ISR-SLAM: Multi-Agent 3DGS SLAM via Inter-Submap Reinforcement

Multi-agent simultaneous localization and mapping (SLAM) based on 3D Gaussian Splatting (3DGS) enables high-fidelity collaborative 3D reconstruction. A conventional pipeline partitions each agent's observations into short temporal segments and constructs a local submap with 3DGS for each segment. Reconstructed independently, the local 3DGS submaps are registered into a global map by optimizing inter-submap relative poses, after which the global 3DGS map is refined as a whole. However, this procedure breaks up submap registration and global map refinement. Consequently, defects in submaps can undermine the registration, which in turn hinders the global map refinement since submaps are incorrectly registered. With this insight, we propose ISR-SLAM, a multi-agent 3DGS SLAM framework that creates an online feedback loop between submap registration and global map refinement. As new submaps income, we implement periodical registration of all available submaps, ensuring subsequent tracking and mapping to proceed with the optimal registration up-to-date. Simultaneous to registration, we leverage cross-agent observation overlap to refine 3DGS submaps. The refined submaps, in turn, facilitates subsequent registration, forming a mutually reinforcing cycle. After merging all submaps, ISR-SLAM further refines the overlapped regions and submap boundaries to reduce residual inconsistencies. Extensive experiments on synthetic and real-world multi-agent datasets demonstrate that ISR-SLAM improves localization accuracy, reconstruction quality, and global map consistency significantly over existing methods.