from __future__ import annotations import numpy as np import pytest from k1link.sessions.canonical_lab_spatial import ( _TimedPoints, _TimedPoses, _bounded_local_slam, _estimate_sensor_height, _ground_origin_map, ) def _calibration_cloud(height_m: float, seed: int) -> np.ndarray: rng = np.random.default_rng(seed) xy = rng.uniform(-5.5, 5.5, size=(500, 2)).astype(np.float32) radius = np.linalg.norm(xy, axis=1) xy = xy[(radius >= 1.0) & (radius <= 5.5)][:360] ground = np.column_stack(( xy, rng.normal(-height_m, 0.006, size=xy.shape[0]), )).astype(np.float32) vegetation = np.column_stack(( rng.uniform(-5, 5, size=(300, 2)), rng.uniform(0.0, 1.2, size=300), )).astype(np.float32) return np.concatenate((ground, vegetation), axis=0) def test_session_sensor_height_is_derived_from_initial_source_cloud() -> None: times = tuple(index * 500_000_000 for index in range(12)) points = _TimedPoints( times_ns=times, values=tuple(_calibration_cloud(0.32, index) for index in range(12)), ) poses = _TimedPoses( times_ns=times, translations=tuple(np.zeros(3) for _ in times), quaternions_xyzw=tuple(np.asarray([0.0, 0.0, 0.0, 1.0]) for _ in times), ) height, sample_count, mad = _estimate_sensor_height(points, poses) assert height == pytest.approx(0.32, abs=0.02) assert sample_count == 12 assert mad < 0.02 def test_local_slam_accumulates_source_increments_in_ground_body_frame() -> None: points = _TimedPoints( times_ns=(0, 1_000_000_000, 2_000_000_000), values=( np.asarray([[1.0, 0.0, -0.32]], dtype=np.float32), np.asarray([[2.0, 0.0, -0.32]], dtype=np.float32), np.asarray([[3.0, 0.0, -0.32]], dtype=np.float32), ), ) basis = np.eye(3) ground_origin = _ground_origin_map(np.asarray([0.0, 0.0, 0.0]), basis, 0.32) local, frame_count, source_count = _bounded_local_slam( points, 2_000_000_000, ground_origin, basis, ) assert frame_count == 3 assert source_count == 3 assert local[:, 2].tolist() == pytest.approx([0.0, 0.0, 0.0], abs=1e-6)