Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Multiple Plane Area Detection Using Self Organizing Map

자기 조직화 지도를 이용한 다중 평면영역 검출

  • 김정현 (부산대학교 기계공학부) ;
  • 등죽 (부산대학교 기계공학부) ;
  • 강동중 (부산대학교 기계공학부)
  • Received : 2010.04.12
  • Accepted : 2010.11.18
  • Published : 2011.01.01
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Abstract

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

Keywords

References

  1. K. Okada, S. Kagami, J. Inaba, and H. Inoue, “Plane segment finder: algorithm, implementation and applications,” IEEE Int. Conf. on Robotics and Automation, vol. 2, pp. 2120-2125, 2001. https://doi.org/10.1109/ROBOT.2001.932920
  2. D. Ben-Tzvi and M. B. Sandler, A combinatorial hough transform, Pattern Recogn. Lett. 11, 167-174, 1990. https://doi.org/10.1016/0167-8655(90)90002-J
  3. L. Xu and E. Oja, “Randomized Hough Transform (RHT): basic mechanisms, algorithms, and computational complexities, CVGIP: Image Understanding 57, pp. 131-154, 1993. https://doi.org/10.1006/ciun.1993.1009
  4. L. Xu, E. Oja, and P. Kultanen, “A new curve detection method: Randomized Hough Transform (RHT),” Pattern Recogn. Lett. 11, pp. 331-338, 1990. https://doi.org/10.1016/0167-8655(90)90042-Z
  5. N. Kiryati, Y. Eldar, and A. M. Bruckstein, “Probabilistic hough transform,” Pattern Recogn. Lett. 24, pp. 303-316, 1991. https://doi.org/10.1016/0031-3203(91)90073-E
  6. V. F. Leavers, “The dynamic generalized hough transform: its relationship to the probabilistic hough transforms and an application to the concurrent detection of circles and ellipses,” CVGIP: Image Understanding 56, pp. 381-398, 1992. https://doi.org/10.1016/1049-9660(92)90049-9
  7. H. Kalviainen, P. Hirvonen, L. Xu, and E. Oja, “Comparisons of probabilistic and non-probabilistic hough transforms,” In Proceedings 3rd European Conference on Computer Vision, pp. 351-360, 1994.
  8. W. Lu and J. Tan, “Detection of incomplete ellipse in images with strong noise by Iterative Randomized Hough Transform (IRHT),” Pattern Recognition 41, pp. 1268-1279, 2008. https://doi.org/10.1016/j.patcog.2007.09.006
  9. T. Kohonen, “The self-organizing map,” Proc. of the IEEE, vol. 78, no. 3, pp. 226-239, Mar. 1998.
  10. D. J. Kang, S. J. Lim, J. E. Ha, and M. H. Jeong, “A detection cell using multiple points of a rotating triangle to find local planar regions form stereo depth data,” Pattern Recog. Lett. 30, pp. 486-493, 2009. https://doi.org/10.1016/j.patrec.2008.11.011
  11. PointGray company, http://www.point-gray.com.