发表机构
Singapore University of Technology and Design; Nanyang Technological University（新加坡科技设计大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对同构多机器人系统外观相似难以区分的问题，提出融合LiDAR、UWB与里程计的分布式相对定位方法，仅需机载UWB交换里程计数据，经仿真和实验验证其有效可靠。
AI 中文摘要
准确可靠的相对定位对于多机器人应用（如探索、搜救任务）至关重要。基于LiDAR的解决方案在定位周围物体时精度较高，但由于缺乏独特识别特征，区分外观相似的同构机器人仍具挑战性。本文提出一种完全分布式的相对位姿估计方法，通过融合LiDAR、UWB和里程计测量值，使每个机器人无需外部基础设施即可准确、持续地定位队友。具体而言，动态跟踪器从LiDAR扫描中跟踪潜在的匿名队友机器人集群，随后采用联合匹配策略从这些跟踪的匿名集群中识别队友机器人，确保机器人与集群间的可靠数据关联。最后，结合对应的LiDAR观测、UWB测距和里程计测量值，每个机器人在最小化数据交换的同时精确定位其他机器人。该系统仅需通过机载UWB交换里程计数据，无需WiFi路由器或网状网络等额外通信基础设施。大量仿真和真实实验验证了所提相对定位方法的有效性和可靠性。
英文摘要
Accurate and reliable relative localization is crucial for multi-robot applications like exploration, search, and rescue missions. LiDAR-based solutions offer high accuracy in localizing surrounding objects; however, distinguishing homogeneous robots with similar appearances remains challenging due to the lack of distinctive identification features. In this paper, we propose a fully distributed relative pose estimation approach by integrating LiDAR, UWB, and odometry measurements, allowing each robot to accurately and continuously localize its teammates without external infrastructure. Specifically, potential anonymous teammate robot clusters from LiDAR scans are tracked by a dynamic tracker. We then identify teammate robots from these tracked anonymous clusters using a joint matching strategy, ensuring reliable data association between robots and clusters. Finally, by combining the corresponding LiDAR observations, UWB ranging, and odometry measurements, each robot precisely localizes others while minimizing data exchange. The system requires only odometry data exchange through onboard UWB, eliminating the need for additional communication infrastructure like WiFi routers or mesh networks. Extensive simulation and real-world experiments demonstrate the effectiveness and reliability of the proposed relative localization approach.
CommentsThis paper has been published in IEEE/ASME Transactions on Mechatronics
