多智能体协同定位建图，用单目摄像头实现去中心化高精度导航。
DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems
- 去中心化设计，各机器人独立处理数据不依赖中央节点。
- 仅用单目相机实现与主流集中式系统相当的定位精度。
- 适用于小型机器人和微型无人机，支持实时自主导航。
协作式同时定位与建图（C-SLAM）使多个智能体在未知环境中协同构建地图并估计自身位置。通过智能体间信息共享，该方法提升了系统鲁棒性、可扩展性和定位精度，减少累积漂移，并支持更大范围的集体探索。本文提出首个开源的去中心化单目视觉C-SLAM系统——DVM-SLAM。系统仅依赖低成本、轻量级的单目视觉传感器，特别适合小型机器人和微型飞行器（MAVs）。在真实物理机器人上通过自研避障框架验证了其实际应用能力，展现出在实时多智能体自主导航场景中的潜力。实验表明，其定位精度与当前最先进的集中式单目C-SLAM系统相当。代码及补充材料已公开发布。
原文摘要 · Abstract (English)
Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in mapping unknown environments while simultaneously estimating their own positions. This approach enhances robustness, scalability, and accuracy by sharing information between agents, reducing drift, and enabling collective exploration of larger areas. In this paper, we present Decentralized Visual Monocular SLAM (DVM-SLAM), the first open-source decentralized monocular C-SLAM system. By only utilizing low-cost and light-weight monocular vision sensors, our system is well suited for small robots and micro aerial vehicles (MAVs). DVM-SLAM's real-world applicability is validated on physical robots with a custom collision avoidance framework, showcasing its potential in real-time multi-agent autonomous navigation scenarios. We also demonstrate comparable accuracy to state-of-the-art centralized monocular C-SLAM systems. We open-source our code and provide supplementary material online.
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