RMGS-SLAM: Real-time Multi-sensor Gaussian Splatting SLAM

RMGS-SLAM enters the rapidly evolving 'GS-SLAM' (Gaussian Splatting SLAM) niche, differentiating itself by moving beyond purely visual inputs to a tightly coupled LiDAR-Inertial-Visual (LIV) approach. While projects like SplaTAM and MonoGS have demonstrated 3DGS for SLAM in indoor or small-scale settings, RMGS-SLAM targets the engineering complexity of large-scale environments where visual-only systems suffer from scale drift and lack of geometric constraints. The 11 forks within 3 days of release, despite 0 stars, indicate high academic and industry interest in sensor-fused 3DGS. However, its defensibility is currently low (4) because it is a fresh research release without an established community or easy-to-use production API. The 'LIV' aspect is a high-barrier engineering task, but as 3DGS techniques stabilize, major robotics players (e.g., Waymo, Skydio, or Apple's AR team) are likely to internalize these multi-sensor optimizations. The displacement horizon is 1-2 years as the field shifts from 'how to do GS-SLAM' to 'how to make it robust for production', likely leading to consolidation into a few highly optimized kernels.