Advertisement

Memory Efficient Vision Based Line Feature Extraction for Tiny Mobile Robots

  • Abdul Bais
  • Muhammad U. K. Khan
  • Khawaja M. Yahya
  • Robert Sablatnig
  • Ghulam M. Hassan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5627)

Abstract

This paper presents implementation of a memory efficient all integer line feature extraction algorithm for tiny autonomous mobile robot with limited on-chip memory. A circular buffer is used to bring image data from off chip to the on chip memory of the DSP for detecting edges. Afterwards a gradient based Hough transform is used to group collinear pixels which are processed to detect end points and length of the line segments. Approximation of the two dimensional Hough parameter space using a one dimensional array is discussed. Experimental results illustrate the performance of these features extraction on real and synthetic images.

Keywords

Line Segment Mobile Robot Peak Detection Digital Signal Processor Synthetic Image 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fox, D., Burgard, W., Thrun, S., Cremers, A.: Position estimation for mobile robots in dynamic environments. In: Proceedings of the National Conference on Artificial Intelligence, pp. 983–988 (1998)Google Scholar
  2. 2.
    Cox, I.J.: Blanche — an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation 7(2), 193–204 (1991)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Leonard, J., Durrant-Whyte, H.: Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7(3), 376–382 (1991)CrossRefGoogle Scholar
  4. 4.
    Borenstein, J.: Experimental results from internal odometry error correction with the omnimate mobile robot. IEEE Transactions on Robotics and Automation 14(6), 963–969 (1998)CrossRefGoogle Scholar
  5. 5.
    Dao, N., You, B.J., Oh, S.R., Choi, Y.: Simple visual self-localization for indoor mobile robots using single video camera. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2004, vol. 4, pp. 3767–3772 (2004)Google Scholar
  6. 6.
    Bais, A., Sablatnig, R., Novak, G.: Line-based landmark recognition for self-localization of soccer robots. In: Proceedings of the IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, September 2005, pp. 132–137 (2005)Google Scholar
  7. 7.
    Guru, D.S., Shekar, B.H., Nagabhushan, P.: A simple and robust line detection algorithm based on small eigenvalue analysis. Pattern Recognition Letters 25(1), 1–13 (2004)CrossRefGoogle Scholar
  8. 8.
    Climer, S., Bhatia, S.K.: Local lines: A linear time line detector. Pattern Recognition Letters 24, 2291–2300 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kälviäinen, H., Hirvonen, P.: An extension to the randomized hough transform exploiting connectivity. Pattern Recognition Letters 18(1), 77–85 (1997)CrossRefGoogle Scholar
  10. 10.
    Kiryati, N., Eldar, Y., Bruckstein, A.: A probabilistic hough transform. Pattern Recognition 24(4), 303–316 (1991)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Leavers, V.F.: Survey - which hough transform? Computer Vision, Graphics, and Image Processing 58, 250–264 (1993)CrossRefGoogle Scholar
  12. 12.
    Kälviäinen, H., Hirvonen, P., Xu, L., Oja, E.: Probabilistic and non-probabilistic hough transforms: Overview and comparisons. Image and Vision Computing 13(4), 239–252 (1995)CrossRefGoogle Scholar
  13. 13.
    Gan, W.S., Kuo, S.M.: Embedded Signal Processing with the Micro Signal Architecture. John Wiley and Sons, Chichester (2007)CrossRefGoogle Scholar
  14. 14.
    Bader, M.: Feature-based real-time stereo vision on a dual core dsp with an object detection algorithm. Master’s thesis, Pattern Recongnition and Impage Processing Group, Institute of Computer Aided Automation, Vienna University of Technology, Vienna, Austria (March 2007)Google Scholar
  15. 15.
    Duda, R., Hart, P.: Use of the Hough transformation to detect lines and curves in the pictures. Communications of the ACM 15(1), 11–15 (1972)CrossRefzbMATHGoogle Scholar
  16. 16.
    Singleton, R.C.: A method for computing the fast fourier transform with auxiliary memory and limited high-speed storage. IEEE Transactions on Audio and Electroacoustics (15), 91–98 (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Abdul Bais
    • 1
  • Muhammad U. K. Khan
    • 2
  • Khawaja M. Yahya
    • 2
  • Robert Sablatnig
    • 3
  • Ghulam M. Hassan
    • 2
  1. 1.Sarhad University of Science and Information TechnologyPeshawarPakistan
  2. 2.NWFP University of Engineering and TechnologyPeshawarPakistan
  3. 3.Vienna University of TechnologyAustria

Personalised recommendations