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Robust Visual Odometry Estimation of Road Vehicle from Dominant Surfaces for Large Scale Mapping
Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.ORCID iD: 0000-0003-4327-117X
2015 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 9, no 3, 314-322 p.Article in journal (Refereed) Published
Abstract [en]

Every urban environment contains a rich set of dominant surfaces which can provide a solid foundation for visual odometry estimation. In this work visual odometry is robustly estimated by computing the motion of camera mounted on a vehicle. The proposed method first identifies a planar region and dynamically estimates the plane parameters. The candidate region and estimated plane parameters are then tracked in the subsequent images and an incremental update of the visual odometry is obtained. The proposed method is evaluated on a navigation dataset of stereo images taken by a car mounted camera that is driven in a large urban environment. The consistency and resilience of the method has also been evaluated on an indoor robot dataset. The results suggest that the proposed visual odometry estimation can robustly recover the motion by tracking a dominant planar surface in the Manhattan environment. In addition to motion estimation solution a set of strategies are discussed for mitigating the problematic factors arising from the unpredictable nature of the environment. The analyses of the results as well as dynamic environmental strategies indicate a strong potential of the method for being part of an autonomous or semi-autonomous system.

Place, publisher, year, edition, pages
The Institution of Engineering and Technology , 2015. Vol. 9, no 3, 314-322 p.
Keyword [en]
object tracking, road safety, distance measurement, stereo image processing, cameras, road vehicles, pose estimation, motion estimation
National Category
Signal Processing Computer Science
Identifiers
URN: urn:nbn:se:bth-6319DOI: 10.1049/iet-its.2014.0100ISI: 000351633300009Local ID: oai:bth.se:forskinfo23C6F06F396E6D21C1257DAF004FFEA3OAI: oai:DiVA.org:bth-6319DiVA: diva2:833816
Available from: 2015-05-26 Created: 2014-12-15 Last updated: 2016-09-20Bibliographically approved

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Siddiqui, RafidKhatibi, Siamak
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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