• DocumentCode
    253639
  • Title

    Relative Pose Estimation for a Multi-camera System with Known Vertical Direction

  • Author

    Gim Hee Lee ; Pollefeys, Marc ; Fraundorfer, Friedrich

  • Author_Institution
    Dept. of Comput. Sci., ETH Zurich, Zurich, Switzerland
  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    540
  • Lastpage
    547
  • Abstract
    In this paper, we present our minimal 4-point and linear 8-point algorithms to estimate the relative pose of a multi-camera system with known vertical directions, i.e. known absolute roll and pitch angles. We solve the minimal 4-point algorithm with the hidden variable resultant method and show that it leads to an 8-degree univariate polynomial that gives up to 8 real solutions. We identify a degenerated case from the linear 8-point algorithm when it is solved with the standard Singular Value Decomposition (SVD) method and adopt a simple alternative solution which is easy to implement. We show that our proposed algorithms can be efficiently used within RANSAC for robust estimation. We evaluate the accuracy of our proposed algorithms by comparisons with various existing algorithms for the multi-camera system on simulations and show the feasibility of our proposed algorithms with results from multiple real-world datasets.
  • Keywords
    cameras; estimation theory; polynomials; pose estimation; singular value decomposition; 8-degree univariate polynomial; RANSAC; SVD method; degenerated case; hidden variable resultant method; known absolute roll; linear 8-point algorithm; minimal 4-point algorithm; multicamera system; pitch angles; relative pose estimation; robust estimation; standard singular value decomposition method; vertical direction; Cameras; Estimation; Mathematical model; Polynomials; Robustness; Standards; Generalized Camera; Known Vertical Direction; Minimal Problem; Multi-Camera System; Relative Pose Estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPR.2014.76
  • Filename
    6909470