Fast and globally convergent pose estimation from video images
Lu, C.-P.
Hager, G.D.
Mjolsness, E.
IBEAM Broadcasting Corp., Sunnyvale, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 2000
Volume: 22,
Issue: 6
On page(s): 610-622
ISSN: 0162-8828
References Cited: 46
CODEN: ITPIDJ
INSPEC Accession Number: 6693743
Digital Object Identifier: 10.1109/34.862199
Current Version Published: 2002-08-06
Abstract
Determining the rigid transformation relating 2D images to known
3D geometry is a classical problem in photogrammetry and computer
vision. Heretofore, the best methods for solving the problem have relied
on iterative optimization methods which cannot be proven to converge
and/or which do not effectively account for the orthonormal structure of
rotation matrices. We show that the pose estimation problem can be
formulated as that of minimizing an error metric based on collinearity
in object (as opposed to image) space. Using object space collinearity
error, we derive an iterative algorithm which directly computes
orthogonal rotation matrices and which is globally convergent.
Experimentally, we show that the method is computationally efficient,
that it is no less accurate than the best currently employed
optimization methods, and that it outperforms all tested methods in
robustness to outliers
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