Multiple Plane Detection Method from Range Data of Digital Imaging System for Moving Robot Applications

  • Jeong-Hyun Kim
  • Zhu Teng
  • Dong-Joong KangEmail author
  • Jong-Eun Ha
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 4)


Imaging system using CCD sensors for automatic navigation of intelligent robot is a central element to recognize and interact with the surrounding environment. Specifically, finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this chapter, a noise rejection filter is introduced for defining planar surfaces to reduce the voting of noisy data. We test the normal directions of two arbitrary planes in a small region, which are determined by three vertexes of a triangle and its rotation. If the angle of two normal directions is lower than a given threshold, it is voted into the Hough parameter space. This method is similar to a noise rejection filter to verify the planarity of local planes. We can get accurate parameters of the plane in RHT because most noises and nonplanar data cannot vote into the Hough parameter space. We use a scan window to vote locally. The scan window explores all regions by changing the window size. The window operation improves the accuracy of plane detection because the plane is locally consistent and increases the search speed for finding planes. Finally, the performance of the algorithm for real range data obtained from a stereo imaging system has been verified.


Digital imaging system 3D range data Plane detection Randomized Hough transform Mobile robot 

List of Abbreviations


Combinatorial Hough transform


Dynamic generalized Hough transform


Hough transform


Iteractive randomized Hough transform


Korea Institute for Advancement of Technology


Look up table


Ministry of Education, Science Technology


National Research Foundation of Korea


Plane detection cell


Randomized Hough transform



This work was partly supported by the Ministry of Education, Science Technology (MEST) and Korea Institute for Advancement of Technology (KIAT) through the Human Resource Training Project for Regional Innovation, and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0027990) and the IT R&D program of MSIP/KEIT [Industry convergence original technology development projects, Development of context awareness monitoring and search system based on high definition multi-video].


  1. 1.
    Okada, K., Kagami, S., Inaba, J., Inoue, H.: Plane segment finder: algorithm, implementation and applications. In: IEEE International Conference Robotics Automation, pp. 2051–2058 (2001)Google Scholar
  2. 2.
    Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vision Graphics Image Process. 44 (1), 87–116 (1988)Google Scholar
  3. 3.
    Ben-Tzvi, D., Sandler, M.B.: A combinatorial Hough transform. Pattern Recogn. Lett. 11, 167–174 (1990)CrossRefzbMATHGoogle Scholar
  4. 4.
    Xu, L., Oja, E.: Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities. CVGIP: Image Underst. 57, 131–154 (1993)CrossRefGoogle Scholar
  5. 5.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method: randomized Hough transform (RHT). Pattern Recogn. Lett. 11, 331–338 (1990)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kiryati, N., Eldar, Y., Bruckstein, A.M.: Probabilistic Hough transform. Pattern Recogn. Lett. 24, 303–316 (1991)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Leavers, V.F.: 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 Underst. 56, 381–398 (1992)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kang, D.J., Lim, S.J., Ha, J.E., Jeong, M.H.: A detection cell using multiple points of a rotating triangle to find local planar regions from stereo depth data. Pattern Recog. Lett. 30, 486–493 (2009)CrossRefGoogle Scholar
  9. 9.
    Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Comparisons of probabilistic and non-probabilistic Hough transforms. In: Proceedings 3rd European Conference on Computer Vision, pp. 351–360 (1990)Google Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2001)Google Scholar
  11. 11.
    Lu, W., Tan, J.: Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT). Pattern Recogn. 41, 1268–1279 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Point Grey company page.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jeong-Hyun Kim
    • 1
  • Zhu Teng
    • 2
  • Dong-Joong Kang
    • 3
    Email author
  • Jong-Eun Ha
    • 4
  1. 1.Realhub research instituteRealhub corporation limitedBusanKorea
  2. 2.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  3. 3.School of Mechanical EngineeringPusan National UniversityBusanKorea
  4. 4.Deptartment of Automotive EngineeringSeoul National University of Science and TechnologySeoulKorea

Personalised recommendations