DVM-SLAM：面向多智能体系统的去中心化视觉单目同时定位与建图
DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems
AI总结：
本文提出首个开源去中心化单目协作SLAM系统DVM-SLAM，利用低成本单目视觉实现多智能体实时建图与定位，并在实体机器人上验证了其导航可行性和与集中式系统相当的精度。
摘要：
协作式同时定位与建图（C-SLAM）使多个智能体能够在共同建图未知环境的同时估计自身位置。该方法通过智能体间信息共享、减少漂移并支持对更大区域的集体探索，从而增强鲁棒性、可扩展性和精度。本文提出了去中心化视觉单目SLAM（DVM-SLAM），这是首个开源的去中心化单目C-SLAM系统。由于仅使用低成本、轻量化的单目视觉传感器，本系统非常适合小型机器人和微型飞行器（MAV）。DVM-SLAM在搭载自定义碰撞避免框架的实体机器人上验证了其现实世界适用性，展示了其在实时多智能体自主导航场景中的潜力。我们还证明了其精度可与最先进的集中式单目C-SLAM系统相媲美。我们开源了代码并提供在线补充材料。
英文摘要：
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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