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Robust 2D/3D Calibration Using RANSAC Registration

  • Billy Ray Fortenbury
  • Gutemberg Guerra-Filho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7431)

Abstract

An area of increasing interest in computer vision is the fusion of 2D images with depth maps from 3D sensing devices to obtain more robust 3D information about the scene. Before this can be achieved, one must have an accurate method for the calibration of the 3D sensing devices and the pinhole cameras. In this paper, we introduce a robust method for registering depth maps from 3D sensing devices into point clouds reconstructed from 2D images. Our new calibration method explores RANSAC registration to take into account the high-noise nature of current 3D sensing technologies. We solve this by using a novel application of the RANSAC algorithm to robustly register two point clouds obtained from the 3D sensing device and the pinhole camera. The reprojection error after registration using our algorithm is less than 0.3%.

Keywords

Point Cloud Stereo Pair Stereo Camera Rigid Transformation Pinhole Camera 
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

  1. 1.
    Arunm, K.S., et al.: Least-Squares Fitting of Two 3-D Point Sets. IEEE Transaction on Pattern Analysis and Machine Intelligence 5, 698–700 (1987)CrossRefGoogle Scholar
  2. 2.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transaction on Pattern Analysis and Machine Intelligence 14, 239–254 (2001)CrossRefGoogle Scholar
  3. 3.
    Bouguet, J.Y.: Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/index.html
  4. 4.
    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, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Fontanelli, D., et al.: A Fast RANSAC Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements. In: IEEE Conference on Automation Science and Engineering (2007)Google Scholar
  6. 6.
    Fukai, H., Xu, G.: Fast and Robust Registration of Multiple 3D Point Clouds. In: 20th IEEE International Symposium on Robot and Human Interactive Communication (2011)Google Scholar
  7. 7.
    Pollard, S.B., et al.: PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit. Perception 14, 449–470 (1985)CrossRefGoogle Scholar
  8. 8.
    Szeliski, R.: Bayesian modeling of uncertainty in low-level vision. Journal of Computer Vision 5, 271–301 (1990)CrossRefGoogle Scholar
  9. 9.
    Torr, P.H.S., Murray, D.W.: The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix. International Journal of Computer Vision 24, 271–300 (1997)CrossRefGoogle Scholar
  10. 10.
    Zhang, Z.: Iterative Point Matching for Registration of Free-form Curves (1992)Google Scholar
  11. 11.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Billy Ray Fortenbury
    • 1
  • Gutemberg Guerra-Filho
    • 1
  1. 1.The University of Texas at ArlingtonUSA

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