stats(rotation), "per_pair": rows} + + +def solve_extrinsic(a_array, b_array, planes, args): + rng = np.random.default_rng(args.seed) + starts = [np.zeros(6)] + for _ in range(args.solver_multistart - 1): + starts.append(np.r_[ + rng.normal(0.0, args.start_translation_sigma, 3), + np.deg2rad(rng.normal(0.0, args.start_rotation_sigma, 3)), + ]) + candidates = [] + lower = np.r_[[-5.0] * 3, [-math.pi] * 3] + upper = np.r_[[5.0] * 3, [math.pi] * 3] + for start in starts: + answer = least_squares( + calibration_residual, np.clip(start, lower, upper), + args=(a_array, b_array, planes, args), + bounds=(lower, upper), loss="huber", f_scale=1.5, + x_scale="jac", max_nfev=args.max_nfev, + ) + candidates.append(answer) + best = min(candidates, key=lambda item: item.cost) + return best, candidates + + +def cmd_calibrate(args): + a_array, b_array, meta, stations = read_pairs(args.pairs) + planes = read_planes(args.ground_planes) + best, candidates = solve_extrinsic(a_array, b_array, planes, args) + x = params_transform(best.x) + residual = calibration_residual(best.x, a_array, b_array, planes, args) + absolute = np.abs(residual) + weights = np.ones_like(residual) + weights[absolute > 1.5] = 1.5 / absolute[absolute > 1.5] + weighted_jacobian = best.jac * np.sqrt(weights)[:, None] + singular = np.linalg.svd(weighted_jacobian, compute_uv=False) + condition = float(singular[0] / max(singular[-1], 1e-15)) + dof = max(len(residual) - 6, 1) + covariance = np.linalg.pinv(weighted_jacobian.T @ weighted_jacobian) * float( + np.sum(weights * residual**2) / dof + ) + sigma = np.sqrt(np.maximum(np.diag(covariance), 0.0)) + candidate_summary = [] + for item in candidates: + candidate_x = params_transform(item.x) + candidate_summary.append({ + "cost": float(item.cost), "success": bool(item.success), + **transform_difference(x, candidate_x), + }) + bootstrap = [] + rng = np.random.default_rng(args.seed + 1) + for _ in range(args.bootstrap): + indexes = rng.integers(0, len(a_array), len(a_array)) + answer = least_squares( + calibration_residual, best.x, + args=(a_array[indexes], b_array[indexes], planes, args), + loss="huber", f_scale=1.5, x_scale="jac", max_nfev=args.max_nfev, + ) + bootstrap.append(np.r_[answer.x[:3], rpy_deg(so3_exp(answer.x[3:]))]) + bootstrap = np.asarray(bootstrap) + result = { + "schema_version": 2, + "success": bool(best.success), + "message": best.message, + "convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame", + "equation": "A_ij X = X B_ij", + "measured_extrinsic_used_as_initial": False, + "translation_m": x[:3, 3].tolist(), + "rotation_rpy_deg_xyz": rpy_deg(x[:3, :3]), + "quaternion_xyzw": rotation_to_quat(x[:3, :3]).tolist(), + "matrix_4x4": x.tolist(), + "estimation": {"stations": stations, "pairs": len(a_array), + "residuals": pair_metrics(a_array, b_array, x)}, + "ground": { + "planes": len(planes), + "body_origin_height_above_ground_m": args.body_height, + "formula": "d_lidar - (R_X n_lidar)^T t_X - body_height", + }, + "linearized_one_sigma": { + "translation_m": sigma[:3].tolist(), + "rotation_deg": np.rad2deg(sigma[3:]).tolist(), + "warning": "conditional local estimate; bootstrap is the primary stability check", + }, + "weighted_jacobian_condition_number": condition, + "solver_multistart": { + "runs": len(candidates), "candidates_relative_to_best": candidate_summary, + }, + "bootstrap": { + "runs": len(bootstrap), + "order": ["x_m", "y_m", "z_m", "roll_deg", "pitch_deg", "yaw_deg"], + "std": np.std(bootstrap, axis=0, ddof=1).tolist() if len(bootstrap) > 1 else None, + "p025": np.quantile(bootstrap, 0.025, axis=0).tolist() if len(bootstrap) else None, + "p975": np.quantile(bootstrap, 0.975, axis=0).tolist() if len(bootstrap) else None, + }, + } + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(result, ensure_ascii=False, indent=2)) + + +def cmd_validate(args): + result = json.loads(Path(args.extrinsic).read_text(encoding="utf-8-sig")) + x = np.asarray(result["matrix_4x4"], float) + a_array, b_array, meta, stations = read_pairs(args.pairs) + metrics = pair_metrics(a_array, b_array, x) + for row, pair_meta in zip(metrics["per_pair"], meta): + row.update({"i": int(pair_meta[0]), "j": int(pair_meta[1])}) + report = { + "role": "auxiliary check only; first-batch RTK is sparse", + "blind_with_respect_to_X": True, + "note": "No AX residual was used to select these pairs", + "stations": stations, "metrics": metrics, + } + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(report, ensure_ascii=False, indent=2)) + + +def build_parser(): + parser = argparse.ArgumentParser(description=__doc__) + commands = parser.add_subparsers(dest="command", required=True) + ground = commands.add_parser("ground") + ground.add_argument("--frames", required=True); ground.add_argument("--output", required=True) + ground.add_argument("--min-range", type=float, default=1.0); ground.add_argument("--max-range", type=float, default=30.0) + ground.add_argument("--z-min", type=float, default=-1.4); ground.add_argument("--z-max", type=float, default=-0.4) + ground.add_argument("--voxel", type=float, default=0.08); ground.add_argument("--distance-threshold", type=float, default=0.025) + ground.add_argument("--ransac-iterations", type=int, default=500); ground.add_argument("--min-inliers", type=int, default=500) + ground.add_argument("--max-rms", type=float, default=0.025); ground.set_defaults(func=cmd_ground) + + pairs = commands.add_parser("pairs") + pairs.add_argument("--backend", choices=["open3d", "small_gicp"], required=True) + pairs.add_argument("--frames", required=True); pairs.add_argument("--body", required=True) + pairs.add_argument("--output", required=True); pairs.add_argument("--quality-json"); pairs.add_argument("--quality-csv") + pairs.add_argument("--time-offset", type=float, default=0.0) + pairs.add_argument("--min-stations", type=int, default=30); pairs.add_argument("--min-pairs", type=int, default=25) + pairs.add_argument("--min-gap", type=int, default=1); pairs.add_argument("--max-gap", type=int, default=5) + pairs.add_argument("--min-translation", type=float, default=0.5); pairs.add_argument("--min-rotation", type=float, default=3.0) + pairs.add_argument("--min-range", type=float, default=2.0); pairs.add_argument("--max-range", type=float, default=50.0) + pairs.add_argument("--z-min", type=float, default=-0.60); pairs.add_argument("--z-max", type=float, default=5.0) + pairs.add_argument("--min-roi-points", type=int, default=1000) + pairs.add_argument("--holdout-fraction", type=float, default=0.20) + pairs.add_argument("--voxels", nargs="+", type=float, default=[0.30, 0.15, 0.08]) + pairs.add_argument("--correspondences", nargs="+", type=float, default=[1.20, 0.50, 0.25]) + pairs.add_argument("--iterations", type=int, default=60); pairs.add_argument("--threads", type=int, default=8) + pairs.add_argument("--evaluation-distance", type=float, default=0.25) + pairs.add_argument("--min-inlier-ratio", type=float, default=0.35); pairs.add_argument("--max-inlier-rmse", type=float, default=0.16) + pairs.add_argument("--max-hessian-condition", type=float, default=1e8) + pairs.add_argument("--reverse-translation-tolerance", type=float, default=0.08) + pairs.add_argument("--reverse-rotation-tolerance", type=float, default=0.50) + pairs.add_argument("--multistart", type=int, default=2) + pairs.add_argument("--multistart-translation-sigma", type=float, default=0.30) + pairs.add_argument("--multistart-rotation-sigma", type=float, default=3.0) + pairs.add_argument("--multistart-translation-tolerance", type=float, default=0.08) + pairs.add_argument("--multistart-rotation-tolerance", type=float, default=0.50) + pairs.add_argument("--min-multistart-success", type=float, default=0.50) + pairs.add_argument("--seed", type=int, default=20260721); pairs.set_defaults(func=cmd_pairs) + + calibrate = commands.add_parser("calibrate") + calibrate.add_argument("--pairs", required=True); calibrate.add_argument("--ground-planes", required=True) + calibrate.add_argument("--output", required=True) + calibrate.add_argument("--translation-sigma", type=float, default=0.05) + calibrate.add_argument("--rotation-sigma", type=float, default=0.5) + calibrate.add_argument("--plane-normal-sigma", type=float, default=0.02) + calibrate.add_argument("--plane-height-sigma", type=float, default=0.03) + calibrate.add_argument("--body-height", type=float, default=0.2335) + calibrate.add_argument("--solver-multistart", type=int, default=12) + calibrate.add_argument("--start-translation-sigma", type=float, default=1.0) + calibrate.add_argument("--start-rotation-sigma", type=float, default=20.0) + calibrate.add_argument("--bootstrap", type=int, default=100) + calibrate.add_argument("--max-nfev", type=int, default=1000) + calibrate.add_argument("--seed", type=int, default=20260721); calibrate.set_defaults(func=cmd_calibrate) + + validate = commands.add_parser("validate") + validate.add_argument("--pairs", required=True); validate.add_argument("--extrinsic", required=True) + validate.add_argument("--output", required=True); validate.set_defaults(func=cmd_validate) + return parser + + +def main(): + args = build_parser().parse_args() + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/code/scan_extrinsic_sensitivity.py b/code/scan_extrinsic_sensitivity.py new file mode 100644 index 0000000..d277d64 --- /dev/null +++ b/code/scan_extrinsic_sensitivity.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python3 +"""Scan body-left RPY corrections locally and validate them over every B pair. + +This command is diagnostic only. It never writes or replaces an extrinsic JSON. +""" +from __future__ import annotations + +import argparse +import csv +import json +import math +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + +from rigorous_calibration import ( + inverse_transform, read_pairs, read_planes, rotation_angle_deg, rpy_deg, +) + + +def statistics(values): + values = np.asarray(values, float) + return { + "rms": float(np.sqrt(np.mean(values ** 2))), + "median": float(np.median(values)), + "p90": float(np.quantile(values, 0.90)), + "p95": float(np.quantile(values, 0.95)), + "max": float(np.max(values)), + } + + +def body_left_rpy(x, rpy_correction_deg): + correction = np.eye(4) + correction[:3, :3] = Rotation.from_euler( + "xyz", np.asarray(rpy_correction_deg, float), degrees=True + ).as_matrix() + return correction @ x + + +def pair_delta(a_ij, b_ij, x): + predicted = inverse_transform(x) @ a_ij @ x + delta = inverse_transform(b_ij) @ predicted + translation = np.asarray(delta[:3, 3], float) + return { + "translation_xyz_m": translation.tolist(), + "translation_xyz_cm": (100.0 * translation).tolist(), + "translation_norm_m": float(np.linalg.norm(translation)), + "rotation_rpy_deg_xyz": rpy_deg(delta[:3, :3]), + "rotation_angle_deg": rotation_angle_deg(delta[:3, :3]), + } + + +def ground_metrics(planes, x, body_height): + if len(planes) == 0: + return None + up = np.array([0.0, 0.0, 1.0]) + tilt_deg, height_m = [], [] + for plane in planes: + normal_body = x[:3, :3] @ plane[:3] + normal_body /= np.linalg.norm(normal_body) + tilt_deg.append(math.degrees(math.atan2( + np.linalg.norm(np.cross(normal_body, up)), + float(np.clip(normal_body @ up, -1.0, 1.0)), + ))) + height_m.append( + float(plane[3] - normal_body @ x[:3, 3] - body_height) + ) + return { + "normal_tilt_deg": statistics(tilt_deg), + "height_residual_m": statistics(height_m), + } + + +def evaluate(label, correction, a_array, b_array, meta, x, pair_index, + translation_scale, rotation_scale, planes, body_height): + candidate_x = body_left_rpy(x, correction) + per_pair = [] + translation, rotation, normalized = [], [], [] + for index, (a_ij, b_ij, pair_meta) in enumerate(zip(a_array, b_array, meta)): + item = pair_delta(a_ij, b_ij, candidate_x) + item.update({ + "pair_index": index, + "i": int(pair_meta[0]), + "j": int(pair_meta[1]), + }) + t = item["translation_norm_m"] + r = item["rotation_angle_deg"] + translation.append(t) + rotation.append(r) + normalized.append(math.hypot(t / translation_scale, r / rotation_scale)) + per_pair.append(item) + return { + "label": label, + "body_left_rpy_correction_deg_xyz": list(map(float, correction)), + "candidate_extrinsic": { + "translation_m": candidate_x[:3, 3].tolist(), + "rotation_rpy_deg_xyz": rpy_deg(candidate_x[:3, :3]), + }, + "all_pairs": { + "count": len(per_pair), + "translation_m": statistics(translation), + "rotation_deg": statistics(rotation), + "normalized_pair_score": statistics(normalized), + "normalized_global_rms": float(np.sqrt(np.mean(np.asarray(normalized) ** 2))), + }, + "selected_pair": per_pair[pair_index], + "ground": ground_metrics(planes, candidate_x, body_height), + "per_pair": per_pair, + } + + +def candidate_grid(pitch_values, roll_values, yaw_values): + answer = [("baseline", (0.0, 0.0, 0.0))] + for pitch in pitch_values: + answer.append((f"pitch_{pitch:+.3f}", (0.0, pitch, 0.0))) + for pitch in (0.0, *pitch_values): + for roll in roll_values: + answer.append(( + f"pitch_{pitch:+.3f}_roll_{roll:+.3f}", + (roll, pitch, 0.0), + )) + for yaw in yaw_values: + answer.append((f"yaw_{yaw:+.3f}_diagnostic", (0.0, 0.0, yaw))) + unique = [] + seen = set() + for label, values in answer: + key = tuple(round(float(value), 12) for value in values) + if key not in seen: + seen.add(key) + unique.append((label, values)) + return unique + + +def z_observability(a_array, x, test_shift_m): + shift = np.eye(4) + shift[2, 3] = test_shift_m + shifted_x = shift @ x + effects = [] + for a_ij in a_array: + before = inverse_transform(x) @ a_ij @ x + after = inverse_transform(shifted_x) @ a_ij @ shifted_x + delta = inverse_transform(before) @ after + effects.append(( + float(np.linalg.norm(delta[:3, 3])), + rotation_angle_deg(delta[:3, :3]), + )) + effects = np.asarray(effects, float) + maximum = np.max(effects, axis=0) + return { + "body_left_z_test_shift_m": test_shift_m, + "max_predicted_motion_change_translation_m": float(maximum[0]), + "max_predicted_motion_change_rotation_deg": float(maximum[1]), + "numerically_unobservable": bool(maximum[0] < 1e-10 and maximum[1] < 1e-10), + "note": "AX pairs cannot determine X.z when every A rotation preserves body Z; use ground/external height constraints.", + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--pairs", required=True) + parser.add_argument("--extrinsic", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--csv") + parser.add_argument("--ground-planes") + parser.add_argument("--pair-index", type=int, default=0) + parser.add_argument("--pitch-values", nargs="+", type=float, default=[0.1, 0.2, 0.3]) + parser.add_argument("--roll-values", nargs="+", type=float, default=[-0.2, -0.1, 0.1]) + parser.add_argument("--yaw-values", nargs="+", type=float, default=[-0.2, 0.2]) + parser.add_argument("--translation-scale", type=float, default=0.05) + parser.add_argument("--rotation-scale", type=float, default=0.5) + parser.add_argument("--body-height", type=float, default=0.2335) + args = parser.parse_args() + + a_array, b_array, meta, stations = read_pairs(args.pairs) + if not 0 <= args.pair_index < len(a_array): + raise IndexError(f"pair-index {args.pair_index} outside [0,{len(a_array)-1}]") + with Path(args.extrinsic).open(encoding="utf-8-sig") as stream: + x = np.asarray(json.load(stream)["matrix_4x4"], float) + planes = read_planes(args.ground_planes) if args.ground_planes else np.empty((0, 4)) + + candidates = [ + evaluate( + label, correction, a_array, b_array, meta, x, args.pair_index, + args.translation_scale, args.rotation_scale, planes, args.body_height, + ) + for label, correction in candidate_grid( + args.pitch_values, args.roll_values, args.yaw_values + ) + ] + baseline = candidates[0] + baseline_scores = np.asarray([ + math.hypot( + item["translation_norm_m"] / args.translation_scale, + item["rotation_angle_deg"] / args.rotation_scale, + ) + for item in baseline["per_pair"] + ]) + base_global = baseline["all_pairs"]["normalized_global_rms"] + for candidate in candidates: + scores = np.asarray([ + math.hypot( + item["translation_norm_m"] / args.translation_scale, + item["rotation_angle_deg"] / args.rotation_scale, + ) + for item in candidate["per_pair"] + ]) + delta = scores - baseline_scores + candidate["comparison_to_baseline"] = { + "normalized_global_rms_change": float( + candidate["all_pairs"]["normalized_global_rms"] - base_global + ), + "improved_pairs": int(np.sum(delta < -1e-12)), + "worsened_pairs": int(np.sum(delta > 1e-12)), + "unchanged_pairs": int(np.sum(np.abs(delta) <= 1e-12)), + "median_per_pair_score_change": float(np.median(delta)), + "global_consistency_signal": bool( + candidate["all_pairs"]["normalized_global_rms"] < base_global + and np.sum(delta < -1e-12) > np.sum(delta > 1e-12) + ), + } + + ranking = sorted( + candidates, + key=lambda item: item["all_pairs"]["normalized_global_rms"], + ) + report = { + "schema_version": 1, + "diagnostic_only": True, + "extrinsic_was_modified": False, + "equation": "delta_ij = B_ij^-1 * (X^-1 * A_ij * X)", + "correction_convention": "X_test = DeltaR_body * X; DeltaR uses fixed body xyz RPY axes", + "component_frame": "delta translation/RPY components are in station-j LiDAR coordinates, not screen axes", + "selection_rule": ( + "Never accept a correction from selected_pair alone. Require improvement over all " + "refined pairs, directional consistency across pairs, acceptable ground constraints, " + "and independent visual review. This script never overwrites X." + ), + "pairs_file": str(Path(args.pairs).resolve()), + "extrinsic_file": str(Path(args.extrinsic).resolve()), + "stations": stations, + "pairs": len(a_array), + "selected_pair_index": args.pair_index, + "selected_pair_stations": [int(meta[args.pair_index, 0]), int(meta[args.pair_index, 1])], + "normalization": { + "translation_scale_m": args.translation_scale, + "rotation_scale_deg": args.rotation_scale, + }, + "z_observability": z_observability(a_array, x, 0.10), + "ranking_by_all_pair_normalized_rms": [ + { + "rank": rank, + "label": item["label"], + "body_left_rpy_correction_deg_xyz": item["body_left_rpy_correction_deg_xyz"], + "normalized_global_rms": item["all_pairs"]["normalized_global_rms"], + **item["comparison_to_baseline"], + } + for rank, item in enumerate(ranking, 1) + ], + "candidates": candidates, + } + + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + csv_path = Path(args.csv) if args.csv else output.with_suffix(".csv") + with csv_path.open("w", encoding="utf-8", newline="") as stream: + fields = [ + "label", "roll_correction_deg", "pitch_correction_deg", "yaw_correction_deg", + "selected_pair_translation_cm", "selected_pair_rotation_deg", + "all_pair_translation_rms_m", "all_pair_rotation_rms_deg", + "normalized_global_rms", "normalized_global_rms_change", + "improved_pairs", "worsened_pairs", "global_consistency_signal", + "ground_normal_tilt_rms_deg", "ground_height_rms_m", + ] + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + for item in candidates: + correction = item["body_left_rpy_correction_deg_xyz"] + ground = item["ground"] + comparison = item["comparison_to_baseline"] + writer.writerow({ + "label": item["label"], + "roll_correction_deg": correction[0], + "pitch_correction_deg": correction[1], + "yaw_correction_deg": correction[2], + "selected_pair_translation_cm": item["selected_pair"]["translation_norm_m"] * 100.0, + "selected_pair_rotation_deg": item["selected_pair"]["rotation_angle_deg"], + "all_pair_translation_rms_m": item["all_pairs"]["translation_m"]["rms"], + "all_pair_rotation_rms_deg": item["all_pairs"]["rotation_deg"]["rms"], + "normalized_global_rms": item["all_pairs"]["normalized_global_rms"], + "normalized_global_rms_change": comparison["normalized_global_rms_change"], + "improved_pairs": comparison["improved_pairs"], + "worsened_pairs": comparison["worsened_pairs"], + "global_consistency_signal": comparison["global_consistency_signal"], + "ground_normal_tilt_rms_deg": None if ground is None else ground["normal_tilt_deg"]["rms"], + "ground_height_rms_m": None if ground is None else ground["height_residual_m"]["rms"], + }) + + print(json.dumps({ + "diagnostic_only": True, + "selected_pair": baseline["selected_pair"], + "z_observability": report["z_observability"], + "top_all_pair_candidates": report["ranking_by_all_pair_normalized_rms"][:8], + "output": str(output.resolve()), + "csv": str(csv_path.resolve()), + }, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/summarize_results.py b/code/summarize_results.py new file mode 100644 index 0000000..acde29d --- /dev/null +++ b/code/summarize_results.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""Build a concise backend comparison and select the recommended result.""" +import argparse +import json +from pathlib import Path + +import numpy as np +from scipy.spatial.transform import Rotation + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--open3d", required=True) + parser.add_argument("--small", required=True) + parser.add_argument("--open3d-quality", required=True) + parser.add_argument("--small-quality", required=True) + parser.add_argument("--open3d-check", required=True) + parser.add_argument("--small-check", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--recommended-output", required=True) + args = parser.parse_args() + + open_result = json.loads(Path(args.open3d).read_text(encoding="utf-8-sig")) + small_result = json.loads(Path(args.small).read_text(encoding="utf-8-sig")) + open_quality = json.loads(Path(args.open3d_quality).read_text(encoding="utf-8-sig")) + small_quality = json.loads(Path(args.small_quality).read_text(encoding="utf-8-sig")) + open_check = json.loads(Path(args.open3d_check).read_text(encoding="utf-8-sig")) + small_check = json.loads(Path(args.small_check).read_text(encoding="utf-8-sig")) + x_open = np.asarray(open_result["matrix_4x4"], float) + x_small = np.asarray(small_result["matrix_4x4"], float) + delta = np.linalg.inv(x_open) @ x_small + + def compact(result, quality, check): + estimate = result["estimation"]["residuals"] + auxiliary = check["metrics"] + return { + "translation_m": result["translation_m"], + "rotation_rpy_deg_xyz": result["rotation_rpy_deg_xyz"], + "estimation_pairs": estimate["pairs"], + "estimation_translation_rms_m": estimate["translation_m"]["rms"], + "estimation_rotation_rms_deg": estimate["rotation_deg"]["rms"], + "bootstrap_std": result["bootstrap"]["std"], + "initial_B_loop_closure": quality["accepted_loop_closure"], + "batch1_auxiliary_pairs": auxiliary["pairs"], + "batch1_auxiliary_translation_rms_m": auxiliary["translation_m"]["rms"], + "batch1_auxiliary_rotation_rms_deg": auxiliary["rotation_deg"]["rms"], + } + + summary = { + "recommended_backend": "open3d_gicp", + "selection_reason": ( + "The two X estimates agree closely; Open3D has lower second-batch AX residual, " + "better B loop closure, and lower first-batch auxiliary residual." + ), + "coordinate_convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame", + "measured_extrinsic_used_as_initial": False, + "second_batch_role": "estimation (dense RTK)", + "first_batch_role": "auxiliary check only (sparse RTK)", + "backend_difference": { + "translation_m": float(np.linalg.norm(delta[:3, 3])), + "rotation_deg": float(np.rad2deg(Rotation.from_matrix(delta[:3, :3]).magnitude())), + }, + "open3d_gicp": compact(open_result, open_quality, open_check), + "small_gicp": compact(small_result, small_quality, small_check), + "important_limit": ( + "Backend agreement is strong, but AX rotation RMS remains about one degree. " + "This is not a centimetre-grade absolute certification." + ), + } + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + recommended = dict(open_result) + recommended["selection"] = { + "recommended_backend": "open3d_gicp", + "comparison_summary": str(output.name), + "backend_difference": summary["backend_difference"], + "warning": summary["important_limit"], + } + Path(args.recommended_output).write_text( + json.dumps(recommended, ensure_ascii=False, indent=2), encoding="utf-8" + ) + print(json.dumps(summary, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/code/visualize_pair_3d.py b/code/visualize_pair_3d.py new file mode 100644 index 0000000..3aa71a0 --- /dev/null +++ b/code/visualize_pair_3d.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +"""Interactive 3D comparison of raw, RTK, GICP and hand-eye-predicted motion.""" +import argparse +import json + +import numpy as np +from scipy.spatial.transform import Rotation + +from rigorous_calibration import ( + inverse_transform, load_stations, rotation_angle_deg, rpy_deg, transform_points, +) + + +COLORS = { + "target": [0.10, 0.65, 1.00], + "source": [1.00, 0.35, 0.05], +} + + +def cloud(o3d, points, color, voxel): + item = o3d.geometry.PointCloud() + item.points = o3d.utility.Vector3dVector(points) + item = item.voxel_down_sample(voxel) + item.paint_uniform_color(color) + return item + + +def delta_components(reference, candidate): + """Components of reference^-1*candidate, plus coordinate-invariant norms.""" + delta = inverse_transform(reference) @ candidate + translation = np.asarray(delta[:3, 3], float) + return { + "translation_xyz_cm": (translation * 100.0).tolist(), + "translation_norm_cm": float(np.linalg.norm(translation) * 100.0), + "rotation_rpy_deg_xyz": rpy_deg(delta[:3, :3]), + "rotation_angle_deg": rotation_angle_deg(delta[:3, :3]), + } + + +def body_left_rpy(x, rpy_correction_deg): + correction = np.eye(4) + correction[:3, :3] = Rotation.from_euler( + "xyz", np.asarray(rpy_correction_deg, float), degrees=True + ).as_matrix() + return correction @ x + + +def print_delta(name, reference, candidate): + item = delta_components(reference, candidate) + tx, ty, tz = item["translation_xyz_cm"] + roll, pitch, yaw = item["rotation_rpy_deg_xyz"] + print( + f"{name}: B^-1*motion translation xyz = " + f"[{tx:+.4f}, {ty:+.4f}, {tz:+.4f}] cm; " + f"rpy xyz = [{roll:+.4f}, {pitch:+.4f}, {yaw:+.4f}] deg; " + f"norm = {item['translation_norm_cm']:.4f} cm / " + f"{item['rotation_angle_deg']:.6f} deg" + ) + return item + + +def main(): + import open3d as o3d + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frames", required=True) + parser.add_argument("--pairs", required=True) + parser.add_argument("--extrinsic", required=True) + parser.add_argument("--pair-index", type=int, default=0) + parser.add_argument("--voxel", type=float, default=0.10) + parser.add_argument( + "--left-rpy-deg", nargs=3, type=float, default=[0.0, 0.0, 0.0], + metavar=("ROLL", "PITCH", "YAW"), + help="optional body-frame left correction applied as DeltaR_body * X", + ) + args = parser.parse_args() + + stations = load_stations(args.frames, 1.0, 60.0) + with np.load(args.pairs, allow_pickle=False) as data: + if len(stations) != len(data["station_times"]): + raise ValueError( + f"frames contain {len(stations)} stations but pair file records " + f"{len(data['station_times'])}" + ) + if not 0 <= args.pair_index < len(data["A"]): + raise IndexError( + f"pair-index {args.pair_index} outside [0,{len(data['A']) - 1}]" + ) + a_ij = np.asarray(data["A"][args.pair_index], float) + b_gicp = np.asarray(data["B"][args.pair_index], float) + i, j = np.asarray(data["meta"][args.pair_index, :2], int) + + with open(args.extrinsic, encoding="utf-8-sig") as stream: + result = json.load(stream) + x = np.asarray(result["matrix_4x4"], float) + b_calibrated = inverse_transform(x) @ a_ij @ x + + transforms = { + "1 raw": np.eye(4), + "2 RTK initial (X0=I)": a_ij, + "3 GICP B": b_gicp, + "4 calibrated X^-1 A X": b_calibrated, + } + correction = np.asarray(args.left_rpy_deg, float) + if np.any(np.abs(correction) > 0.0): + x_test = body_left_rpy(x, correction) + transforms[ + f"5 test body-left RPY {correction.tolist()} deg" + ] = inverse_transform(x_test) @ a_ij @ x_test + + target = stations[i][3] + source = stations[j][3] + print(f"pair_index={args.pair_index}, station {i} <- {j}") + print("blue = target station i; orange = source station j after selected transform") + print("keys: 1 raw | 2 RTK initial | 3 GICP | 4 calibrated | 5 test correction | Q/Esc exit") + print( + "IMPORTANT: delta xyz/rpy are components of B^-1*(X^-1*A*X), expressed " + "in station-j LiDAR coordinates; screen-left/right depends on the 3D camera view." + ) + baseline = print_delta("mode 4 minus mode 3", b_gicp, b_calibrated) + roll, pitch, yaw = np.abs(baseline["rotation_rpy_deg_xyz"]) + if max(roll, pitch) > max(0.10, 2.0 * yaw): + print("diagnosis: roll/pitch components dominate yaw; do not prioritize yaw tuning for this pair.") + tx, ty, tz = np.abs(baseline["translation_xyz_cm"]) + if tz > max(tx, ty): + print("diagnosis: the largest translation component is relative Z, not lateral XY.") + body_up = np.array([0.0, 0.0, 1.0]) + if np.linalg.norm(a_ij[:3, :3] @ body_up - body_up) < 1e-8: + print( + "observability: this A preserves the body Z axis, so body-left X.z " + "translation is unobservable from this pair; use ground/external height constraints." + ) + if "5 test body-left RPY " + str(correction.tolist()) + " deg" in transforms: + print_delta("mode 5 minus mode 3", b_gicp, list(transforms.values())[-1]) + + viewer = o3d.visualization.VisualizerWithKeyCallback() + viewer.create_window("Rigorous LiDAR registration inspection - 3D", 1400, 900) + target_cloud = cloud(o3d, target, COLORS["target"], args.voxel) + source_cloud = cloud(o3d, source, COLORS["source"], args.voxel) + viewer.add_geometry(target_cloud) + viewer.add_geometry(source_cloud) + axes = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1.0) + viewer.add_geometry(axes) + current = np.eye(4) + + def select(name): + def callback(vis): + nonlocal current + desired = transforms[name] + source_cloud.transform(desired @ inverse_transform(current)) + current = desired + vis.update_geometry(source_cloud) + if name == "3 GICP B": + print(f"{name}: reference registration B; delta = 0") + else: + print_delta(name + " minus mode 3", b_gicp, desired) + return False + return callback + + for key, name in zip((ord("1"), ord("2"), ord("3"), ord("4"), ord("5")), transforms): + viewer.register_key_callback(key, select(name)) + viewer.get_render_option().background_color = np.array([0.02, 0.02, 0.02]) + viewer.get_render_option().point_size = 2.0 + viewer.run() + viewer.destroy_window() + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..36d6239 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy>=1.26 +scipy>=1.11 +open3d>=0.18 +small-gicp==1.0.1 diff --git a/results/README.md b/results/README.md new file mode 100644 index 0000000..2a2899e --- /dev/null +++ b/results/README.md @@ -0,0 +1,60 @@ +# 标定结果索引 + +本目录保存本次双天线 RTK—3D LiDAR 手眼标定的可复现结果。坐标约定统一为： + +```text +X = T_body_lidar +``` + +即外参将**原始 LiDAR 点**变换到以**后轮轴中心**为原点、X 前/Y 左/Z 上的车体系。 + +## 部署时只使用这一个文件 + +[`final_extrinsic_deployment.json`](final_extrinsic_deployment.json) 是唯一用于部署的外参文件： + +```text +translation [m] = [ 1.297759692, -0.000067331, 0.720497835 ] +RPY xyz [deg] = [ -0.785151146, 1.202660822, -0.835510053 ] +``` + +## 顶层汇总文件 + +| 文件 | 用途 | 是否用于部署 | +|---|---|---| +| [`final_extrinsic_deployment.json`](final_extrinsic_deployment.json) | 精简、固定的最终外参；含平移、RPY、四元数和 4×4 矩阵。 | **是，唯一入口** | +| [`final_extrinsic_recommended.json`](final_extrinsic_recommended.json) | 同一算法外参的完整求解记录、残差、地面约束、条件数和 Bootstrap。 | 否；用于审计/复现。 | +| [`final_summary.json`](final_summary.json) | 共识结果的摘要。 | 否；用于查看指标。 | +| [`comparison_summary.json`](comparison_summary.json) | Open3D GICP、small_gicp 与共识解的对比。 | 否；用于方法对比。 | + +`final_extrinsic_deployment.json` 与 `final_extrinsic_recommended.json` 的外参数值相同；前者只是删除了求解过程字段，供上游程序稳定读取。 + +## 子目录 + +| 目录 | 内容 | 用途 | +|---|---|---| +| [`common/`](common/) | 第二批每个静止站点拟合得到的地面平面。 | 地面约束检查。 | +| [`open3d_gicp/`](open3d_gicp/) | Open3D GICP 生成的 LiDAR 相对运动 B、质量指标、精筛记录和对应外参。 | 后端一的独立结果。 | +| [`small_gicp/`](small_gicp/) | small_gicp 生成的 LiDAR 相对运动 B、质量指标、精筛记录和对应外参。 | 后端二的独立结果。 | +| [`consensus/`](consensus/) | 两后端一致性筛选后的共识 B，共识外参和第一批辅助复核。 | 主要求解依据。 | +| [`diagnostics/`](diagnostics/) | 单运动对 roll/pitch/yaw 局部灵敏度扫描。 | 仅诊断，不可直接当外参。 | + +## 如何识别 B 文件 + +每个 `.npz` 都是 LiDAR 相对运动集合 `B_ij = T_Li_Lj`，把第 j 个静止站点的点云变换到第 i 个站点 LiDAR 坐标系。它们不是车体—雷达外参，不能直接写入部署配置。 + +- `B_batch2_*`：第二批 38 站高频 RTK 数据，主要求解使用。 +- `B_batch1_*`：第一批 38 站稀疏 RTK 数据，仅作辅助复核。 +- `*_estimation.npz`：点云配准的初始候选集合。 +- `*_refined.npz`：经过点云质量门限后的集合。 +- `consensus/B_batch2_consensus.npz`：Open3D 与 small_gicp 对同一运动对的 B 差异不超过 `0.05 m / 0.50°` 后保留的 39 对；它是最终主求解输入。 + +同名的 `.quality.json/.csv` 记录配准质量，`.refinement.json` 记录精筛过程，`.consensus.json` 记录跨后端一致性筛选。它们都用于追溯，不用于部署。 + +## 结果使用边界 + +- 主结果由第二批数据求解；第一批只作为独立辅助检查，不参与主结果拟合。 +- B 的筛选先于 X 的求解，并且不读取待求外参 X，不用 AX 残差反过来选择 B。 +- 第二批共识集的 AX RMS 为 `0.07985 m / 0.96118°`；旋转残差约 1°，因此不是厘米级绝对精度认证。 +- 当前 RTK 轨迹只有位置和 yaw，没有 roll/pitch。AX 残差对 Z 不可观；Z 必须结合地面约束和独立机械量测确认。 + +更多流程、运行命令和可视化说明见仓库根目录的 [`README.md`](../README.md)。 \ No newline at end of file diff --git a/results/common/ground_planes_batch2.csv b/results/common/ground_planes_batch2.csv new file mode 100644 index 0000000..9d84bc2 --- /dev/null +++ b/results/common/ground_planes_batch2.csv @@ -0,0 +1,39 @@ +time,nx,ny,nz,d,inliers,rms_m,frame_counter 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"first_batch_role": "auxiliary check only (sparse RTK)", + "backend_difference": { + "translation_m": 0.00181035475331947, + "rotation_deg": 0.06312194455789993 + }, + "open3d_gicp": { + "translation_m": [ + 1.2978831677200011, + -0.0030997734098957433, + 0.7217892226151688 + ], + "rotation_rpy_deg_xyz": [ + -0.7572070226288585, + 1.1460424368859559, + -0.8797812288284186 + ], + "estimation_pairs": 66, + "estimation_translation_rms_m": 0.10017183831403174, + "estimation_rotation_rms_deg": 1.011529772202344, + "bootstrap_std": [ + 0.0020832827985508263, + 0.002063603632322515, + 0.001427746471392659, + 0.07576290622767821, + 0.06304875844333606, + 0.07843172452171372 + ], + "initial_B_loop_closure": { + "count": 106, + "translation_rms_m": 0.020350848901853437, + "translation_p95_m": 0.04464367942375473, + "rotation_rms_deg": 0.2674546762953808, + "rotation_p95_deg": 0.5761692075739222 + }, + "batch1_auxiliary_pairs": 41, + "batch1_auxiliary_translation_rms_m": 0.06627076507951374, + 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strong, but AX rotation RMS remains about one degree. This is not a centimetre-grade absolute certification." +} \ No newline at end of file diff --git a/results/consensus/B_batch1_consensus.consensus.json b/results/consensus/B_batch1_consensus.consensus.json new file mode 100644 index 0000000..3967e3b --- /dev/null +++ b/results/consensus/B_batch1_consensus.consensus.json @@ -0,0 +1,328 @@ +{ + "selection_is_X_independent": true, + "B_source": "Open3D; small_gicp is used only as an agreement gate", + "max_translation_m": 0.05, + "max_rotation_deg": 0.5, + "input_open3d_pairs": 41, + "accepted_pairs": 22, + "pairs": [ + { + "i": 0, + "j": 1, + "open3d_small_translation_m": 0.04084575096493436, + "open3d_small_rotation_deg": 0.8091245488258404, + "accepted": false, + "reason": "backend_disagreement" + }, + { + "i": 0, + "j": 3, + "open3d_small_translation_m": 0.007440836728606337, + "open3d_small_rotation_deg": 0.14377171116926005, + "accepted": true, + "reason": "" + }, + { + "i": 1, + "j": 2, + "open3d_small_translation_m": 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"C:\Users\admin\Documents\Codex\2026-07-15\wo\outputs\calibration_data1_20260720\prepared" +) +$ErrorActionPreference = "Stop" +$Repo = Split-Path -Parent $PSScriptRoot +$Code = Join-Path $Repo "code\rigorous_calibration.py" +$Refine = Join-Path $Repo "code\refine_pairs.py" +$Summary = Join-Path $Repo "code\summarize_results.py" +$R = Join-Path $Repo "results" +$B1Frames = Join-Path $Batch1Prepared "frames_all" +$B2Frames = Join-Path $Batch2Prepared "frames_all" +$B1Body = Join-Path $Batch1Prepared "body_poses_rear_gga_raw_rear_to_front.csv" +$B2Body = Join-Path $Batch2Prepared "body_poses_rear_gga_raw_rear_to_front.csv" + +python $Code ground --frames $B2Frames --output "$R\common\ground_planes_batch2.csv" + +python $Code pairs --backend small_gicp --frames $B2Frames --body $B2Body --output "$R\small_gicp\B_batch2_estimation.npz" --quality-json "$R\small_gicp\B_batch2_quality.json" --quality-csv "$R\small_gicp\B_batch2_quality.csv" +python $Code pairs --backend open3d --frames $B2Frames --body $B2Body --output "$R\open3d_gicp\B_batch2_estimation.npz" --quality-json "$R\open3d_gicp\B_batch2_quality.json" --quality-csv "$R\open3d_gicp\B_batch2_quality.csv" --max-gap 3 --multistart 1 --iterations 40 +python $Refine --pairs "$R\small_gicp\B_batch2_estimation.npz" --quality-json "$R\small_gicp\B_batch2_quality.json" --output "$R\small_gicp\B_batch2_refined.npz" +python $Refine --pairs "$R\open3d_gicp\B_batch2_estimation.npz" --quality-json "$R\open3d_gicp\B_batch2_quality.json" --output "$R\open3d_gicp\B_batch2_refined.npz" +python $Code calibrate --pairs "$R\small_gicp\B_batch2_refined.npz" --ground-planes "$R\common\ground_planes_batch2.csv" --output "$R\small_gicp\extrinsic_batch2_refined.json" +python $Code calibrate --pairs "$R\open3d_gicp\B_batch2_refined.npz" --ground-planes "$R\common\ground_planes_batch2.csv" --output "$R\open3d_gicp\extrinsic_batch2_refined.json" + +python $Code pairs --backend small_gicp --frames $B1Frames --body $B1Body --output "$R\small_gicp\B_batch1_auxiliary.npz" --quality-json "$R\small_gicp\B_batch1_quality.json" --quality-csv "$R\small_gicp\B_batch1_quality.csv" --max-gap 3 --multistart 1 --iterations 40 +python $Code pairs --backend open3d --frames $B1Frames --body $B1Body --output "$R\open3d_gicp\B_batch1_auxiliary.npz" --quality-json "$R\open3d_gicp\B_batch1_quality.json" --quality-csv "$R\open3d_gicp\B_batch1_quality.csv" --max-gap 3 --multistart 1 --iterations 40 +python $Refine --pairs "$R\small_gicp\B_batch1_auxiliary.npz" --quality-json "$R\small_gicp\B_batch1_quality.json" --output "$R\small_gicp\B_batch1_auxiliary_refined.npz" +python $Refine --pairs "$R\open3d_gicp\B_batch1_auxiliary.npz" --quality-json "$R\open3d_gicp\B_batch1_quality.json" --output "$R\open3d_gicp\B_batch1_auxiliary_refined.npz" +python $Code validate --pairs "$R\small_gicp\B_batch1_auxiliary_refined.npz" --extrinsic "$R\small_gicp\extrinsic_batch2_refined.json" --output "$R\small_gicp\batch1_auxiliary_check.json" +python $Code validate --pairs "$R\open3d_gicp\B_batch1_auxiliary_refined.npz" --extrinsic "$R\open3d_gicp\extrinsic_batch2_refined.json" --output "$R\open3d_gicp\batch1_auxiliary_check.json" + +python $Summary --open3d "$R\open3d_gicp\extrinsic_batch2_refined.json" --small "$R\small_gicp\extrinsic_batch2_refined.json" --open3d-quality "$R\open3d_gicp\B_batch2_quality.json" --small-quality "$R\small_gicp\B_batch2_quality.json" --open3d-check "$R\open3d_gicp\batch1_auxiliary_check.json" --small-check "$R\small_gicp\batch1_auxiliary_check.json" --output "$R\comparison_summary.json" --recommended-output "$R\final_extrinsic_recommended.json" diff --git a/run/run_consensus_finish.ps1 b/run/run_consensus_finish.ps1 new file mode 100644 index 0000000..b94c368 --- /dev/null +++ b/run/run_consensus_finish.ps1 @@ -0,0 +1,12 @@ +$ErrorActionPreference = "Stop" +$Repo = Split-Path -Parent $PSScriptRoot +$Code = Join-Path $Repo "code\rigorous_calibration.py" +$Filter = Join-Path $Repo "code\cross_backend_filter.py" +$Finalize = Join-Path $Repo "code\finalize_consensus.py" +$R = Join-Path $Repo "results" + +python $Filter --open3d-pairs "$R\open3d_gicp\B_batch2_refined.npz" --small-pairs "$R\small_gicp\B_batch2_refined.npz" --output "$R\consensus\B_batch2_consensus.npz" +python $Filter --open3d-pairs "$R\open3d_gicp\B_batch1_auxiliary_refined.npz" --small-pairs "$R\small_gicp\B_batch1_auxiliary_refined.npz" --output "$R\consensus\B_batch1_consensus.npz" --min-pairs 15 +python $Code calibrate --pairs "$R\consensus\B_batch2_consensus.npz" --ground-planes "$R\common\ground_planes_batch2.csv" --output "$R\consensus\extrinsic_batch2_consensus.json" +python $Code validate --pairs "$R\consensus\B_batch1_consensus.npz" --extrinsic "$R\consensus\extrinsic_batch2_consensus.json" --output "$R\consensus\batch1_auxiliary_check.json" +python $Finalize --consensus-extrinsic "$R\consensus\extrinsic_batch2_consensus.json" --consensus-check "$R\consensus\batch1_auxiliary_check.json" --open3d-extrinsic "$R\open3d_gicp\extrinsic_batch2_refined.json" --small-extrinsic "$R\small_gicp\extrinsic_batch2_refined.json" --output "$R\final_extrinsic_recommended.json" --summary "$R\final_summary.json" diff --git a/run/run_sensitivity_scan.ps1 b/run/run_sensitivity_scan.ps1 new file mode 100644 index 0000000..c8c09aa --- /dev/null +++ b/run/run_sensitivity_scan.ps1 @@ -0,0 +1,15 @@ +param([int]$PairIndex = 0) +$Repo = Split-Path -Parent $PSScriptRoot +$Code = "$Repo\code\scan_extrinsic_sensitivity.py" +$X = "$Repo\results\final_extrinsic_recommended.json" +$Ground = "$Repo\results\common\ground_planes_batch2.csv" +$Out = "$Repo\results\diagnostics" + +python $Code --pairs "$Repo\results\open3d_gicp\B_batch2_refined.npz" --extrinsic $X --ground-planes $Ground --pair-index $PairIndex --output "$Out\open3d_refined_pair${PairIndex}_left_rpy_scan.json" +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +python $Code --pairs "$Repo\results\small_gicp\B_batch2_refined.npz" --extrinsic $X --ground-planes $Ground --pair-index $PairIndex --output "$Out\small_gicp_refined_pair${PairIndex}_left_rpy_scan.json" +if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } + +python $Code --pairs "$Repo\results\consensus\B_batch2_consensus.npz" --extrinsic $X --ground-planes $Ground --pair-index $PairIndex --output "$Out\consensus_pair${PairIndex}_left_rpy_scan.json" +exit $LASTEXITCODE diff --git a/run/view_open3d_result.ps1 b/run/view_open3d_result.ps1 new file mode 100644 index 0000000..0ed3dff --- /dev/null +++ b/run/view_open3d_result.ps1 @@ -0,0 +1,14 @@ +param( + [int]$PairIndex = 0, + [string]$Frames = "C:\Users\admin\Documents\Codex\2026-07-15\wo\outputs\calibration_data1_20260720\prepared\frames_all", + [double]$LeftRollDeg = 0.0, + [double]$LeftPitchDeg = 0.0, + [double]$LeftYawDeg = 0.0 +) +$Repo = Split-Path -Parent $PSScriptRoot +python "$Repo\code\visualize_pair_3d.py" ` + --frames $Frames ` + --pairs "$Repo\results\open3d_gicp\B_batch2_refined.npz" ` + --extrinsic "$Repo\results\final_extrinsic_recommended.json" ` + --pair-index $PairIndex ` + --left-rpy-deg $LeftRollDeg $LeftPitchDeg $LeftYawDeg diff --git a/run/view_small_gicp_result.ps1 b/run/view_small_gicp_result.ps1 new file mode 100644 index 0000000..da49c02 --- /dev/null +++ b/run/view_small_gicp_result.ps1 @@ -0,0 +1,18 @@ +param( + [int]$PairIndex = 0, + [string]$Frames = "C:\Users\admin\Documents\Codex\2026-07-15\wo\outputs\calibration_data1_20260720\prepared\frames_all", + [string]$Extrinsic = "", + [double]$LeftRollDeg = 0.0, + [double]$LeftPitchDeg = 0.0, + [double]$LeftYawDeg = 0.0 +) +$Repo = Split-Path -Parent $PSScriptRoot +if ([string]::IsNullOrWhiteSpace($Extrinsic)) { + $Extrinsic = "$Repo\results\small_gicp\extrinsic_batch2_refined.json" +} +python "$Repo\code\visualize_pair_3d.py" ` + --frames $Frames ` + --pairs "$Repo\results\small_gicp\B_batch2_refined.npz" ` + --extrinsic $Extrinsic ` + --pair-index $PairIndex ` + --left-rpy-deg $LeftRollDeg $LeftPitchDeg $LeftYawDeg