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：建图是实现移动机器人自主导航的关键任务之一。传统建图方法输出稠密几何地图表示（如占据网格），但在长时间覆盖大范围环境时难以保持一致性。而捕捉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较小。基于此思想，本文提出PRISM-TopoMap——一种不依赖全局度量坐标、维护局部对齐位置图结构的拓扑建图方法。该方法将可学习多模态位置识别与扫描匹配管线相结合，用于位置图中的定位与闭环检测。该图结构可在线更新，机器人每个时间步均被定位至对应节点。我们在多种逼真环境及真实机器人（轮式差速驱动Husky机器人）上对建议方法进行了广泛实验评估，并与现有最优方法进行了比较。实证评估结果证实，PRISM-TopoMap在建图与导航效率的多项指标上均持续优于对比方法，且在真实机器人上表现良好。PRISM-TopoMap代码已开源，访问地址为https://github.com/kirillMouraviev/prism-topomap。