上与下：跨水面和水下领域的异构多机器人SLAM
Above and Below: Heterogeneous Multi-robot SLAM Across Surface and Underwater Domains
AI总结：
本文提出一种基于循环闭合的中央多机器人SLAM系统，用于跨水面和水下领域的异构机器人协作定位与建图，通过融合不同环境下的数据提升定位精度。
摘要：
多机器人同步定位与建图（SLAM）是多机器人操作中的基本任务。机器人必须对自身和团队成员的位置有共同的理解才能完成协同行动。然而，无人水面船（USVs）和自主水下车辆（AUVs）之间的多机器人SLAM主要通过机器人之间的声学ping来获取距离测量；这种测量技术要求机器人同时位于相似位置，有不间断的信号传播路径，并可能需要同步时钟。这在复杂的、拥挤的海洋环境中尤其具有挑战性，其中结构可能阻碍信号。然而，这些相同的结构可能在水面以上和以下都可见，为USV和AUV数据流之间的机器人SLAM循环闭合提供了机会。本文基于最近关于USV和AUV数据流之间机器人SLAM循环闭合的研究，将其扩展为提出一个集中式的多机器人SLAM系统。每台机器人执行其状态估计，并检测每台AUV与USV数据之间的循环闭合。这些机器人之间的循环闭合用于将每台机器人的状态估计合并到集中化的图中，从而得到整个USV时间历史和系统内所有AUVs的状态估计。使用真实世界感知数据在三个不同环境中进行验证。结果表明，与单机器人SLAM相比，多机器人SLAM系统在相同轨迹上的AUV误差有所改善。据我们所知，这是首次基于循环闭合而非声学距离测量构建的多机器人SLAM系统，涉及AUVs和USVs。
英文摘要：
Multi-robot simultaneous localization and mapping (SLAM) is a fundamental task in multi-robot operations. Robots must have a common understanding of their location and that of their team members to complete coordinated actions. However, multi-robot SLAM between Uncrewed Surface Vessels (USVs) and Autonomous Underwater Vehicles (AUVs) has primarily been achieved through acoustic pinging between robots to retrieve range measurements; a measurement technique requires that robots to be in similar locations simultaneously, have an uninterrupted path for signal propagation, and may necessitate synchronized clocks. This is especially challenging in complex, cluttered maritime environments, where structures may impede signals. However, these same structures may be observable above and below the water's surface, presenting an opportunity for inter-robot SLAM loop closure between USV and AUV data streams. This work builds upon recent research on inter-robot SLAM loop closure between USV and AUV data, extending it to propose a centralized multi-robot SLAM system. Each robot performs its state estimation, and we detect loop closures between each AUV and the USV data. These inter-robot loop closures are used to merge each robot's state estimate into a centralized graph, yielding estimates for the whole time history of the USV and all AUVs in the system. Validation is performed using real-world perceptual data in three different environments. Results show improved errors for AUVs in the multi-robot SLAM system compared to single-robot SLAM over the same trajectories. To our knowledge, this is the first instance of a multi-robot SLAM system with AUVs and USVs built on loop closures rather than acoustic distance measurements.
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。
