利用先验拓扑度量信息的基于图的 SLAM 感知探索
Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information
AI总结：
提出一种基于先验拓扑度量图的 SLAM 感知探索规划方法，通过全局回环增强路径并以贪心算法剪枝，在保持探索效率的同时提升建图精度。
摘要：
自主探索要求机器人在探索未知环境的同时，使用同步定位与地图构建（SLAM）技术构建精确的地图。在没有先验信息的情况下，由于规划时域有限，探索性能通常较为保守。本文利用以拓扑度量图表示的环境先验信息，以同时提升探索效率和 SLAM 中位姿图的可靠性。基于位姿图可靠性与图拓扑之间的关系，我们在先验图上构建了一个 SLAM 感知路径规划问题，该问题寻找一条快速探索路径，并通过具有全局信息量的回环闭合动作加以增强，从而稳定 SLAM 位姿图。文中提出了一种贪心算法来求解该问题，并推导了理论阈值，以大幅剪除非最优的回环闭合动作，同时不影响潜在具有信息量的动作。此外，我们将所提出的规划器纳入一个分层探索框架，该框架具备路径重规划，以及向先验图添加额外信息的在线先验图更新等灵活特性。仿真与真实世界实验表明，在探索拓扑丰富的环境时，所提方法能够可靠地取得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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
