jbnu/3d-environment-perception Private

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from __future__ import annotationsfrom collections import defaultdictdef execute(payload: dict) -> dict:    cell = float(payload.get("cell_size_m", 1))    minimum = int(payload.get("min_points", 2))    if cell <= 0:        raise ValueError("cell_size_m must be positive")    buckets = defaultdict(list)    for point in payload.get("points", []):        buckets[(int(point[0] // cell), int(point[1] // cell))].append(point)    obstacles = []    for key, points in buckets.items():        if len(points) < minimum:            continue        obstacles.append({"cell": list(key), "centroid": [round(sum(p[i] for p in points)/len(points), 3) for i in range(3)], "point_count": len(points), "class": "unknown_obstacle"})    return {"frame": "lidar", "obstacles": obstacles, "obstacle_count": len(obstacles)}