Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel

TL;DR

This paper introduces a novel coregionalized Gaussian process model with a spatiotemporal kernel for informative path planning, significantly improving dynamic scalar field estimation in environmental monitoring tasks.

Contribution

The work presents a new modeling approach that incorporates spatiotemporal correlations into Gaussian processes, enhancing IPP for dynamic fields and integrating seamlessly with existing algorithms.

Findings

More accurate scalar field estimations in simulations

Improved performance over previous methods ignoring temporal data

Effective integration with existing IPP algorithms

Abstract

The proliferation of unmanned vehicles offers many opportunities for solving environmental sampling tasks with applications in resource monitoring and precision agriculture. Informative path planning (IPP) includes a family of methods which offer improvements over traditional surveying techniques for suggesting locations for observation collection. In this work, we present a novel solution to the IPP problem by using a coregionalized Gaussian processes to estimate a dynamic scalar field that varies in space and time. Our method improves previous approaches by using a composite kernel accounting for spatiotemporal correlations and at the same time, can be readily incorporated in existing IPP algorithms. Through extensive simulations, we show that our novel modeling approach leads to more accurate estimations when compared with formerly proposed methods that do not account for the temporal dimension.