• DocumentCode
    1389922
  • Title

    Two Efficient Solutions for Visual Odometry Using Directional Correspondence

  • Author

    Naroditsky, Oleg ; Zhou, Xun S. ; Gallier, Jean ; Roumeliotis, Stergios I. ; Daniilidis, Kostas

  • Author_Institution
    Dept. of Comput. & Inf. Sci., Univ. of Pennsylvania, Philadelphia, PA, USA
  • Volume
    34
  • Issue
    4
  • fYear
    2012
  • fDate
    4/1/2012 12:00:00 AM
  • Firstpage
    818
  • Lastpage
    824
  • Abstract
    This paper presents two new, efficient solutions to the two-view, relative pose problem from three image point correspondences and one common reference direction. This three-plus-one problem can be used either as a substitute for the classic five-point algorithm, using a vanishing point for the reference direction, or to make use of an inertial measurement unit commonly available on robots and mobile devices where the gravity vector becomes the reference direction. We provide a simple, closed-form solution and a solution based on algebraic geometry which offers numerical advantages. In addition, we introduce a new method for computing visual odometry with RANSAC and four point correspondences per hypothesis. In a set of real experiments, we demonstrate the power of our approach by comparing it to the five-point method in a hypothesize-and-test visual odometry setting.
  • Keywords
    computer vision; distance measurement; geometry; pose estimation; vectors; RANSAC; algebraic geometry; directional correspondence; five-point algorithm; five-point method; four point correspondences; gravity vector; image point correspondences; inertial measurement unit; mobile devices; reference direction; relative pose problem; robots; three-plus-one problem; vanishing point; visual odometry; Cameras; Noise; Polynomials; Sparse matrices; Vectors; Visualization; Computer vision; Groebner basis.; minimal problems; structure from motion; visual odometry; Algorithms; Equipment and Supplies; Imaging, Three-Dimensional; Robotics; Visual Perception;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.2011.226
  • Filename
    6095561