oru.sePublications
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
Mini-SLAM: minimalistic visual SLAM in large-scale environments based on a new interpretation of image similarity
Örebro University, Department of Technology. (AASS)ORCID iD: 0000-0002-2953-1564
Dept. of Computing & Informatics, University of Lincoln, Lincoln, United Kingdom. (Dept. of Computing & Informatics)
Örebro University, Department of Technology. (AASS)ORCID iD: 0000-0003-0217-9326
2007 (English)In: 2007 IEEE international conference on robotics and automation (ICRA), New York, NY, USA: IEEE, 2007, p. 4096-4101, article id 4209726Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages.

Place, publisher, year, edition, pages
New York, NY, USA: IEEE, 2007. p. 4096-4101, article id 4209726
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Engineering and Technology Computer and Information Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-4261DOI: 10.1109/ROBOT.2007.364108ISI: 000250915304016Scopus ID: 2-s2.0-36349024788ISBN: 978-1-4244-0601-2 (print)OAI: oai:DiVA.org:oru-4261DiVA, id: diva2:138560
Conference
2007 IEEE international conference on robotics and automation (ICRA), Rome, Italy, 10-14 April, 2007
Available from: 2008-11-26 Created: 2008-11-26 Last updated: 2018-06-12Bibliographically approved

Open Access in DiVA

Mini-SLAM: Minimalistic Visual SLAM in Large-Scale Environments Based on a New Interpretation of Image Similarity(861 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 861 kBChecksum SHA-512
320eb03579217b254195f7fed992d538e103d519ec6f72b7b5dbe9a4993103734aa398ce0c036d53c0e50c4a12a955f38b5710dbd3f2696bba0890e6c8727aaa
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopushttp://www.aass.oru.se/Research/Learning/publications/Andreasson_etal_2007-ICRA07-Mini_SLAM_Minimalistic_Visual_SLAM_in_Large_Scale_Environments.html

Authority records BETA

Andreasson, HenrikLilienthal, Achim J.

Search in DiVA

By author/editor
Andreasson, HenrikLilienthal, Achim J.
By organisation
Department of Technology
Engineering and TechnologyComputer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 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
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 441 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