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.

翻译：自主探索要求机器人在未知环境中进行探索，同时利用同步定位与建图（SLAM）技术构建精确地图。在缺乏先验信息的情况下，由于规划视野有限，探索性能通常较为保守。本文利用以拓扑度量图形式表示的环境先验信息，旨在同时提升探索效率与SLAM中的位姿图可靠性。基于位姿图可靠性与图拓扑结构之间的关系，我们在先验图上构建了一个SLAM感知路径规划问题，该问题通过融合全局信息丰富的闭环动作来寻找快速探索路径，以稳定SLAM位姿图。我们提出一种贪心算法来解决该问题，并推导出理论阈值以显著剪除非最优的闭环动作，同时不影响潜在的信息化动作。此外，我们将所提出的规划器集成到分层探索框架中，该框架具备路径重规划、在线先验图更新（用于向先验图添加额外信息）等灵活特性。仿真与真实环境实验表明，在探索具有丰富拓扑结构的环境时，所提方法能够可靠地实现比对比方法更高的建图精度，同时保持相当的探索效率。我们的方法已在GitHub上开源。