四足歩行ロボットによる情報利得ベースの能動的マッピング
Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot
四足歩行ロボットが未知の構造物を自律的にスキャン・再構築するため、情報利得と移動コストから次の最良視点を選ぶオンライン能動マッピングシステムを開発した。
著者: Yiduo Wang, Milad Ramezani, Maurice Fallon
分類: cs.RO
原文アブストラクト
In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure.
