Abstract
A method of determining the motion of a camera from its image velocities is described that is insensitive to noise and intrinsic camera parameters. This algorithm is based on a novel extension of motion parallax which does not require the instantaneous alignment of features, but uses sparse visual motion estimates to extract the direction of translation of the camera directly, after which determination of the camera rotation and the depths of the image features follows easily. A method for calculating the expected uncertainty in the estimates is also described which allows optimal estimation and can also detect and reject independent motion and false correspondences. Experiments using small perturbation analysis show a favourable comparison with existing methods, and specifically the Fundamental Matrix method.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
R. Cipolla, Y. Okamoto, and Y. Kuno. Robust structure from motion using motion parallax. In Proc. 4th Int. Conf. on Computer Vision, pages 374–382, 1993.
O.D. Faugeras. What can be seen in three dimensions with an uncalibrated stereo rig? In G. Sandini, editor, Proc. 2nd European Conference on Computer Vision, pages 563–578. Springer-Verlag, 1992.
O.D. Faugeras, Q.-T. Luong, and S.J. Maybank. Camera self-calibration: Theory and experiments. In G. Sandini, editor, Proc. 2nd European Conference on Computer Vision, pages 321–334. Springer-Verlag, 1992.
D.J. Heeger and A.D. Jepson. Subspace methods for recovering rigid motion I: Algorithm and implementation. Int. J. of Comp. Vision, 7(2):95–117, 1992.
E.C. Hildreth. Recovering heading for visually guided navigation in the presence of self-moving objects. Phil. Trans. Roy. Soc. London, Ser. B, 337:305–313, 1992.
K. Kanatani. Renormalization for unbiased estimation. In Proc. 4th Int. Conf. on Computer Vision, pages 599–606, 1993.
J.J. Koenderink and A.J. van Doorn. Affine structure from motion. J. Opt. Soc. America, 8:377–385, 1991.
J.M. Lawn and R. Cipolla. Epipole estimation using affine motion-parallax. In Proc. 4th British Machine Vision Conference, pages 379–388, 1993.
J.M. Lawn and R. Cipolla. Robust egomotion estimation from affine motion-parallax. Tech. Rep. CUED/F-INFENG/TR.160, University of Cambridge, 1994.
H.C. Longuet-Higgins and K. Prazdny. The interpretation of a moving retinal image. Proc. of the Royal Society of London, Series B, 208:385–397, 1980.
Q.-T. Luong, R. Deriche, O. Faugeras, and T. Papadopoulo. On determining the fundamental matrix: Analysis of different methods and experimental results. Technical Report 1894, INRIA, France, 1993.
S. Negahdaripour and S. Lee. Motion recovery from image sequences using only first order optical flow information. Int. J. of Comp. Vision, 9(3):163–184, 1992.
J.H. Rieger and D.L. Lawton. Processing differential image motion. J. Opt. Soc. America, A2(2):354–360, 1985.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lawn, J.M., Cipolla, R. (1994). Robust egomotion estimation from affine motion parallax. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57956-7_24
Download citation
DOI: https://doi.org/10.1007/3-540-57956-7_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57956-4
Online ISBN: 978-3-540-48398-4
eBook Packages: Springer Book Archive