DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm

To execute collaborative tasks in unknown environments, a robotic swarm must 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 front-end agnostic fully distributed collaborative Light Detection And Ranging (LiDAR) SLAM framework to co-localize in an unknown environment with low information exchange. Based on peer-to-peer communication, DCL-SLAM adopts the lightweight LiDAR-Iris descriptor for place recognition and does not require full team connectivity. DCL-SLAM includes three main parts: a replaceable single-robot front-end LiDAR odometry, a distributed loop closure module that detects overlaps between robots, and a distributed back-end module that adapts distributed pose graph optimizer combined with rejecting spurious loop measurements. We integrate the proposed framework with diverse open-source LiDAR odometry to show its versatility. The proposed system is extensively evaluated on benchmarking datasets and field experiments over various scales and environments. The experimental results show that DCL-SLAM achieves higher accuracy and lower bandwidth than other state-of-the-art multirobot LiDAR SLAM systems. The source code and video demonstration are available at https://github.com/PengYu-Team/DCL-SLAM.