SGM-SLAM: Scene Graph Matching for Data-Efficient Distributed SLAM
2026-06-15 • Robotics
Robotics
AI summaryⓘ
The authors created a system that helps a group of robots figure out where they are and map their surroundings together. Instead of matching tiny details in images, their method uses simple labels and center points of objects to connect information between robots. They combine data from cameras and LiDAR to build easy-to-understand maps made of objects and robot paths. The robots share only important object info with each other to save communication effort. The authors tested their system in both simulations and real places with walking robots and it worked well.
SLAMLiDARScene GraphSemantic SegmentationMulti-robot SystemsData FusionInertial SensorsObject MatchingDistributed OptimizationLegged Robots
Authors
Yewei Huang, Tixiao Shan, Abhinav Rajvanshi, Niluthpol Chowdhury Mithun, Yaxuan Li, Brendan Englot, Han-Pang Chiu
Abstract
We introduce a data-efficient distributed Simultaneous Localization and Mapping (SLAM) framework designed for a team of robots equipped with LiDAR, cameras, and inertial sensors. Our framework uses scene graph matching to identify inter-robot measurement constraints. Unlike prior approaches that rely on feature-level matching, our framework is the first to perform scene graph matching using only object labels and centroids. 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 efficiency of our approach using both simulation and real-world datasets collected by legged robots in indoor and outdoor environments.