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ID 48127
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Abstract
Because 3-D data are acquired using 3-D sensing such as stereo vision and laser range finders, they have inhomogeneous and anisotropic noise. This paper studies optimal computation of the similarity (rotation, translation, and scale change) of such 3-D data. We first point out that the Gauss-Newton and the Gauss-Helmert methods, regarded as different techniques, have similar structures. We then combine them to define what we call the modified Gauss-Helmert method and do stereo vision simulation to show that it is superior to either of the two in convergence performance. Finally, we show an application to real GPS geodetic data and point out that the widely used homogeneous and isotropic noise model is insufficient and that GPS geodetic data are prone to numerical problems.
Published Date
2012-01
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Publication Title Alternative
岡山大学工学部紀要
Volume
volume46
Publisher
Faculty of Engineering, Okayama University
Start Page
21
End Page
33
ISSN
1349-6115
NCID
AA12014085
Content Type
Departmental Bulletin Paper
language
英語
Copyright Holders
Copyright © by the authors
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publisher
Refereed
False
Eprints Journal Name
mfe