用物体标签和中心点实现多机器人高效建图，减少数据传输。
SGM-SLAM: Scene Graph Matching for Data-Efficient Distributed SLAM
- 通过物体标签与中心点构建场景图，跨机器人匹配约束。
- 在真实环境的室内外测试中，显著降低通信量并保持建图精度。
- 适合资源受限的多机器人协同导航任务。
我们提出一种面向配备激光雷达、相机和惯性传感器的机器人团队的数据高效分布式同步定位与地图构建（SLAM）框架。该框架利用场景图匹配识别跨机器人测量约束。与依赖特征级匹配的以往方法不同，本框架首次仅使用物体标签和质心进行场景图匹配。通过融合RGB-LiDAR点云，生成语义分割点云层与离散有界物体层，并结合估计的机器人轨迹构建场景图。场景图通过与邻近机器人交换并匹配物体数据实现协同匹配。为最大化通信效率，采用多步数据交换与优化流程。我们在模拟及腿式机器人在室内外环境中采集的真实数据集上验证了该方法的有效性与高效性。
原文摘要 · Abstract (English)
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.
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