118 lines
3.2 KiB
Python
118 lines
3.2 KiB
Python
"""Shared contracts for the LiDAR–IMU calibration pipeline."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from enum import Enum
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from pathlib import Path
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from typing import Any
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import numpy as np
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class TransformConvention(str, Enum):
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"""The only transform convention used by this project."""
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T_A_B = "T_A_B maps points from frame B into frame A"
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class CalibrationMode(str, Enum):
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ROTATION_ONLY = "rotation_only"
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FULL_SE3 = "full_se3"
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class CalibrationStatus(str, Enum):
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NOT_RUN = "not_run"
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BLOCKED = "blocked"
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ROTATION_ONLY_ACCEPTED = "rotation_only_accepted"
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FULL_SE3_ACCEPTED = "full_se3_accepted"
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FULL_SE3_REJECTED = "full_se3_rejected_due_to_observability"
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@dataclass(frozen=True)
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class SessionInput:
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"""Input paths for one independently recorded session."""
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session_id: str
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imu_source: Path
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lidar_source: Path
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board_configuration_id: str | None = None
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@dataclass(frozen=True)
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class CalibrationRequest:
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"""Top-level calibration request."""
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vehicle_config: Path | None
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sessions: tuple[SessionInput, ...] = ()
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requested_mode: CalibrationMode = CalibrationMode.ROTATION_ONLY
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output_directory: Path | None = None
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max_iterations: int = 2
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min_pair_rotation_deg: float = 3.0
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min_pair_translation_m: float = 0.3
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time_offset_search_s: float = 1.0
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@dataclass
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class CalibrationResult:
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"""Result envelope written by finalize after pipeline gates."""
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status: CalibrationStatus = CalibrationStatus.NOT_RUN
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message: str = "Calibration has not been executed."
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details: dict[str, Any] = field(default_factory=dict)
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T_IMU_lidar: np.ndarray | None = None
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time_offset_s: float | None = None
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@dataclass(frozen=True)
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class ImuSeries:
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"""Normalized IMU samples.
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``t_s`` is the native IMU clock in seconds (need not match LiDAR epoch).
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Gyro must be rad/s; accelerometer must be m/s^2.
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"""
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t_s: np.ndarray
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gyro_rad_s: np.ndarray
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acc_m_s2: np.ndarray
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def __post_init__(self) -> None:
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object.__setattr__(self, "t_s", np.asarray(self.t_s, dtype=float).reshape(-1))
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object.__setattr__(self, "gyro_rad_s", np.asarray(self.gyro_rad_s, dtype=float).reshape(-1, 3))
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object.__setattr__(self, "acc_m_s2", np.asarray(self.acc_m_s2, dtype=float).reshape(-1, 3))
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n = self.t_s.size
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if self.gyro_rad_s.shape != (n, 3) or self.acc_m_s2.shape != (n, 3):
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raise ValueError("IMU arrays must share the same length and have shape (N, 3)")
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@dataclass(frozen=True)
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class LidarFrame:
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"""One LiDAR sweep in Cartesian sensor coordinates."""
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frame_id: str
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t_start_s: float
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t_end_s: float
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points_xyz: np.ndarray
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path: Path | None = None
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@property
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def t_mid_s(self) -> float:
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return 0.5 * (self.t_start_s + self.t_end_s)
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@dataclass(frozen=True)
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class MotionPair:
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"""One relative-motion observation between keyframes i and j."""
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session_id: str
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i: int
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j: int
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t_i_s: float
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t_j_s: float
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R_A: np.ndarray
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R_B: np.ndarray
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t_A_m: np.ndarray | None = None
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t_B_m: np.ndarray | None = None
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fitness: float = 0.0
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metadata: dict[str, Any] = field(default_factory=dict)
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