Abstract
The goal in the motion estimation problem is to determine the transformation between two three-dimensional positions of a rigid body that is undergoing some arbitrary motion. Throughout this discussion it will be assumed that it is possible to acquire three-dimensional positional information of points located on the rigid body at two separate time instances. This may be accomplished through the use of a stereo camera setup (Barnard and Fischler, 1982). Alternatively, three-dimensional positional data can be obtained explicitly from a laser range finder. In general, points selected from the rigid body will tend to correspond to special geometrical features, such as corners or identifiable surface markings, and must often be extracted by special low level processing tasks (Gu and Huang, 1984). Such feature points will be invariant to the particular position sensing techniques used, and thus may be reliably located independent of object position and orientation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arun, S., Huang, T.S., and Blostein S.D., (1987) ‘Least-squares fitting of two 3-D point sets,’ IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 9, no, 5.
Ball, R., (1876) Theory of Screws, Hodges, Foster and Co., Dublin.
Barnard, S., and Fischler, M.A., (1982) ‘Computational stereo.,’ Computing Surveys, vol. 14, no. 4.
Blostein, S.D., and Huang, T.S., (1985) ‘Motion estimation based on stereo sequences,’ Tech. Rep. T-168, Coordinated Science Lab, University of Illinois.
Bottema, O., and Roth, B., (1979) Theoretical Kinematics, North Holland, Amsterdam, pp. 56–62.
Brogan, W.L., (1982) Modern Control Theory, Prentice-Hall, Englewood Cliffs, NJ, pp. 69–70.
Forsythe, G., and Malcolm, M., (1977) Computer Methods for Mathematical Computations, Prentice-Hall, Englewood Cliffs, NJ, pp. 41–48.
Faugeras, O.D., and Hebert, M., (1985) ‘A 3-D recognition and positioning algorithm using geometrical matching between primative surfaces,’ Proc. Int. Joint Conf. on Artificial Intelligence,Karlshure, FR Germany, pp. 998–1002.
Gu, W.K., and Huang, T.S., (1984) ‘Connected edge extraction from per- spective views of a polyhedron,’ Tech. Rep., University of Illinois.
Huang, T.S., Blostein, S.D., (1985) ‘Robust algorithms for motion estimation based on two sequential stereo image pairs,’ IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 518–523.
Huang, T.S., Blostein S.D., and Margerum, E.A., (1986) ‘Least-squares estimation of motion parameters from 3-D point correspondences,’ IEEE Conf. on Computer Vision and Pattern Recognition,Miami, FL, pp. 198–201.
Kuperman, I.B., (1971) Approximate Linear Algebraic Equations, Van Nostrand Reinholt, London.
MacMillan, W.D., (1936) Theoretical Mechanics. Vol 3: Dynamics of Rigid Bodies, McGraw-Hill, New York, pp. 166–172.
Papoulis, A., (1965) Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New York.
Paul, R.P., (1981) Mathematics, Programming, and Control, MIT Press, Cambridge, MA, pp. 29–32.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Kluwer Academic Publishers
About this chapter
Cite this chapter
Blostein, S.D., Huang, T.S. (1988). Algorithms for Motion Estimation Based on Three-Dimensional Correspondences. In: Martin, W.N., Aggarwal, J.K. (eds) Motion Understanding. The Kluwer International Series in Engineering and Computer Science, vol 44. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1071-6_10
Download citation
DOI: https://doi.org/10.1007/978-1-4613-1071-6_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8413-0
Online ISBN: 978-1-4613-1071-6
eBook Packages: Springer Book Archive