トポロジカル特徴グラフによる情報量ベースのアクティブSLAM
Information-based Active SLAM via Topological Feature Graphs
トポロジカル特徴グラフ(TFG)を用いて、単一のエントロピー指標で探索と活用のバランスを取るアクティブSLAMアルゴリズムを提案し、グリッドマップ法より高精度かつ大幅に少ない計算・メモリで実現した。
著者: Beipeng Mu, Matthew Giamou, Liam Paull, Ali-akbar Agha-mohammadi, John Leonard, Jonathan How
分類: cs.RO
原文アブストラクト
Active SLAM is the task of actively planning robot paths while simultaneously building a map and localizing within. Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources.
