Fully-probabilistic Terrain Modelling with Stochastic Variational Gaussian Process Maps

TL;DR

This paper introduces a flexible stochastic variational Gaussian process framework for terrain mapping that effectively incorporates uncertain inputs, improving map accuracy and robot localization in large-scale, real-world surveys.

Contribution

It presents a novel scalable method using Stochastic Variational GPs and Monte Carlo sampling to handle uncertain inputs in terrain mapping, surpassing previous limitations.

Findings

UI SVGP maps outperform deterministic input maps in accuracy.

The approach enables large-scale terrain modeling with uncertainty quantification.

Enhanced localization results demonstrate practical benefits in real AUV missions.

Abstract

Gaussian processes (GPs) are becoming a standard tool to build terrain representations thanks to their capacity to model map uncertainty. This effectively yields a reliability measure of the areas of the map, which can be directly utilized by Bayes filtering algorithms in robot localization problems. A key insight is that this uncertainty can incorporate the noise intrinsic to the terrain surveying process through the GPs ability to train on uncertain inputs (UIs). However, existing techniques to build GP maps with UIs in a tractable manner are restricted in the form and degree of the input noise. In this letter, we propose a flexible and efficient framework to build large-scale GP maps with UIs based on Stochastic Variational GPs and Monte Carlo sampling of the UIs distributions. We validate our mapping approach on a large bathymetric survey collected with an AUV and analyze its performance against the use of deterministic inputs (DI). Finally, we show how using UI SVGP maps yields more accurate particle filter localization results than DI SVGP on a real AUV mission over an entirely predicted area.