Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity, dependencies on well-structured environments, etc. To overcome these limitations, we propose a new distributed approach to perform relative localization using a Gaussian Processes map of the Radio Signal Strength Indicator (RSSI) values from a single wireless Access Point (AP) to which the robots are connected. Our approach, Gaussian Processes-based Relative Localization (GPRL), combines two pillars. First, the robots locate the AP w.r.t. their local reference frames using novel hierarchical inferencing that significantly reduces computational complexity. Secondly, the robots obtain relative positions of neighbor robots with an AP-oriented vector transformation. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available RSSI measurement. We extensively validate the performance of the two pillars of the proposed GRPL in Robotarium simulations. We also demonstrate the applicability of GPRL through a multi-robot rendezvous task with a team of three real-world robots. The results demonstrate that GPRL outperformed state-of-the-art approaches regarding accuracy, computation, and real-time performance.

翻译：相对定位对于多机器人系统执行协作任务至关重要，特别是在无GPS环境中。当前的多机器人相对定位技术依赖摄像头和激光雷达等高成本或短距离传感器，导致这些算法面临计算复杂度高、依赖结构化环境等挑战。为克服这些局限，我们提出一种全新分布式方法，通过构建机器人所连接单一无线接入点（AP）的接收信号强度指示（RSSI）值的高斯过程地图实现相对定位。我们的方法——基于高斯过程的相对定位（GPRL）——融合两大支柱：首先，机器人通过新型分层推理技术，以自身局部参考系定位AP位置，显著降低计算复杂度；其次，机器人通过面向AP的向量变换获取相邻机器人的相对位置。该方法可直接应用于资源受限设备，仅需依赖于普遍可用的RSSI测量值。我们在Robotarium仿真中对GPRL两大支柱的性能进行了广泛验证，并通过三台实体机器人的多机器人会合任务展示了其适用性。结果表明，GPRL在精度、计算效率和实时性能方面均优于现有最优方法。