不確かな入力に対応する確率的変分ガウス過程による地形マップ構築
Fully-probabilistic Terrain Modelling with Stochastic Variational Gaussian Process Maps
入力ノイズを考慮した確率的変分ガウス過程で大規模地形マップを構築し、AUVの測深データで検証した研究。
著者: Ignacio Torroba, Christopher Illife Sprague, John Folkesson
分類: cs.RO
原文アブストラクト
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.
