DocumentCode
1125597
Title
Least-Squares Fitting of Two 3-D Point Sets
Author
Arun, K.S. ; Huang, T.S. ; Blostein, S.D.
Author_Institution
Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801.
Issue
5
fYear
1987
Firstpage
698
Lastpage
700
Abstract
Two point sets {pi} and {p´i}; i = 1, 2,..., N are related by p´i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p´i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.
Keywords
Application software; Computer vision; Economic indicators; Iterative algorithms; Matrix decomposition; Motion estimation; Parameter estimation; Position measurement; Quaternions; Singular value decomposition; Computer vision; least-squares; motion estimation; quaternion; singular value decomposition;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.1987.4767965
Filename
4767965
Link To Document