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This paper presents SGM-SLAM, a novel framework enabling data-efficient distributed localization and mapping for multi-robot teams in GPS-denied environments. By leveraging fused RGB-LiDAR point clouds to construct scene graphs, the method allows robots with limited spatial overlap to collaboratively match object data and optimize trajectories despite communication constraints. The approach demonstrates significant improvements in handling viewpoint variations and degraded visual conditions while minimizing bandwidth usage through a multi-step exchange process.
SGM-SLAM: Scene Graph Matching for Data-Efficient Distributed SLAM
arXiv:2606.16881v2 Announce Type: replace Abstract: We introduce a data-efficient distributed Simultaneous Localization and Mapping (SLAM) framework for multi-robot operation in GPS-denied and communication-constrained environments. The framework is designed for teams of robots equipped with LiDAR, cameras, and inertial sensors, where robots may operate from independently initialized local coordinate frames and share only limited spatial overlap. Our approach constructs a scene graph by using fused RGB-LiDAR point clouds to generate both a semantically segmented point cloud layer, and a layer of discrete bounded objects, to accompany estimated robot trajectories. Scene graph matching is performed collaboratively through exchanging and matching object data with neighboring robots. To maximize communication efficiency, we utilize a multi-step data exchange and optimization process. We demonstrate the effectiveness and communication efficiency of the proposed approach using real-world datasets containing limited trajectory overlap, viewpoint variation, repeated structures, and degraded visual conditions.
Abstract:We introduce a data-efficient distributed Simultaneous Localization and Mapping (SLAM) framework for multi-robot operation in GPS-denied and communication-constrained environments. The framework is designed for teams of robots equipped with LiDAR, cameras, and inertial sensors, where robots may operate from independently initialized local coordinate frames and share only limited spatial overlap. Our approach constructs a scene graph by using fused RGB-LiDAR point clouds to generate both a semantically segmented point cloud layer, and a layer of discrete bounded objects, to accompany estimated robot trajectories. Scene graph matching is performed collaboratively through exchanging and matching object data with neighboring robots. To maximize communication efficiency, we utilize a multi-step data exchange and optimization process. We demonstrate the effectiveness and communication efficiency of the proposed approach using real-world datasets containing limited trajectory overlap, viewpoint variation, repeated structures, and degraded visual conditions.
Robotics (cs.RO)
arXiv:2606.16881 [cs.RO]
(or arXiv:2606.16881v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16881 Focus to learn more arXiv-issued DOI via DataCite
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