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Non-stationary dependent Gaussian processes for data fusion in large-scale terrain modeling
| Citations: | 4 - 3 self |
BibTeX
@MISC{Vasudevan_non-stationarydependent,
author = {Shrihari Vasudevan and Fabio Ramos and Eric Nettleton and Hugh Durrant-whyte},
title = {Non-stationary dependent Gaussian processes for data fusion in large-scale terrain modeling},
year = {}
}
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Abstract
Abstract — Obtaining a comprehensive model of large and complex terrain typically entails the use of both multiple sensory modalities and multiple data sets. This paper demonstrates the use of dependent Gaussian processes for data fusion in the context of large scale terrain modeling. Specifically, this paper derives and demonstrates the use of a non-stationary kernel (Neural Network) in this context. Experiments performed on multiple large scale (spanning about 5 sq km) 3D terrain data sets obtained from multiple sensory modalities (GPS surveys and laser scans) demonstrate the approach to data fusion and provide a preliminary demonstration of the superior modeling capability of Gaussian processes based on this kernel. I.
Keyphrases
data fusion non-stationary dependent gaussian process large-scale terrain modeling multiple sensory modality superior modeling capability large scale terrain modeling multiple data set gaussian process sq km complex terrain terrain data set multiple large scale dependent gaussian process preliminary demonstration comprehensive model gps survey neural network non-stationary kernel
