Topology-Aware Decision Making for Multi-Session Localization and Mapping

arXiv:2602.17226v2 Announce Type: replace Abstract: 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.

Abstract: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.

Robotics (cs.RO)

arXiv:2602.17226 [cs.RO]

(or arXiv:2602.17226v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.17226 Focus to learn more arXiv-issued DOI via DataCite

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Topology-Aware Decision Making for Multi-Session Localization and Mapping

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