oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping
University of Màlaga, Màlaga, Spain.
University of Màlaga, Màlaga, Spain.
University of Màlaga, Màlaga, Spain.
Örebro University, School of Science and Technology, Örebro University, Sweden. (( AASS ) MRO Lab)ORCID iD: 0000-0003-0217-9326
2013 (English)Conference paper (Refereed)
Abstract [en]

Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method.

Place, publisher, year, edition, pages
ACM Digital Library, 2013. 217-222 p.
Keyword [en]
Kalman Filter, Gas Distribution Mapping, Mobile Olfaction
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-32672DOI: 10.1145/2480362.2480409Scopus ID: 2-s2.0-84877944182ISBN: 978-1-4503-1656-9 (print)OAI: oai:DiVA.org:oru-32672DiVA: diva2:676862
Conference
28th ACM Symposium on Applied Computing (SAC 2013), Special Track on Intelligent Robotics Systems
Note

© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in 28th ACM Symposium on Applied Computing (SAC 2013), 2013} http://doi.acm.org/10.1145/2480362.2480409"

Available from: 2013-12-07 Created: 2013-12-07 Last updated: 2017-03-20Bibliographically approved

Open Access in DiVA

fulltext(1359 kB)314 downloads
File information
File name FULLTEXT01.pdfFile size 1359 kBChecksum SHA-512
bf6b8442cc0fdc56f4c96764eb43f6d7da0dffda16334bf5de1ec0711c467d5e9a7388c91d8d98ef56a98e1d889e689744e8d14e39a7c8967a202364f51773a6
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopushttp://dl.acm.org/citation.cfm?id=2480409

Search in DiVA

By author/editor
Lilienthal, Achim J.
By organisation
School of Science and Technology, Örebro University, Sweden
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 314 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

Altmetric score

Total: 305 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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