This paper presents a methodology for an autonomous agent to map an unknown scalar field in GPS-denied regions. To reduce localization errors, the agent alternates between GPS-enabled and GPS-denied areas while collecting measurements. User-defined error bounds determine the dwell time in each region. A switching trajectory is then designed to ensure field measurements in GPS-denied regions remain within the specified error limits. A Lyapunov-based stability analysis guarantees bounded error trajectories while tracking the desired path. The effectiveness of the proposed methodology is demonstrated through simulations, with an error analysis comparing the GP-predicted scalar field model to the actual field.
@article{arxiv.2502.17584,
title = {Gaussian Process-Based Scalar Field Estimation in GPS-Denied Environments},
author = {Muzaffar Qureshi and Tochukwu Elijah Ogri and Humberto Ramos and Zachary I. Bell and Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:2502.17584},
year = {2025}
}