Multi-session Localization and Mapping Exploiting Topological Information

2026-02-19

AI Summary

This work addresses the degradation of multi-session mapping accuracy in autonomous systems operating in repeatedly visited environments, where accumulated drift and map inconsistency pose significant challenges. To this end, the authors propose a topology-aware incremental mapping and localization framework that analyzes the pose graph’s topological structure to identify poorly connected regions. Leveraging an uncertainty-driven mechanism, the system dynamically triggers selective loop closure detection and map fusion, eschewing conventional full SLAM post-alignment strategies. This approach enables efficient and globally consistent map construction across sessions. Extensive experiments on publicly available overlapping sequences and real-world mine tunnel datasets demonstrate the method’s effectiveness, showing a significant reduction in cumulative error and marked improvement in global consistency of multi-session maps.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• multi-session
• localization
• mapping
• topological information
• SLAM

Innovation

• multi-session SLAM
• topology-aware mapping
• uncertainty-aware decision-making
• pose-graph analysis
• map-based localization