時空間カーネルと共領域化ガウス過程による動的スカラー場再構成のための情報経路計画
Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel
空間と時間で変化するスカラー場を共領域化ガウス過程と時空間相関を考慮した複合カーネルで推定し、既存の情報経路計画アルゴリズムに組み込むことで、時間次元を扱わない従来手法より高精度な推定を実現した。
著者: Lorenzo Booth, Stefano Carpin
分類: cs.RO
原文アブストラクト
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.
