SGM-SLAM: Scene Graph Matching for Data-Efficient Distributed SLAM
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Yewei Huang and co-authors propose SGM-SLAM, a distributed SLAM framework for multi-robot collaboration in GPS-denied, communication-constrained environments. Each robot can initialize its coordinate frame independently and only needs to share limited spatially overlapping regions. The method builds scene graphs from fused RGB-LiDAR point clouds, comprising a semantically segmented point cloud layer and a discrete bounded-object layer; robots exchange and match object data to achieve collaboration, and improve communication efficiency through multi-step data exchange and optimization. The authors report that the method's effectiveness and communication efficiency were validated on real-world datasets containing limited trajectory overlap, viewpoint changes, repetitive structures, and visually degraded conditions.
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- arXiv cs.ROSGM-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.
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