Motion and structure estimation from stereo image sequences

A closed-form approximate matrix-weighted solution for estimating motion parameters from point correspondences in two stereo image pairs is presented. The corresponding algorithm is noniterative and fast. Simulation shows that the solution is significantly more reliable than the unweighted and the s...

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Vydáno v:IEEE transactions on robotics and automation Ročník 8; číslo 3; s. 362 - 382
Hlavní autoři: Weng, J., Cohen, P., Rebibo, N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.06.1992
Institute of Electrical and Electronics Engineers
Témata:
Applied sciences
Closed-form solution
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Filtering
Gaussian noise
Image sequences
Iterative algorithms
Kalman filters
Least squares approximation
Least squares methods
Motion estimation
Robotics
Stereo vision
Parameter estimation
Experimental result
Minimal variance
Simulation
Least squares method
Image sequence
Kalman filter
Nonlinearity
Closed form equation
Optimal estimation
Robotics
Cramer Rao inequality
ISSN:1042-296X
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Shrnutí:A closed-form approximate matrix-weighted solution for estimating motion parameters from point correspondences in two stereo image pairs is presented. The corresponding algorithm is noniterative and fast. Simulation shows that the solution is significantly more reliable than the unweighted and the scalar-weighted solutions, except when the number of point correspondences is less than seven. The matrix-weighted least squares solution can be used as a final solution if the speed requirement does not allow iterations. An approach to optimal estimation of the motion parameters and the structure of the three-dimensional points is also introduced. In experiments with a calibrated real stereo setup, the motion parameters and depth maps were automatically computed from real-world images. Some ground truths were used to validate the accuracy of these results.< >
ISSN:1042-296X
DOI:10.1109/70.143354