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.

翻译：我们提出了一种面向多机器人团队的分布式同步定位与地图构建（SLAM）数据高效框架，该框架适用于配备激光雷达、相机和惯性传感器的机器人系统。该框架通过场景图匹配识别机器人间的测量约束。与依赖特征级匹配的现有方法不同，本框架首次实现仅利用对象标签和质心进行场景图匹配。我们通过融合RGB-激光雷达点云构建场景图，生成语义分割点云层和离散有界对象层，以辅助估计机器人轨迹。通过相邻机器人间的对象数据交换与匹配协同完成场景图匹配。为最大化通信效率，我们采用了多步数据交换与优化流程。通过足式机器人在室内外环境采集的仿真数据集和真实场景数据集，验证了该方法的有效性与高效性。