3D LiDARにおける動的物体のマップレスオンライン検出
Mapless Online Detection of Dynamic Objects in 3D Lidar
スキャン中の移動による歪みを補正し、マップ不要で設定非依存の3D LiDAR動的物体検出法を提案。点単位で動的/静的を分類し、CARLAベンチマークと実データで評価した。
著者: David J. Yoon, Tim Y. Tang, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operation (motion distortion) of present-day 3D spinning lidar sensors. Our detection method uses a motion-compensated freespace querying algorithm and classifies between dynamic (currently moving) and static (currently stationary) labels at the point level. For a quantitative analysis, we establish a benchmark with motion-distorted lidar data using CARLA, an open-source simulator for autonomous driving research. We also provide a qualitative analysis with real data using a Velodyne HDL-64E in driving scenarios. Compared to existing 3D lidar methods that are model-free, our method is unique because of its setting independence and compensation for pointcloud motion distortion.
