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ID 48127
フルテキストURL
著者
Kanatani, Kenichi Department of Computer Science, Okayama University
Niitsuma, Hirotaka Department of Computer Science, Okayama University
抄録
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.
発行日
2012-01
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
出版物タイトル(別表記)
岡山大学工学部紀要
46巻
出版者
Faculty of Engineering, Okayama University
開始ページ
21
終了ページ
33
ISSN
1349-6115
NCID
AA12014085
資料タイプ
紀要論文
言語
English
OAI-PMH Set
岡山大学
著作権者
Copyright © by the authors
論文のバージョン
publisher
査読
無し
Sort Key
4
Eprints Journal Name
mfe