Collaborative Simultaneous Localization and Mapping (CSLAM) is a critical capability for enabling multiple robots to operate in complex environments. Most CSLAM techniques rely on the transmission of low-level features for visual and LiDAR-based approaches, which are used for pose graph optimization. However, these low-level features can lead to incorrect loop closures, negatively impacting map generation.Recent approaches have proposed the use of high-level semantic information in the form of Hierarchical Semantic Graphs to improve the loop closure procedures and overall precision of SLAM algorithms. In this work, we present Multi S-Graphs, an S-graphs [1] based distributed CSLAM algorithm that utilizes high-level semantic information for cooperative map generation while minimizing the amount of information exchanged between robots. Experimental results demonstrate the promising performance of the proposed algorithm in map generation tasks.

翻译：协同同时定位与地图构建（CSLAM）是实现多机器人在复杂环境中运行的关键能力。大多数CSLAM技术依赖于向视觉与激光雷达方法传递低级特征，这些特征用于位姿图优化。然而，这些低级特征可能导致错误的闭环检测，对地图生成产生负面影响。近期研究提出利用分层语义图形式的高级语义信息来改进SLAM算法的闭环检测流程及整体精度。本文提出一种基于S-graphs[1]的分布式CSLAM算法——Multi S-Graphs，该算法利用高级语义信息实现协同地图生成，并最小化机器人间的信息交换量。实验结果表明，该算法在地图生成任务中展现出具有前景的性能。