Title
Fast and Globally Convergent Pose Estimation from Video Images
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.
Year
DOI
Venue
2000
10.1109/34.862199
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
computes orthogonal rotation matrix,globally convergent pose estimation,iterative optimization method,object space collinearity error,estimation problem,computer vision,video images,iterative algorithm,rotation matrix,optimization method,best method,classical problem,intersymbol interference,robot kinematics,photogrammetry,iterative methods,time domain,impulse response,indexing terms,computational complexity,computer graphics,computational geometry,test methods,pose estimation,optimization,convergence,minimisation,rotation matrices
Computer vision,Collinearity,Rotation matrix,Computer science,Iterative method,Rigid transformation,3D pose estimation,Pose,Orthonormal basis,Artificial intelligence,Computational complexity theory
Journal
Volume
Issue
ISSN
22
6
0162-8828
Citations 
PageRank 
References 
265
14.96
26
Authors
3
Search Limit
100265
Name
Order
Citations
PageRank
Chien-Ping Lu127218.14
Gregory D. Hager23871400.32
Eric Mjolsness31058140.00