perf(perception): reuse local surface cell observations

tests/test_lidar_local_surface.py

@@ -26,6 +26,11 @@ from k1link.compute.lidar_local_surface import (
 from k1link.compute.lidar_local_surface_geometry import (
     DEFAULT_K1_LOCAL_SURFACE_PROFILE as WORKER_LOCAL_SURFACE_PROFILE,
 )
+from k1link.compute.lidar_local_surface_geometry import (
+    cloud_cell_observations,
+    update_cache,
+    update_cache_observations,
+)
 from k1link.web.lidar_api import build_lidar_router
 from k1link.web.lidar_local_surface_service import K1LocalSurfaceReadService
 
@@ -48,6 +53,26 @@ def test_replay_and_minimal_worker_pin_the_same_local_surface_profile() -> None:
     assert WORKER_LOCAL_SURFACE_PROFILE.to_dict() == REPLAY_LOCAL_SURFACE_PROFILE.to_dict()
 
 
+def test_precomputed_local_surface_observations_preserve_cache_update() -> None:
+    cloud = np.asarray(
+        [
+            [0.1, 0.1, 0.8],
+            [0.2, 0.2, 0.2],
+            [1.1, 0.1, 0.5],
+            [-0.2, -0.2, -0.1],
+        ],
+        dtype=np.float64,
+    )
+    baseline: dict[tuple[int, int], tuple[float, float]] = {}
+    optimized: dict[tuple[int, int], tuple[float, float]] = {}
+
+    update_cache(baseline, cloud, 4.25, WORKER_LOCAL_SURFACE_PROFILE)
+    keys, points = cloud_cell_observations(cloud, WORKER_LOCAL_SURFACE_PROFILE)
+    update_cache_observations(optimized, keys, points, 4.25)
+
+    assert optimized == baseline
+
+
 def _source_pack(root: Path) -> Path:
     frame_count = 8
     available = np.asarray([True, True, True, True, True, True, True, False])