التفاصيل البيبلوغرافية
| العنوان: |
DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm |
| المؤلفون: |
Zhong, Shipeng, Qi, Yuhua, Chen, Zhiqiang, Wu, Jin, Chen, Hongbo, Liu, Ming |
| سنة النشر: |
2022 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Robotics |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| DOI: |
10.1109/JSEN.2023.3345541 |
| URL الوصول: |
http://arxiv.org/abs/2210.11978 |
| رقم الانضمام: |
edsarx.2210.11978 |
| قاعدة البيانات: |
arXiv |