A registration method for rigid objects without point matching

  • Yasuyo Kita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


A method for the registration of rigid objects by attracting objects to the desired posture by appropriate forces is described. Inputs are a group of three-dimensional coordinates of the points representing an object in the initial state and in the goal state. No information on point correspondence between the initial and the goal states is given. Firstly, the object is translated from the initial position so that its centroid coincides with one of the goal position. The difference in posture of the object is corrected by rotating round the centroid by the torque which attracts it to the goal posture. We demonstrate that repulsive forces to each point of the object from all points at the goal posture, whose magnitude is the square of the distance between the points, satisfactorily produces such torque.


Repulsive Force Stable Posture Rotation Matrix Goal State Point Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    O. D. Faugeras and M. Hebert: “The representation, recognition, and locating of 3D objects”, Int. J. Robotics Research, Vol. 5, No. 3, pp. 27–52, 1986.Google Scholar
  2. 2.
    S. Umeyama, T. Kazvand and M. Hospital: “Recognition and positioning of three-dimensional objects by combining matchings of primitive local patterns”, Computer Vision, Graphics & Image Processing, Vol. 44, No. 1, pp. 58–76, 1988.Google Scholar
  3. 3.
    L. G. Brown: “A survey of image registration techniques”, ACM Computing Surveys, Vol 24, No. 4, pp. 325–376, 1992.Google Scholar
  4. 4.
    J. M. Galves and M. Canton: “Normalization and shape recognition of three-dimensional objects by 3D moments”, Pattern Recognition, Vol. 26, No. 5, pp. 667–681, 1993.Google Scholar
  5. 5.
    P. J. Besl and N. D. Mckay: “A method for registration of 3D shapes”, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-14, No.2, pp. 239–256, 1992.Google Scholar
  6. 6.
    L. Bruine, S. Lavallée, and R. Szeliski: “Using force fields derived from 3D distance maps for inferring the attitude of a 3D rigid object”, In Proc. of European Conference on Computer Vision '92, pp.670–675, 1992.Google Scholar
  7. 7.
    T. Masuda and N. Yokoya: “Robust estimation of rigid motion parameters between a pair of range images”, In Proc. of The 8th Scandinavian Conference on Image Analysis, pp. 499–506, 1993.Google Scholar
  8. 8.
    T. Yoshimi and M. Oshima: “Multi light sources range finder system”, In Proc. of IAPR workshop on CV, pp. 245–248, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Yasuyo Kita
    • 1
  1. 1.Electrotechnical LaboratoryIbarakiJapan

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