Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines

翻译：全局定位对于实现机器人自主性至关重要，而协同定位则是多机器人系统的关键。本文针对计算和通信约束下的协同全局定位任务展开研究。我们提出了一种能够减少信息交换量和计算成本的方法。同时，我们分析、实现并开源了若干基础性方法，相信这将对领域社区产生重要价值。我们利用近线性时间内的分布压缩技术，并提供了误差保证。通过多个具有挑战性的模拟和真实场景，我们对所提方法及实现基线进行了评估。该方法可在机载计算机上在线运行。我们公开了所提方法及基线的C++/ROS2开源实现。