English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 .

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