Mapping is one of the crucial tasks enabling autonomous navigation of a mobile robot. Conventional mapping methods output dense geometric map representation, e.g. an occupancy grid, which is not trivial to keep consistent for the prolonged runs covering large environments. Meanwhile, capturing the topological structure of the workspace enables fast path planning, is less prone to odometry error accumulation and does not consume much memory. Following this idea, this paper introduces PRISM-TopoMap -- a topological mapping method that maintains a graph of locally aligned locations not relying on global metric coordinates. The proposed method involves learnable multimodal place recognition paired with the scan matching pipeline for localization and loop closure in the graph of locations. The latter is updated online and the robot is localized in a proper node at each time step. We conduct a broad experimental evaluation of the suggested approach in a range of photo-realistic environments and on a real robot (wheeled differential driven Husky robot), and compare it to state of the art. The results of the empirical evaluation confirm that PRISM-Topomap consistently outperforms competitors across several measures of mapping and navigation efficiency and performs well on a real robot. The code of PRISM-Topomap is open-sourced and available at https://github.com/kirillMouraviev/prism-topomap.

翻译：地图构建是移动机器人自主导航的关键任务之一。传统地图构建方法输出稠密几何地图表示（例如占据栅格），这类方法在覆盖大范围环境的长时间运行中难以保持一致性。与此同时，捕获工作空间的拓扑结构能够实现快速路径规划、不易受里程计误差累积影响且内存消耗低。基于此思路，本文提出PRISM-TopoMap——一种不依赖全局度量坐标、通过维护局部对齐位置图来实现拓扑地图构建的方法。该方法将可学习多模态地点识别与扫描匹配流水线相结合，用于位置图中的定位与闭环检测。后者可在线更新，并在每个时间步将机器人定位至对应节点。我们在一系列逼真仿真环境及真实机器人（轮式差速驱动Husky机器人）上对所提方法进行了广泛实验评估，并与现有最先进方法进行对比。实验结果表明，PRISM-TopoMap在多项地图构建与导航效率指标上持续优于对比方法，且在真实机器人上表现良好。PRISM-TopoMap代码已开源，可通过 https://github.com/kirillMouraviev/prism-topomap 获取。