A Novel Approach in Computer Vision and Photogrammetry to Recover the Relative Position and Orientation of Cameras in Stereo Images Using the SVD Decomposition of the Essential Matrix

Document Type : Persian Original Article

Authors

1 Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran

2 Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran.

3 School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

4 School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The relative position and orientation between two cameras in a stereo pair are included within the Essential matrix, E. The decomposition of this matrix into a rotation matrix, R, and a skew-symmetric matrix, S, is an efficient tool for retrieving the relative position and orientation of the cameras. In this paper, a new method is proposed to recover the relative position and orientation of the cameras in a stereo pair using the singular value decomposition (SVD) of the Essential matrix. First, the existing formulas in the decomposition of the Essential matrix into a rotation matrix and a skew-symmetric matrix using the SVD decomposition are directly proved using the SVD properties. Then, based on these results, a new method in the decomposition of the Essential matrix using SVD will be presented. The Essential matrix decomposition in this method is accomplished by extracting the base vector of the left null space of the Essential matrix and then by SVD decomposition of the skew-symmetric matrix corresponding to this base vector. In this method, the initial mapping of the Essential matrix, recovered from the erroneous coordinates of the corresponding image points in two images, into the space of Essential matrices does not require. This mapping is performed by determining the skew-symmetric matrix, S. The proposed numerical analysis shows that the results of the new presented method are correct and identical with the results of the existing formulas.

Keywords


[1]        T. Luhmann, S. Robson, S. Kyle, and J. Boehm, Close-range photogrammetry and 3D imaging. Walter de Gruyter, 2013.
[2]           J. C. McGlone, E. M. Mikhail, J. S. Bethel, and R. Mullen, "Manual of photogrammetry," 2004: American society for photogrammetry and remote sensing Bethesda, MD.
[3]           K. Kraus, Photogrammetry: geometry from images and laser scans. Walter de Gruyter, 2011.
[4]           J. O. Ogundare, Understanding Least Squares Estimation and Geomatics Data Analysis. John Wiley & Sons, 2018.
[5]           R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge university press, 2003.
[6]           Y. Ma, S. Soatto, J. Kosecka, and S. S. Sastry, An invitation to 3-d vision: from images to geometric models. Springer Science & Business Media, 2012.
[7]           H. C. J. N. Longuet-Higgins, "A computer algorithm for reconstructing a scene from two projections," vol. 293, no. 5828, p. 133, 1981.
[8]           G. H. Georgiev and V. D. J. A. M. S. Radulov, "A practical method for decomposition of the essential matrix," vol. 8, no. 176, pp. 8755-8770, 2014.
[9]           M. J. T. P. R. Gerke, "Photogrammetric Computer Vision–Statistics, Geometry, Orientation and Reconstruction. By Wolfgang Förstner and Bernhard P. Wrobel. Springer International Publishing, 2016. ISBN 978‐3‐319‐11549‐8. 816 pages with 59 colour figures. Price:£ 44· 99 hardback;£ 35· 99 eBook," vol. 32, no. 158, pp. 182-183, 2017.
[10]         B. K. J. J. o. t. O. S. o. A. Horn, "Recovering baseline and orientation from essential matrix," vol. 1000, pp. 1-10, 1990.
[11]         W. Wei and T. Hung Tat, "A SVD decomposition of essential matrix with eight solutions for the relative positions of two perspective cameras," in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, vol. 1, pp. 362-365 vol.1.
[12]         G. Strang, "Introduction to Linear Algebra . Wellesley Cambridge, 2016," ISBN 978-09802327-7-62016.
[13]         R. Bro, E. Acar, and T. G. J. J. o. C. A. J. o. t. C. S. Kolda, "Resolving the sign ambiguity in the singular value decomposition," vol. 22, no. 2, pp. 135-140, 2008.
[14]         D. J. I. t. o. p. a. Nistér and m. intelligence, "An efficient solution to the five-point relative pose problem," vol. 26, no. 6, pp. 756-770, 2004.
[15]         K. Kanatani, Geometric Computation for Machine Vision. Clarendon Press, 1993.
[16]         C. B. J. P. E. Duane, "Close-range camera calibration," vol. 37, no. 8, pp. 855-866, 1971.