Multi-session Localization and Mapping Exploiting Topological Information

TL;DR

This paper introduces a multi-session localization framework that leverages topological information to improve map consistency and reduce errors in environments revisited multiple times, enhancing autonomous navigation.

Contribution

The work presents a novel topology-informed, uncertainty-aware approach for multi-session mapping that selectively triggers mapping modules based on pose-graph analysis, unlike traditional full SLAM methods.

Findings

Improved map accuracy and consistency in multi-session environments.

Effective detection of low-connectivity regions for targeted mapping.

Validated on real-world mine-like environment data.

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.