Scalable Multi-Session Visual SLAM in Large-Scale Scenes with Subgraph Optimization

Abstract

Multi-session visual SLAM systems enable 6-DoF camera localization along with long-term maintenance and expansion of the global map, by utilizing image data from different sessions. However, in large-scale environments, these systems often suffer from severe scale drift. While modern SLAM systems attempt to maintain global map consistency through loop detection and correction, they still face challenges in terms of convergence and accuracy. In this paper, we propose a robust large-scale multi-session SLAM system for long-term localization and mapping that achieves global consistency. Furthermore, to address the backend optimization problem in large-scale environments, we introduce a hierarchical optimization strategy based on the graph structure. More specifically, a subgraph structure is introduced to reduce the size of problem while effectively propagating scale correction information. In addition, a hierarchical strategy enables coarse-to-fine updates of the graph states. Experimental results not only demonstrate that our method efficiently optimizes the pose graph and maintains map consistency in large-scale environments, but also highlight the effectiveness and scalability of the proposed approach.