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Multimodal Scale Estimation for Monocular Visual Odometry
Goethe Univ, Germany.
Goethe Univ, Germany.
Goethe Univ, Germany.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe Univ, Germany.
2017 (English)In: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017), IEEE , 2017, p. 1714-1721Conference paper, Published paper (Refereed)
Abstract [en]

Monocular visual odometry / SLAM requires the ability to deal with the scale ambiguity problem, or equivalently to transform the estimated unscaled poses into correctly scaled poses. While propagating the scale from frame to frame is possible, it is very prone to the scale drift effect. We address the problem of monocular scale estimation by proposing a multimodal mechanism of prediction, classification, and correction. Our scale correction scheme combines cues from both dense and sparse ground plane estimation; this makes the proposed method robust towards varying availability and distribution of trackable ground structure. Instead of optimizing the parameters of the ground plane related homography, we parametrize and optimize the underlying motion parameters directly. Furthermore, we employ classifiers to detect scale outliers based on various features (e.g. moments on residuals). We test our method on the challenging KITTI dataset and show that the proposed method is capable to provide scale estimates that are on par with current state-of-the-art monocular methods without using bundle adjustment or RANSAC.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1714-1721
Series
IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-145828DOI: 10.1109/IVS.2017.7995955ISI: 000425212700266ISBN: 978-1-5090-4804-5 OAI: oai:DiVA.org:liu-145828DiVA, id: diva2:1192092
Conference
28th IEEE Intelligent Vehicles Symposium (IV)
Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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