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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, dependencies on well-structured environments,etc. To overcome these limitations, we propose a new distributed approach toperform relative localization using a Gaussian Processes map of the RadioSignal Strength Indicator (RSSI) values from a single wireless Access Point(AP) to which the robots are connected. Our approach, Gaussian Processes-basedRelative Localization (GPRL), combines two pillars. First, the robots locatethe AP w.r.t. their local reference frames using novel hierarchical inferencingthat significantly reduces computational complexity. Secondly, the robotsobtain relative positions of neighbor robots with an AP-oriented vectortransformation. The approach readily applies to resource-constrained devicesand relies only on the ubiquitously-available RSSI measurement. We extensivelyvalidate the performance of the two pillars of the proposed GRPL in Robotariumsimulations. We also demonstrate the applicability of GPRL through amulti-robot rendezvous task with a team of three real-world robots. The resultsdemonstrate that GPRL outperformed state-of-the-art approaches regardingaccuracy, computation, and real-time performance.

GPRL: Gaussian Processes-Based Relative Localization for Multi-Robot Systems