Recursive three-dimensional model reconstruction based on Kalman filtering
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AbstractA recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-dimensional (3-D) space. These two steps alternate from frame to frame. The initial model converges to the final structure as the image sequence is scanned sequentially. The performance of the algorithm is demonstrated with both synthetic data and real-world objects. Analytical and empirical comparisons are made among our approach, the interleaved bundle adjustment method, and the Kalman filtering-based recursive algorithm by Azarbayejani and Pentland. Our approach outperformed the other two algorithms in terms of computation speed without loss in the quality of model reconstruction.
All Author(s) ListYu YK, Wong KH, Chang MMY
Journal nameIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume Number35
Issue Number3
Pages587 - 592
LanguagesEnglish-United Kingdom
Keywords3-D structure acquisition; Kalman filtering; multimedia processing; structure from motion
Web of Science Subject CategoriesAutomation & Control Systems; AUTOMATION & CONTROL SYSTEMS; Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Cybernetics; COMPUTER SCIENCE, CYBERNETICS

Last updated on 2020-09-08 at 04:32