oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning Gas Distribution Models Using Sparse Gaussian Process Mixtures
University of Freiburg. (AIS)
Stanford University. (Computer Science Dept.)
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)ORCID iD: 0000-0003-0217-9326
2009 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 26, no 2-3, p. 187-202Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions 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 formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply 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. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics.

Place, publisher, year, edition, pages
Springer, 2009. Vol. 26, no 2-3, p. 187-202
Keywords [en]
Gas distribution modeling, Gas sensing, Gaussian processes, Mixture models
National Category
Engineering and Technology Other Computer and Information Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-8432DOI: 10.1007/s10514-009-9111-5ISI: 000265684000007Scopus ID: 2-s2.0-67349215695OAI: oai:DiVA.org:oru-8432DiVA, id: diva2:274845
Projects
EU FP7 STREP DiademEU FP6 STREP DustbotAvailable from: 2009-11-08 Created: 2009-11-02 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

fulltext(4044 kB)667 downloads
File information
File name FULLTEXT01.pdfFile size 4044 kBChecksum SHA-512
3d6a55e0d289829e8fdfd7142e2e366728cb78449dc880e9db4d0c2245f88502ba2225b67e7b5b002797e71a9dfc1ef9c9d66c6434b85681209284d39b5a303e
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Lilienthal, Achim J.

Search in DiVA

By author/editor
Lilienthal, Achim J.
By organisation
School of Science and Technology
In the same journal
Autonomous Robots
Engineering and TechnologyOther Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 667 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 416 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf