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Estimating rigid‐body motion from three‐dimensional data without matching point correspondences
Author(s) -
Lin ZseCherng,
Lee Hua,
Huang Thomas S.
Publication year - 1990
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.1850020108
Subject(s) - matching (statistics) , rotation (mathematics) , motion (physics) , artificial intelligence , point set registration , feature (linguistics) , computer vision , noise (video) , computer science , rigid body , structure from motion , mathematics , point (geometry) , motion estimation , trajectory , object (grammar) , matrix (chemical analysis) , pattern recognition (psychology) , image (mathematics) , geometry , statistics , linguistics , philosophy , physics , materials science , classical mechanics , composite material , astronomy
The estimation of the three‐dimensional (3‐D) motion parameters of a rigid body is a very important subject in scene analysis and trajectory prediction. Motion parameters can be estimated from two sets of object feature points before and after the motion. In general, the matching correspondences of the feature points are available, and the motion parameters can be estimated by solving equations associated with the correspondences. In this paper, we present a new method for motion estimation from 3‐D data without requiring the knowledge of matching correspondences. In the noise‐free case, this approach identifies four candidates for the rotation matrix. The rotation matrix giving the best match of the point features can then be selected from the four candidates, and the matching correspondences are subsequently established. Possible ambiguities due to symmetrical feature points are also discussed in this paper. In the presence of noise, this method provides an initial estimate of the motion, which is then used to establish the matching correspondences. Subsequently, a new estimate of the motion parameters can be obtained with the established matching correspondence information. The effects of random zero‐mean noise are studied. Simulated results are shown to demonstrate the effectiveness and accuracy of this technique.