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Relative localization is crucial for multi-robot systems to performcooperative tasks, especially in GPS-denied environments. Current techniquesfor multi-robot relative localization rely on expensive or short-range sensorssuch as cameras and LIDARs. As a result, these algorithms face challenges suchas high computational complexity (e.g., map merging), dependencies onwell-structured environments, etc. To remedy this gap, we propose a newdistributed approach to perform relative localization (RL) using a commonAccess Point (AP). To achieve this efficiently, we propose a novel HierarchicalGaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI)values from a Wi-Fi AP to which the robots are connected. Each robot performshierarchical inference using the HGP map to locate the AP in its referenceframe, and the robots obtain relative locations of the neighboring robotsleveraging AP-oriented algebraic transformations. The approach readily appliesto resource-constrained devices and relies only on the ubiquitously-availableWiFi RSSI measurement. We extensively validate the performance of the proposedHGR-PL in Robotarium simulations against several state-of-the-art methods. Theresults indicate superior performance of HGP-RL regarding localizationaccuracy, computation, and communication overheads. Finally, we showcase theutility of HGP-RL through a multi-robot cooperative experiment to achieve arendezvous task in a team of three mobile robots.

HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems