ProxMaP: 屋内ロボットナビゲーションのための近接占有マップ予測
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
ロボット周囲の遮蔽領域の占有マップを自己教師あり学習で予測し、屋内ナビゲーションを効率化する手法を提案。
著者: Vishnu Dutt Sharma, Jingxi Chen, Pratap Tokekar
分類: cs.RO
原文アブストラクト
In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground robot's knowledge about the environment may be limited due to occlusions. Therefore, the map will have many as-yet-unknown regions that may need to be avoided by a conservative planner. Instead, if a robot is able to correctly predict what its surroundings and occluded regions look like, the robot may be more efficient in navigation. In this work, we focus on predicting occupancy within the reachable distance of the robot to enable faster navigation and present a self-supervised proximity occupancy map prediction method, named ProxMaP. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by \textbf{$12.40\%$} against the traditional navigation method. We share our findings on our project webpage (see https://raaslab.org/projects/ProxMaP ).
