トポメトリック事前情報を活用したSLAM対応グラフベース探索
Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information
環境のトポメトリックグラフを事前情報として用い、SLAMのポーズグラフ信頼性を高めるループ閉じ込み行動を組み込んだ探索経路計画を提案し、階層的探索フレームワークに統合した。
著者: Ruofei Bai, Hongliang Guo, Wei-Yun Yau, Lihua Xie
分類: cs.RO
原文アブストラクト
Autonomous exploration requires a robot to explore an unknown environment while constructing an accurate map using Simultaneous Localization and Mapping (SLAM) techniques. Without prior information, the exploration performance is usually conservative due to the limited planning horizon. This paper exploits prior information about the environment, represented as a topo-metric graph, to benefit both the exploration efficiency and the pose graph reliability in SLAM. Based on the relationship between pose graph reliability and graph topology, we formulate a SLAM-aware path planning problem over the prior graph, which finds a fast exploration path enhanced with the globally informative loop-closing actions to stabilize the SLAM pose graph. A greedy algorithm is proposed to solve the problem, where theoretical thresholds are derived to significantly prune non-optimal loop-closing actions, without affecting the potential informative ones. Furthermore, we incorporate the proposed planner into a hierarchical exploration framework, with flexible features including path replanning, and online prior graph update that adds additional information to the prior graph. Simulation and real-world experiments indicate that the proposed method can reliably achieve higher mapping accuracy than compared methods when exploring environments with rich topologies, while maintaining comparable exploration efficiency. Our method has been open-sourced on GitHub.
