• DocumentCode
    3179049
  • Title

    Experiments with robust estimation techniques in real-time robot vision

  • Author

    Malis, Ezio ; Marchand, Eric

  • Author_Institution
    INRIA, Sophia-Antipolis
  • fYear
    2006
  • fDate
    Oct. 2006
  • Firstpage
    223
  • Lastpage
    228
  • Abstract
    The goal of this paper is to present an overview of robust estimation techniques with a special focus on robotic vision applications. In this particular context, constraints due computation time have to be considered in the choice of the estimation algorithm. Among the numerous techniques proposed in the literature to obtained robust estimation we have, not being exhaustive, Hough transform, RANSAC (Random Sample Consensus), the LMedS (Least Median of Squares), the M-estimators, etc. In this overview, we describe these various approaches in the light of a simple example. Finally, we illustrate the use of robust estimation techniques by various examples in real-time robot vision
  • Keywords
    Hough transforms; estimation theory; least mean squares methods; robot vision; Hough transform; least median of squares; random sample consensus; real-time robot vision; robust estimation; Cameras; Convergence; Data mining; Electric breakdown; Intelligent robots; Parameter estimation; Robot vision systems; Robustness; Solid modeling; Time factors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    1-4244-0259-X
  • Electronic_ISBN
    1-4244-0259-X
  • Type

    conf

  • DOI
    10.1109/IROS.2006.282572
  • Filename
    4058722