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

arXiv: https://arxiv.org/abs/2210.11978 · HTML: https://mlsanity.com/paper/2210.11978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM](https://mlsanity.com/paper/2210.13723/) (2022)
- [Self-Localized Collaborative Perception](https://mlsanity.com/paper/2406.12712/) (2024)
- [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025)
- [Geometric Multi-Session Map Merging with Learned Local Descriptors](https://mlsanity.com/paper/2512.24384/) (2025)

## Abstract (arXiv)

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.