Gaussian Process-Based Scalar Field Estimation in GPS-Denied Environments

TL;DR

This paper introduces a method for autonomous agents to accurately map scalar fields in GPS-denied environments by combining switching trajectories, error bounds, and stability analysis, validated through simulations.

Contribution

It proposes a novel trajectory design and stability framework for scalar field mapping in GPS-denied areas, ensuring bounded errors during autonomous exploration.

Findings

Effective field mapping demonstrated in simulations.

Bounded error trajectories confirmed by Lyapunov stability analysis.

Comparison shows accurate scalar field predictions.

Abstract

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.