Multi-session Localization and Mapping Exploiting Topological Information

• URL: http://arxiv.org/abs/2602.17226v1
• Date: Thu, 19 Feb 2026 10:17:46 GMT
• Title: Multi-session Localization and Mapping Exploiting Topological Information
• Abstract summary: We propose a novel multi-session framework that builds on map-based localization.<n>Our approach incorporates a topology-informed, uncertainty-aware decision-making mechanism.<n>We validate our method on sequences overlapping from datasets and demonstrate its effectiveness in a real-world mine-like environment.
• Score: 1.3316173544577008
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 -- key components for enabling any higher-level task. 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. Our approach incorporates a topology-informed, uncertainty-aware decision-making mechanism that analyzes the pose-graph structure to detect low-connectivity regions, selectively triggering mapping and loop closing modules. The resulting map and pose-graph are seamlessly integrated into the existing model, reducing accumulated error and enhancing global consistency. We validate our method on overlapping sequences from datasets and demonstrate its effectiveness in a real-world mine-like environment.

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