Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling

Abstract

This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain modeling for a mobile robot. Building a model of large scale and complex terrain that can adequately handle uncertainty and incompleteness in a statistically sound manner is a very challenging problem. To obtain a comprehensive model of such terrain, typically, multiple sensory modalities as well as multiple data sets are required. This work uses Gaussian processes to model large scale terrain. The model naturally provides a multi-resolution representation of space, incorporates and handles uncertainties appropriately and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in unknown areas) elevation information across the field. In this work, the GP modeling approach is extended to fuse multiple, multi-modal data sets to obtain a best estimate of the elevation given the individual data sets. The individual data sets are treated as different noisy samples of the same underlying terrain. Experiments performed on sparse GPS based survey data and dense laser scanner data taken at mine-sites are reported.