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Gas distribution modeling using sparse Gaussian process mixture models
Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany. (AIS)
Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany. (AIS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-0217-9326
Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany. (AIS)
2008 (English)In: Robotics: science and systems IV / [ed] Oliver Brock, Jeff Trinkle, Fabio Ramos, Cambridge, MA: MIT Press, 2008, Vol. 4, p. 310-317Conference paper, Published paper (Other (popular science, discussion, etc.))
Abstract [en]

In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used 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 an EM procedure used for learning the mixture components and the gating function. 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 for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task.

Place, publisher, year, edition, pages
Cambridge, MA: MIT Press, 2008. Vol. 4, p. 310-317
Keywords [en]
Gas distribution modeling, gas sensing, Gaussian processes, mixture models
National Category
Computer Sciences Engineering and Technology
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-3265Scopus ID: 2-s2.0-84960106082ISBN: 978-0-262-51309-8 (print)OAI: oai:DiVA.org:oru-3265DiVA, id: diva2:137562
Conference
International Conference on Robotics Science and Systems, Robotics: science and systems, 2008, Zürich, Switzerland, June 25-28, 2008
Note

Accepted as oral presentation (acceptance rate <15%), selected from these papers as one of the best conference papers

Available from: 2008-11-28 Created: 2008-11-28 Last updated: 2018-06-12Bibliographically approved

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Lilienthal, Achim J.

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Citation style
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