MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments

• URL: http://arxiv.org/abs/2506.23514v1
• Date: Mon, 30 Jun 2025 04:35:00 GMT
• Title: MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments
• Authors: Sai Krishna Ghanta, Ramviyas Parasuraman,
• Abstract summary: Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments.<n>This paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points.<n>We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points (AP). To accomplish this, we employ co-regionalized multi-output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty-aware multi-AP localization, which is further coupled with weighted convex hull-based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well-suited for devices with limited computational resources and operates solely on widely available Wi-Fi RSSI measurements without necessitating any dedicated pre-calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments, comparing it against multiple state-of-the-art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency. Finally, we open source MGPRL as a ROS package https://github.com/herolab-uga/MGPRL.

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