Autonomous exploration requires the robot to explore an unknown environment while constructing an accurate map with the SLAM (Simultaneous Localization and Mapping) techniques. Without prior information, the exploratory performance is usually conservative due to the limited planning horizon. This paper exploits a prior topo-metric graph of the environment to benefit both the exploration efficiency and the pose graph accuracy in SLAM. Based on recent advancements in relating pose graph reliability with graph topology, we are able to formulate both objectives into a SLAM-aware path planning problem over the prior graph, which finds a fast exploration path with informative loop closures that globally stabilize the pose graph. Furthermore, we derive theoretical thresholds to speed up the greedy algorithm to the problem, which significantly prune non-optimal loop closures in iterations. The proposed planner is incorporated into a hierarchical exploration framework, with flexible features including path replanning and online prior map update that adds additional information to the prior graph. Extensive experiments indicate that our method has comparable exploration efficiency to others while consistently maintaining higher mapping accuracy in various environments. Our implementations will be open-source on GitHub.

翻译：自主探索要求机器人在未知环境中利用同时定位与地图构建（SLAM）技术构建精确地图的同时进行探索。在没有先验信息的情况下，由于规划视野有限，探索性能通常趋于保守。本文利用环境的先验拓扑度量图，在提升探索效率和SLAM位姿图精度两方面均取得优势。基于近期关于位姿图可靠性与图拓扑关系的研究进展，我们能够将这两个目标统一表述为先验图上的SLAM感知路径规划问题，从而找到一条既能实现快速探索、又能通过提供信息丰富的闭环来全局稳定位姿图的路径。此外，我们推导了理论阈值以加速该问题的贪心算法，该阈值在迭代过程中显著剪除非最优闭环。所提出的规划器被嵌入到分层探索框架中，具备路径重规划和在线先验地图更新等灵活特性，可向先验图补充额外信息。大量实验表明，我们的方法在保持与其他方法相当的探索效率的同时，能在多种环境中持续保持更高的建图精度。我们的实现代码将在GitHub上开源。