利用拓扑信息的多会话定位与建图
Topology-Aware Decision Making for Multi-Session Localization and Mapping
AI总结：
本研究提出一种基于拓扑信息的多会话框架，通过分析姿态图结构优化建图与定位，减少误差并提升一致性。
摘要：
在已访问环境中操作日益成为自主系统的关键，直接应用于自动驾驶、测绘和仓库或家庭机器人。对同一区域的重复观察给建图和定位——实现更高层次任务的关键组件——带来了重大挑战。在本工作中，我们提出了一种新颖的多会话框架，基于基于地图的定位，而非传统做法中贪心地运行完整SLAM会话并尝试在生成的地图之间寻找对应关系。我们的方法结合了拓扑信息引导、不确定性意识的决策机制，分析姿态图结构以检测低连通性区域，选择性地触发建图和回环关闭模块。所得到的地图和姿态图被无缝整合到现有模型中，减少累积误差并增强全局一致性。我们在重叠序列数据集上验证了我们的方法，并在现实中的类似矿井环境中展示了其有效性。
英文摘要：
Operating in previously visited environments is becoming increasingly crucial for autonomous systems, with direct applications in autonomous driving, surveying, and warehouse or household robotics. This repeated exposure to observing the same areas poses significant challenges for mapping and localization across sessions, particularly in deciding when a prior model is sufficient for reliable localization and when new mapping is required. In this work, we propose a novel multi-session framework that builds on map-based localization, in contrast to the common practice of greedily running full SLAM sessions and trying to find correspondences between the resulting maps. The core contribution is a principled, topology-driven mechanism to detect multi-session mapping needs from the pose-graph structure. Specifically, our approach uses spectral connectivity metrics on the joint pose-graph to identify disconnections and weakly constrained regions, and selectively triggers mapping and loop closing only when the pose-graph topology indicates insufficient structural support. The resulting map and pose-graph are seamlessly integrated into the existing model, reducing accumulated error and enhancing global consistency while avoiding redundant remapping. We validate our method on overlapping sequences from datasets and demonstrate its effectiveness in a real-world mine-like environment.
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