  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.
