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Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment?
Goethe University, Germany.
Goethe University, Germany.
Goethe University, Germany.
Goethe University, Germany.
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2017 (English)In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 68Article in journal (Refereed) Published
Abstract [en]

Visual odometry using only a monocular camera faces more algorithmic challenges than stereo odometry. We present a robust monocular visual odometry framework for automotive applications. An extended propagation-based tracking framework is proposed which yields highly accurate (unscaled) pose estimates. Scale is supplied by ground plane pose estimation employing street pixel labeling using a convolutional neural network (CNN). The proposed framework has been extensively tested on the KITTI dataset and achieves a higher rank than current published state-of-the-art monocular methods in the KITTI odometry benchmark. Unlike other VO/SLAM methods, this result is achieved without loop closing mechanism, without RANSAC and also without multiframe bundle adjustment. Thus, we challenge the common belief that robust systems can only be built using iterative robustification tools like RANSAC. (C) 2017 Published by Elsevier B.V.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 68
Keywords [en]
Visual odometry; Monocular visual odometry; SLAM; Pose prediction; Joint epipolar tracking; Ground plane estimation; Driver assistance
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-144457DOI: 10.1016/j.imavis.2017.08.002ISI: 000419418900002OAI: oai:DiVA.org:liu-144457DiVA, id: diva2:1176566
Available from: 2018-01-22 Created: 2018-01-22 Last updated: 2018-01-22

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Mester, Rudolf
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  • apa
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  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf