Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multi-robot operations in environments without an external positioning system, such as indoors, underground or underwater. In this paper, we introduce Swarm-SLAM, an open-source C-SLAM system that is designed to be scalable, flexible, decentralized, and sparse, which are all key properties in swarm robotics. Our system supports inertial, lidar, stereo, and RGB-D sensing, and it includes a novel inter-robot loop closure prioritization technique that reduces communication and accelerates convergence. We evaluated our ROS-2 implementation on five different datasets, and in a real-world experiment with three robots communicating through an ad-hoc network. Our code is publicly available: https://github.com/MISTLab/Swarm-SLAM

翻译：协同同时定位与建图（C-SLAM）是在无外部定位系统环境（如室内、地下或水下）中实现多机器人成功运作的关键组成部分。本文提出Swarm-SLAM，一个开源C-SLAM系统，其设计具备可扩展性、灵活性、去中心化及稀疏性等群体机器人学的关键特性。该系统支持惯性、激光雷达、立体视觉及RGB-D感知，并包含一种新颖的机器人间回环闭合优先级排序技术，可减少通信开销并加速收敛。我们在五个不同数据集上评估了基于ROS-2的实现，并在一个通过自组网通信的三机器人真实世界实验中验证了其性能。代码已开源：https://github.com/MISTLab/Swarm-SLAM