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.
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References
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)
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)
Leonard, J., Durrant-Whyte, H.: Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7(3), 376–382 (1991)
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)
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)
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)
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)
Climer, S., Bhatia, S.K.: Local lines: A linear time line detector. Pattern Recognition Letters 24, 2291–2300 (2003)
Kälviäinen, H., Hirvonen, P.: An extension to the randomized hough transform exploiting connectivity. Pattern Recognition Letters 18(1), 77–85 (1997)
Kiryati, N., Eldar, Y., Bruckstein, A.: A probabilistic hough transform. Pattern Recognition 24(4), 303–316 (1991)
Leavers, V.F.: Survey - which hough transform? Computer Vision, Graphics, and Image Processing 58, 250–264 (1993)
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)
Gan, W.S., Kuo, S.M.: Embedded Signal Processing with the Micro Signal Architecture. John Wiley and Sons, Chichester (2007)
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)
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)
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)
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Bais, A., Khan, M.U.K., Yahya, K.M., Sablatnig, R., Hassan, G.M. (2009). Memory Efficient Vision Based Line Feature Extraction for Tiny Mobile Robots. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_29
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DOI: https://doi.org/10.1007/978-3-642-02611-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02610-2
Online ISBN: 978-3-642-02611-9
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