feat(perception): add persistent lidar motion evidence

src/k1link/compute/integrated_perception.py

@@ -73,7 +73,7 @@ class _PresentedCuboid:
     track_id: int
     label: str
     association_group: str
-    distance_m: float
+    distance_m: float | None
     support_points: int
     center: np.ndarray
     half_size: np.ndarray
@@ -126,12 +126,20 @@ class _CuboidPresentationState:
                 raise RecordedPerceptionOverlayError(
                     "integrated perception cuboid track identity is invalid"
                 )
+            distance_value = item.get("distance_smoothed_m")
+            distance_m = (
+                float(distance_value)
+                if isinstance(distance_value, int | float)
+                and not isinstance(distance_value, bool)
+                and np.isfinite(float(distance_value))
+                else None
+            )
             self._latest[track_id] = _PresentedCuboid(
                 observed_ns=timestamp_ns,
                 track_id=track_id,
                 label=str(item.get("label", "object")),
                 association_group=str(item.get("association_group", "object")),
-                distance_m=float(item["distance_smoothed_m"]),
+                distance_m=distance_m,
                 support_points=int(item["clustered_points"]),
                 center=np.asarray(center).copy(),
                 half_size=np.asarray(half_size).copy(),
@@ -160,9 +168,10 @@ class _CuboidPresentationState:
                 color[3] = max(24, round(float(color[3]) * (1.0 - 0.55 * fade)))
             presented_colors.append(color)
             age_label = "" if age_ns == 0 else f" · hold {age_ns / 1_000_000:.0f} ms"
+            distance_label = "" if cuboid.distance_m is None else f" · {cuboid.distance_m:.1f} m"
             labels.append(
-                f"{cuboid.association_group} #{cuboid.track_id} {cuboid.label} · "
-                f"{cuboid.distance_m:.1f} m · {cuboid.support_points} pts{age_label}"
+                f"{cuboid.association_group} #{cuboid.track_id} {cuboid.label}"
+                f"{distance_label} · {cuboid.support_points} pts{age_label}"
             )
         return (
             np.stack([cuboid.center for cuboid in presented]),