The Cooperative Localization (CL) problem considers the case where groups of robots aim to improve their overall localization by sharing position estimates within the team instead of using landmarks in the environment. Despite being a well-studied problem, very few works deal with the increased complexity when a very large number of robots is used, as is the case in robotic swarms. In this paper, we propose a methodology to perform cooperative localization in robotic swarms while they navigate through the environment. A collective motion strategy maintains the group’s cohesion, which allows each robot to perform the localization using information from its immediate neighbors. The method is based on the Covariance Intersection (CI) algorithm, which is employed to perform the localization in a decentralized way. Experiments in both simulation and real-world scenarios show the feasibility of the proposed approach. Furthermore, it overcomes and has a reduced space usage and time complexity compared to a traditional centralized EKF-based method. We also investigate how the methodology can be used by a robotic swarm to build occupancy grid maps, a task that generally requires more sophisticated robots and is heavily dependent on good localization estimates.

协作定位 (CL) 问题考虑了以下情况：机器人组旨在通过在团队内共享位置估计而不是使用环境中的地标来改善其整体定位。尽管是一个经过充分研究的问题，但很少有工作涉及使用大量机器人时增加的复杂性，就像机器人群中的情况。在本文中，我们提出了一种在机器人群在环境中导航时执行协作定位的方法。集体运动策略保持了团队的凝聚力，这使每个机器人都可以使用来自其直接邻居的信息来执行定位。该方法基于协方差交集 (CI) 算法，该算法用于以分散的方式执行定位。模拟和现实场景中的实验表明了所提出方法的可行性。此外，与传统的基于集中式 EKF 的方法相比，它克服并降低了空间使用和时间复杂度。我们还研究了机器人群如何使用该方法来构建占用网格图，这项任务通常需要更复杂的机器人，并且在很大程度上依赖于良好的定位估计。