MakeWay: LiDARを用いた物体認識型コストマップによる先読み屋内ナビゲーション
MakeWay: Object-Aware Costmaps for Proactive Indoor Navigation Using LiDAR
LiDARの3D物体検出と追跡で物体マップを構築し、物体のアフォーダンスに基づくコストマップで先回り的な衝突回避を実現する屋内ナビゲーションシステムを提案した。
著者: Binbin Xu, Allen Tao, Hugues Thomas, Jian Zhang, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
In this paper, we introduce a LiDAR-based robot navigation system, based on novel object-aware affordance-based costmaps. Utilizing a 3D object detection network, our system identifies objects of interest in LiDAR keyframes, refines their 3D poses with the Iterative Closest Point (ICP) algorithm, and tracks them via Kalman filters and the Hungarian algorithm for data association. It then updates existing object poses with new associated detections and creates new object maps for unmatched detections. Using the maintained object-level mapping system, our system creates affordance-driven object costmaps for proactive collision avoidance in path planning. Additionally, we address the scarcity of indoor semantic LiDAR data by introducing an automated labeling technique. This method utilizes a CAD model database for accurate ground-truth annotations, encompassing bounding boxes, positions, orientations, and point-wise semantics of each object in LiDAR sequences. Our extensive evaluations, conducted in both simulated and real-world robot platforms, highlights the effectiveness of proactive object avoidance by using object affordance costmaps, enhancing robotic navigation safety and efficiency. The system can operate in real-time onboard and we intend to release our code and data for public use.
