Total Least Squares Registration of 3D Surfaces

Authors : Umut Aydar, M. Orham Altan

Pages : 27-38

Doi:10.30897/ijegeo.303539

View : 12 | Download : 7

Publication Date : 2015-08-03

Article Type : Research Paper

Abstract :Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:`Normal Tablo`; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:``; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:none; font-size:11.0pt; font-family:`Calibri`,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of co-registration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In computer vision and photogrammetry domain one of the most popular methods is the ICP insert ignore into journalissuearticles values(Iterative Closest Point); algorithm and its variants. There exist the 3D Least Squares insert ignore into journalissuearticles values(LS); matching methods as well insert ignore into journalissuearticles values(Gruen and Akca, 2005);. The co-registration methods commonly use the least squares insert ignore into journalissuearticles values(LS); estimation method in which the unknown transformation parameters of the insert ignore into journalissuearticles values(floating); search surface is functionally related to the observation of the insert ignore into journalissuearticles values(fixed); template surface. Here, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values of the search surface. . This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a method where the stochastic properties of both the observations and the parameters are considered under an errors-in-variables insert ignore into journalissuearticles values(EIV); model. The experiments have been carried out using diverse laser scanning data sets and the results of EIV with the ICP and the conventional LS matching methods have been compared.
Keywords : Laser scanning, Point Cloud, Registration, Matching