Robotics: Science and Systems IV

Gas Distribution Modeling using Sparse Gaussian Process Mixture Models

Cyrill Stachniss, Christian Plagemann, Achim Lilienthal, Wolfram Burgard

Abstract: In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps, which are able to accurately predict the gas concentration at query locations, is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into the EM procedure used to learn the mixture components and the gating function. In this way, we are able to overcome a serious drawback of Gaussian processes, namely their computational complexity. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited to predict the concentration as well as the uncertainty at new query locations and yield higher data likelihoods than other methods.

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Bibtex:

@INPROCEEDINGS{Stachniss-RSS08,
    AUTHOR    = {Cyrill Stachniss, Christian Plagemann, Achim Lilienthal, Wolfram Burgard},
    TITLE     = {Gas Distribution Modeling using Sparse Gaussian Process Mixture Models},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems IV},
    YEAR      = {2008},
    ADDRESS   = {Zurich, Switzerland},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2008.IV.040} 
}