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

arXiv: https://arxiv.org/abs/2506.23514 · HTML: https://mlsanity.com/paper/2506.23514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [SARDO: An Automated Search-and-Rescue Drone-based Solution for Victims Localization](https://mlsanity.com/paper/2003.05819/) (2020)
- [Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks](https://mlsanity.com/paper/2105.12797/) (2021)
- [GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model](https://mlsanity.com/paper/1404.5165/) (2014)
- [SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems](https://mlsanity.com/paper/2306.12623/) (2023)
- [3DS-SLAM: A 3D Object Detection based Semantic SLAM towards Dynamic Indoor Environments](https://mlsanity.com/paper/2310.06385/) (2023)
- [Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization](https://mlsanity.com/paper/1508.02324/) (2015)
- [Empirical Analysis of Bi-directional Wi-Fi Network Performance on Mobile Robots in Indoor Environments](https://mlsanity.com/paper/2110.03011/) (2021)
- [HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems](https://mlsanity.com/paper/2307.10614/) (2023)

## Built on by (most cited first, 1 of 1)

- [Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM](https://mlsanity.com/paper/2510.22740/) (2025)

## Abstract (arXiv)

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.