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

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 144))

Abstract

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.

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References

  1. Yamauchi, B., Growley, J.: A comparison of position estimation techniques using occupancy grids. Journal of Robotics and Autonomous Systems 12, 163–171 (1994)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Qing, R.: The Research of Three-Dimensional Reconstruction Based On Multi-Depth Images. Zhejiang University Master’s Degree thesis (2006)

    Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  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. Bradski, G., Kaebler, A.: Learning OpenCV, pp. 433–436. O’Reilly Media, Inc., Sebastopol (2008)

    Google Scholar 

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Correspondence to Linying Jiang .

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Jiang, L., Liu, J., Li, D., Zhu, Z. (2012). 3D Point Sets Matching Method Based on Moravec Vertical Interest Operator. In: Gaol, F., Nguyen, Q. (eds) Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Advances in Intelligent and Soft Computing, vol 144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28314-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-28314-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28313-0

  • Online ISBN: 978-3-642-28314-7

  • eBook Packages: EngineeringEngineering (R0)

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