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

2025-06-30

AI Summary

In GPS-denied large-scale indoor environments, multi-robot relative localization suffers from reliance on costly short-range sensors, high computational overhead, and poor adaptability to spatially separated regions. To address these challenges, this paper proposes MGPRL—a Wi-Fi RSSI-based distributed collaborative localization framework. Its core contributions are threefold: (1) the first integration of co-regionalized multi-output Gaussian processes (Co-Regionalized MOGP) with uncertainty-aware multi-AP localization fusion; (2) a novel weighted convex hull alignment mechanism enabling robust, pre-calibration-free, and offline-fingerprint-library-free relative pose estimation; and (3) an end-to-end Wi-Fi-only pipeline comprising online RSSI scanning, MOGP modeling, and distributed convex hull matching. Evaluated in both ROS simulations and real-world deployments, MGPRL achieves superior localization accuracy and computational efficiency compared to state-of-the-art methods. The implementation is publicly released as an open-source ROS package.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Enables Wi-Fi-based relative localization in GPS-denied environments
• Reduces reliance on costly short-range sensors like LiDAR
• Addresses computational overhead in multi-robot localization

Innovation

• Distributed multi-Gaussian Processes for RSSI prediction
• Uncertainty-aware multi-AP localization with convex hulls
• Wi-Fi-based relative pose estimation without calibration