  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.
