DCL-SLAM：面向机器人集群的分布式协作激光雷达SLAM框架
DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm
AI总结：
提出DCL-SLAM，一个完全分布式的协作激光雷达SLAM框架，通过轻量级描述符和分布式优化实现机器人集群在未知环境中的协同定位，具有高精度和低通信带宽。
摘要：
为了在未知环境中执行协作任务，机器人集群需要建立全局参考系，并在对环境的共同理解中定位自身。然而，在实际场景中，它面临许多挑战，例如环境先验信息缺失以及团队成员之间通信不良。本工作提出了DCL-SLAM，一个完全分布式的协作激光雷达SLAM框架，旨在使机器人集群在未知环境中以最少的信息交换同时进行协同定位。基于自组织无线点对点通信（有限带宽和通信范围），DCL-SLAM采用轻量级LiDAR-Iris描述符进行位置识别，且不需要团队间的完全连接。DCL-SLAM包括三个主要部分：一个可替换的单机器人前端，产生激光雷达里程计结果；一个分布式回环检测模块，利用关键帧检测机器人间的回环；以及一个分布式后端模块，采用自适应分布式位姿图优化器结合成对一致测量集最大化算法来拒绝虚假的机器人间回环。我们将所提出的框架与多种开源激光雷达里程计方法集成，以展示其通用性。所提出的系统在基准数据集和实地实验中进行了广泛评估，覆盖了不同规模和环境。实验结果表明，DCL-SLAM在准确性和通信带宽方面优于其他最先进的多机器人SLAM系统。完整源代码可在https://github.com/zhongshp/DCL-SLAM.git获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
