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%.
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References
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)
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)
Bouguet, J.Y.: Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/index.html
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)
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)
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)
Pollard, S.B., et al.: PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit. Perception 14, 449–470 (1985)
Szeliski, R.: Bayesian modeling of uncertainty in low-level vision. Journal of Computer Vision 5, 271–301 (1990)
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)
Zhang, Z.: Iterative Point Matching for Registration of Free-form Curves (1992)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
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© 2012 Springer-Verlag Berlin Heidelberg
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Fortenbury, B.R., Guerra-Filho, G. (2012). Robust 2D/3D Calibration Using RANSAC Registration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_18
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DOI: https://doi.org/10.1007/978-3-642-33179-4_18
Publisher Name: Springer, Berlin, Heidelberg
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