GauSem-SLAM: Gaussian Semantic Submaps with Loop Closure for Globally Consistent SLAM

Abstract

3DGS has shown outstanding performance in multi-view geometry, driving its adoption in visual SLAM. However, real-time semantic 3DGS mapping faces challenges. Current methods typically treat semantics as external priors, making it hard to integrate them into SLAM tracking or loop closure correction. Moreover, traditional semantic SLAM corrects accumulated drift by applying rigid adjustments to dense point clouds, which is costly for 3DGS maps and limits loop closure performance. We propose GauSem-SLAM, which uses a Gaussian semantic submap representation with a progressive allocation strategy, integrating semantics into tracking, mapping, loop detection, and submap management. We fully exploit semantic information by designing a robust loop detection module that combines DINOv2 semantic features with 3D semantic landmarks. Furthermore, we introduce Semantic-Guided Registration (SGR), a method for computing inter-submap loop constraints. Through intra-submap and inter-submap loop correction, followed by a two-stage global map refinement, our system achieves globally consistent pose estimation and mapping. Experiments on three public datasets demonstrate that our method outperforms prior methods in both tracking and mapping.