FPGA Implementation of the Robust Essential Matrix Estimation with RANSAC and the 8-Point and the 5-Point Method

  • Michał Fularz
  • Marek Kraft
  • Adam Schmidt
  • Andrzej Kasiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7174)


This paper presents a FPGA-based multiprocessor system for the essential matrix estimation from a set of point correspondences containing outliers. The estimation is performed using two methods: the 8-point and the 5-point algorithm, and complemented with robust estimation. The description of the architecture and the hardware-specific design considerations are given. Performance and resource use depending on the chosen method and the number of processing cores are also given.


FPGA robust estimation essential matrix multicore 


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  1. 1.
    Batra, D., Nabbe, B., Hebert, M.: An alternative formulation for five point relative pose problem. In: Proceedings of the IEEE Workshop on Motion and Video Computing, pp. 21–26. IEEE Computer Society Press, Washington, DC, USA (2007)Google Scholar
  2. 2.
    Cyganek, B., Siebert, J.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley (2009)Google Scholar
  3. 3.
    Draper, B.A., Beveridge, J.R., Bohm, A.P.W., Ross, C., Chawathe, M.: Accelerated image processing on FPGAs. IEEE Transactions on Image Processing 12(12), 1543–1551 (2003)CrossRefGoogle Scholar
  4. 4.
    Farrugia, N., Mamalet, F., Roux, S., Yang, F., Paindavoine, M.: Fast and robust face detection on a parallel optimized architecture implemented on FPGA. IEEE Transactions on Circuits and Systems for Video Technology 19(4), 597–602 (2009)CrossRefGoogle Scholar
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518Google Scholar
  7. 7.
    Jin, S., Cho, J., Pham, X.D., Lee, K.M., Park, S.K., Kim, M., Jeon, J.W.: FPGA design and implementation of a real-time stereo vision system. IEEE Transactions on Circuits and Systems for Video Technology 20(1), 15–26 (2010)CrossRefGoogle Scholar
  8. 8.
    Kukelova, Z., Bujnak, M., Pajdla, T.: Polynomial eigenvalue solutions to the 5-pt and 6-pt relative pose problems. Proceedings of the British Machine Vision Conference (2008)Google Scholar
  9. 9.
    Li, H., Hartley, R.: Five-point motion estimation made easy. In: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1, pp. 630–633. IEEE Computer Society Press, Washington, DC, USA (2006)Google Scholar
  10. 10.
    Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Transactions Pattern Analysis Machine Intelligence 26, 756–777 (2004)CrossRefGoogle Scholar
  11. 11.
    Stewénius, H., Engels, C., Nistér, D.: Recent developments on direct relative orientation. ISPRS Journal of Photogrammetry and Remote Sensing 60, 284–294 (2006)CrossRefGoogle Scholar
  12. 12.
    Xilinx: Fast Simplex Link (FSL) Bus (v2.11c) Data Sheet (April 2010)Google Scholar
  13. 13.
    Xilinx Inc.: UG081 MicroBlaze Processor Reference Guide – Embedded Development Kit EDK 12.4, v11.4 edn. (November 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michał Fularz
    • 1
  • Marek Kraft
    • 1
  • Adam Schmidt
    • 1
  • Andrzej Kasiński
    • 1
  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

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