NVMS-SLAM: Normal Vector-Based Multi-Session LiDAR SLAM in Indoor Environments

Multi-session SLAM is essential for long-term robotic operations in indoor environments such as warehouses, office buildings, and industrial facilities. However, the thin walls separating enclosed spaces in such environments introduce a challenge known as the double-sided issue, where point clouds from opposite sides are mistakenly associated as a single surface during single-session mapping, and are prone to being grouped into the same voxel during voxelization in multi-session map fusion, leading to poor voxel planarity, which causes voxel invalidation and reduces the available constraints for global optimization. To address this, we propose NVMS-SLAM, a normal vector-based multi-session LiDAR SLAM system tailored for indoor environments. For single-session mapping, an extended voxel map is designed to preserve normal vector information and to distinguish between primary and secondary surfaces, thereby improving data association. At the multi-session level, a density-encoded indoor scan-context descriptor is introduced for robust loop closure. In addition, a two-stage global map fusion strategy is adopted, combining joint pose graph optimization and normal vector-based bundle adjustment to ensure globally consistent mapping. Experiments on simulated datasets and real-world environments demonstrate that NVMS-SLAM can effectively resolve the double-sided issue at both the single-session and multi-session stages.