Swarm-SLAM：面向多机器人系统的稀疏去中心化协同同时定位与建图框架
Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems
AI总结：
本文提出开源多机器人协同SLAM系统Swarm-SLAM，通过稀疏去中心化架构和回环闭合优先级排序技术，在减少通信的同时加速收敛，支持多种传感器并在多数据集和真实三机器人实验中验证。
摘要：
协同同时定位与建图（C-SLAM）是在没有外部定位系统的环境（如室内、地下或水下）中成功执行多机器人任务的关键组成部分。本文介绍了 Swarm-SLAM，一个开源的 C-SLAM 系统，其设计具备可扩展性、灵活性、去中心化和稀疏性，这些都是群体机器人技术中的关键特性。该系统支持惯性、激光雷达、立体视觉和 RGB-D 感知，并包含一种新颖的机器人间回环闭合优先级排序技术，可减少通信量并加速收敛。我们在五个不同数据集上评估了 ROS-2 实现，并在一个由三台机器人通过自组织网络通信的真实世界实验中进行了验证。代码已公开：https://github.com/MISTLab/Swarm-SLAM
英文摘要：
Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multi-robot operations in environments without an external positioning system, such as indoors, underground or underwater. In this paper, we introduce Swarm-SLAM, an open-source C-SLAM system that is designed to be scalable, flexible, decentralized, and sparse, which are all key properties in swarm robotics. Our system supports inertial, lidar, stereo, and RGB-D sensing, and it includes a novel inter-robot loop closure prioritization technique that reduces communication and accelerates convergence. We evaluated our ROS-2 implementation on five different datasets, and in a real-world experiment with three robots communicating through an ad-hoc network. Our code is publicly available: https://github.com/MISTLab/Swarm-SLAM
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。
