ProxMaP：近接占有マップ予測による効率的な屋内ロボットナビゲーション
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
ロボット周辺の遮蔽領域の占有状態を自己教師あり学習で予測し、未知環境での移動経路計画を効率化する手法を提案。シミュレーションで従来手法より12.40%ナビゲーション効率を改善した。
著者: Vishnu Dutt Sharma, Jingxi Chen, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Planning a path for a mobile robot typically requires building a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating in an unknown environment, the map built by the robot online may have many as-yet-unknown regions. A conservative planner may avoid such regions taking a longer time to navigate to the goal. Instead, if a robot is able to correctly predict the occupancy in the occluded regions, the robot may navigate efficiently. We present a self-supervised occupancy prediction technique, ProxMaP, to predict the occupancy within the proximity of the robot to enable faster navigation. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by 12.40% against a traditional navigation method. We share our findings and code at https://raaslab.org/projects/ProxMaP.
