Calibration of a wide angle stereoscopic system

Abstract

This paper was published in OPTICS LETTERS and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/OL.36.003064. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.Inaccuracies in the calibration of a stereoscopic system appear with errors in point correspondences between both images and inexact points localization in each image. Errors increase if the stereoscopic system is composed of wide angle lens cameras. We propose a technique where detected points in both images are corrected before estimating the fundamental matrix and the lens distortion models. Since points are corrected first, errors in point correspondences and point localization are avoided. To correct point location in both images, geometrical and epipolar constraints are imposed in a nonlinear minimization problem. Geometrical constraints define the point localization in relation to its neighbors in the same image, and eipolar constraints represent the location of one point referred to its corresponding point in the other image. © 2011 Optical Society of America.Ricolfe Viala, C.; Sánchez Salmerón, AJ.; Martínez Berti, E. (2011). Calibration of a wide angle stereoscopic system. Optics Letters. 36(16):3064-3067. doi:10.1364/OL.36.003064306430673616Zhang, Z., Ma, H., Guo, T., Zhang, S., & Chen, J. (2011). Simple, flexible calibration of phase calculation-based three-dimensional imaging system. Optics Letters, 36(7), 1257. doi:10.1364/ol.36.001257Longuet-Higgins, H. C. (1981). A computer algorithm for reconstructing a scene from two projections. Nature, 293(5828), 133-135. doi:10.1038/293133a0Ricolfe-Viala, C., & Sanchez-Salmeron, A.-J. (2010). Lens distortion models evaluation. Applied Optics, 49(30), 5914. doi:10.1364/ao.49.005914Armangué, X., & Salvi, J. (2003). Overall view regarding fundamental matrix estimation. Image and Vision Computing, 21(2), 205-220. doi:10.1016/s0262-8856(02)00154-3Devernay, F., & Faugeras, O. (2001). Straight lines have to be straight. Machine Vision and Applications, 13(1), 14-24. doi:10.1007/pl0001326