SGM-SLAM: Scene Graph Matching for Data-Efficient Distributed SLAM

The authors propose SGM-SLAM, a distributed SLAM framework for multi-robot collaboration in GPS-denied, communication-constrained environments, where robots can each initialize their coordinate frames independently and share only limited spatial overlap. The method uses fused RGB-LiDAR point clouds to build a scene graph containing a semantically segmented point cloud layer and a discrete bounded object layer, and performs collaborative matching by exchanging and matching object data with neighboring robots, using multi-step data exchange and optimization to improve communication efficiency. The authors validated effectiveness and communication efficiency on real-world datasets containing limited trajectory overlap, viewpoint variation, repeated structures, and degraded visual conditions.