To execute collaborative tasks in unknown environments, a robotic swarm needs to establish a global reference frame and locate itself in a shared understanding of the environment. However, it faces many challenges in real-world scenarios, such as the prior information about the environment being absent and poor communication among the team members. This work presents DCL-SLAM, a fully distributed collaborative LiDAR SLAM framework intended for the robotic swarm to simultaneously co-localize in an unknown environment with minimal information exchange. Based on ad-hoc wireless peer-to-peer communication (limited bandwidth and communication range), DCL-SLAM adopts the lightweight LiDAR-Iris descriptor for place recognition and does not require full connectivity among teams. DCL-SLAM includes three main parts: a replaceable single-robot front-end that produces LiDAR odometry results; a distributed loop closure module that detects inter-robot loop closures with keyframes; and a distributed back-end module that adapts distributed pose graph optimizer combined with a pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures. We integrate our proposed framework with diverse open-source LiDAR odometry methods to show its versatility. The proposed system is extensively evaluated on benchmarking datasets and field experiments over various scales and environments. Experimental result shows that DCL-SLAM achieves higher accuracy and lower communication bandwidth than other state-of-art multi-robot SLAM systems. The full source code is available at https://github.com/zhongshp/DCL-SLAM.git.

翻译：在未知环境中执行协同任务时，机器人集群需要建立全局参考框架，并在对环境的共享理解中进行自身定位。然而，实际应用场景面临诸多挑战，例如环境先验信息缺失及团队成员间通信条件恶劣。本文提出DCL-SLAM——一个完全分布式的协同LiDAR SLAM框架，旨在使机器人集群能在未知环境中以最小信息交换实现协同定位。该框架基于自组织无线点对点通信（有限带宽与通信范围），采用轻量级LiDAR-Iris描述符进行地点识别，无需集群内节点全连接。DCL-SLAM包含三大核心模块：可替换的单机器人前端（生成LiDAR里程计结果）、分布式回环检测模块（通过关键帧检测机器人间回环）以及分布式后端模块（结合分布式位姿图优化器与成对一致测量集最大化算法以剔除虚假机器人间回环）。我们通过与多种开源LiDAR里程计算法集成验证了本框架的通用性。该系统在跨尺度、多环境的基准数据集及现场实验中进行了全面评估。实验结果表明，DCL-SLAM在定位精度与通信带宽消耗方面均优于其他先进多机器人SLAM系统。完整源代码开源地址：https://github.com/zhongshp/DCL-SLAM.git。