3D Point Sets Matching Method Based on Moravec Vertical Interest Operator

  • Linying Jiang
  • Jingming Liu
  • Dancheng Li
  • Zhiliang Zhu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)


The purpose of this paper is to solve the problem of matching two 3D point sets quickly in the field of robot vision. Moravec vertical interest operator is used to extract vertical edge feature of objects. The method of the sum squared difference (SSD) is used to match the feature points and obtain the 3D point sets which contain vertical line feature. Find the transformation relation of rotation and translation of corresponding straight lines in two 3D point sets according to the projection point of vertical line projected into x-y plane. Depending on the transformation relation, it can match two 3D point sets. The experiment results illustrate that this method has good exactness and robustness.


Feature Point Iterative Close Point Iterative Close Point Transformation Relation Vertical Feature 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yamauchi, B., Growley, J.: A comparison of position estimation techniques using occupancy grids. Journal of Robotics and Autonomous Systems 12, 163–171 (1994)CrossRefGoogle Scholar
  2. 2.
    Moravec, H.P., Elfes, A.: High Resolution Maps from Wide Angle Sonar. In: IEEE Iternational Conference on Roboics and Automation, pp. 116–121 (March 1985)Google Scholar
  3. 3.
    Haehnel, D., Schultz, D., Burgard, W.: Map Building with Mobile Robots in Populated Environments. In: Proceedings of the International Conference on Intelligent Robots and Systems(IROS) (2002)Google Scholar
  4. 4.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transaction Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  5. 5.
    Chetverikov, D., Svirko, D., Stepanov, D., Krsek, P.: The Trimmed Iterative Closest Point Algorithm. In: Proceedings of International Conference on Pattern Recognition, Quebec City, Canada, pp. 545–548 (August 2002)Google Scholar
  6. 6.
    Qing, R.: The Research of Three-Dimensional Reconstruction Based On Multi-Depth Images. Zhejiang University Master’s Degree thesis (2006)Google Scholar
  7. 7.
    Se, S., Lowe, D.G., Little, J.J.: Vision-Based Global Localization and Mapping for Mobile Robots. IEEE Transactions on Robotics 21(3) (June 2005)Google Scholar
  8. 8.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1/2/3), 7–42 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Shupeng, W., Lili, F.: The Analysis of Using Moravec Operator to Extract Feature Points. Computer Knowledge and Technology (26), 125–126 (2006)Google Scholar
  10. 10.
    Bradski, G., Kaebler, A.: Learning OpenCV, pp. 433–436. O’Reilly Media, Inc., Sebastopol (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Linying Jiang
    • 1
  • Jingming Liu
    • 1
  • Dancheng Li
    • 1
  • Zhiliang Zhu
    • 1
  1. 1.Software CollegeNortheastern UniversityShenyangChina

Personalised recommendations