oru.sePublikationer
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
Statistical gas distribution modelling for mobile robot applications
Örebro University, School of Science and Technology, Örebro University, Sweden.
2014 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

In this dissertation, we present and evaluate algorithms for statistical gas distribution modelling in mobile robot applications. We derive a representation of the gas distribution in natural environments using gas measurements collected with mobile robots. The algorithms fuse different sensors readings (gas, wind and location) to create 2D or 3D maps.

Throughout this thesis, the Kernel DM+V algorithm plays a central role in modelling the gas distribution. The key idea is the spatial extrapolation of the gas measurement using a Gaussian kernel. The algorithm produces four maps: the weight map shows the density of the measurements; the confidence map shows areas in which the model is considered being trustful; the mean map represents the modelled gas distribution; the variance map represents the spatial structure of the variance of the mean estimate.

The Kernel DM+V/W algorithm incorporates wind measurements in the computation of the models by modifying the shape of the Gaussian kernel according to the local wind direction and magnitude.

The Kernel 3D-DM+V/W algorithm extends the previous algorithm to the third dimension using a tri-variate Gaussian kernel.

Ground-truth evaluation is a critical issue for gas distribution modelling with mobile platforms. We propose two methods to evaluate gas distribution models. Firstly, we create a ground-truth gas distribution using a simulation environment, and we compare the models with this ground-truth gas distribution. Secondly, considering that a good model should explain the measurements and accurately predicts new ones, we evaluate the models according to their ability in inferring unseen gas concentrations.

We evaluate the algorithms carrying out experiments in different environments. We start with a simulated environment and we end in urban applications, in which we integrated gas sensors on robots designed for urban hygiene. We found that typically the models that comprise wind information outperform the models that do not include the wind data.

Place, publisher, year, edition, pages
Örebro: Örebro university , 2014. , 199 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 62
Keyword [en]
statistical modelling; gas distribution mapping; mobile robots; gas sensors; kernel density estimation; Gaussian kernel
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-37896ISBN: 978-91-7529-034-8 (print)OAI: oai:DiVA.org:oru-37896DiVA: diva2:757230
Public defence
2014-11-19, Långhuset, Hörsal 2, Örebro universitet, Fakultetsgatan 1, Örebro, 15:15 (English)
Opponent
Supervisors
Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2015-12-28Bibliographically approved

Open Access in DiVA

Avhandling(3625 kB)208 downloads
File information
File name FULLTEXT02.pdfFile size 3625 kBChecksum SHA-512
9553716418cc6518d226d62524f2c9e5fbfd59c1ece044346e11945453c370e5c31c169d40fad268cfd50d94440426790a2dc08899fa93fbb2e6ce43a738462c
Type fulltextMimetype application/pdf
Cover(155 kB)15 downloads
File information
File name COVER01.pdfFile size 155 kBChecksum SHA-512
fdb88d605e001f605ce43a4a873bab01626b0c7bae4017af3ba506d3b87323c0d28d8bdb10dc992765622c5f707c10e8825578daa67d65f09bea773537c20011
Type coverMimetype application/pdf
Spikblad(45 kB)4 downloads
File information
File name SPIKBLAD01.pdfFile size 45 kBChecksum SHA-512
612eb40cfcd8ea18b047e8c3df2bbcf8f5e8789313dabb5b9c82052cac7920f407ecd693e62d0e06bdaa352e96ce444d5e9c61861c788a10986b0eccdc906e9e
Type spikbladMimetype application/pdf

Search in DiVA

By author/editor
Reggente, Matteo
By organisation
School of Science and Technology, Örebro University, Sweden
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 208 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

Total: 254 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