Multiresolution Representations for Large-Scale Terrain with Local Gaussian Process Regression

Abstract

To address the problem of building accurate and coherent models for large-scale terrains from incomplete and noisy sensor data, this paper proposes a novel framework that can efficiently infer terrain structures by divisionally providing the best linear unbiased estimates for the elevation values. To avoid data ambiguity caused by the uncertainty of sensor data, the proposed method introduces elevation filtering to extract the terrain surfaces, which reduces the amount of data greatly while the contained terrain information is basically unchanged. Then, for the large-scale terrains, the Gaussian mixture model is used to divide the interested regions, which remarkably improves the prediction accuracy and speed. Finally, for each subregion, a gaussian process regression model based on the static kernel is used to create a multiresolution terrain representation, which can deal with incompleteness of sensor data by considering the spatial correlations of the terrain. Evaluations of the proposed technique were conducted on diverse large-scale field terrains, including the quarry, planetary emulation terrain and highland, showing that the proposed method outperforms the state-of-art terrain modeling techniques in terms of the prediction accuracy, computation speed and memory consumption. As a practical application, the path planning problem was explored based on this terrain modeling technique to produce a better path.