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 (e.g., map merging), dependencies on well-structured environments, etc. To remedy this gap, we propose a new distributed approach to perform relative localization (RL) using a common Access Point (AP). To achieve this efficiently, we propose a novel Hierarchical Gaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI) values from a Wi-Fi AP to which the robots are connected. Each robot performs hierarchical inference using the HGP map to locate the AP in its reference frame, and the robots obtain relative locations of the neighboring robots leveraging AP-oriented algebraic transformations. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available WiFi RSSI measurement. We extensively validate the performance of the proposed HGR-PL in Robotarium simulations against several state-of-the-art methods. The results indicate superior performance of HGP-RL regarding localization accuracy, computation, and communication overheads. Finally, we showcase the utility of HGP-RL through a multi-robot cooperative experiment to achieve a rendezvous task in a team of three mobile robots.

翻译：相对定位对于多机器人系统执行协同任务至关重要，尤其在GPS拒止环境中。当前的多机器人相对定位技术依赖于昂贵或短距离传感器，如摄像头与激光雷达。因此，这些算法面临高计算复杂度（如图谱融合）、对结构化环境的依赖等挑战。为弥补这一不足，我们提出一种利用公共接入点（AP）实现相对定位（RL）的新型分布式方法。为实现高效定位，我们提出一种新颖的分层高斯过程（HGP）映射方法，将机器人所连接Wi-Fi接入点的接收信号强度指示（RSSI）值进行映射。每个机器人使用HGP地图在其参考系中通过分层推理定位接入点，并利用面向接入点的代数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，且仅依赖普遍可用的Wi-Fi RSSI测量值。我们在Robotarium仿真环境中将所提出的HGP-RL与多种先进方法进行广泛性能验证。结果表明HGP-RL在定位精度、计算与通信开销方面均表现出优越性能。最后，我们通过三台移动机器人的集结任务协同实验，展示了HGP-RL的实际应用价值。