Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B5, 151-155, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B5/151/2012/
doi:10.5194/isprsarchives-XXXIX-B5-151-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
27 Jul 2012
CO-REGISTRATION OF 3D POINT CLOUDS BY USING AN ERRORS-IN-VARIABLES MODEL
U. Aydar1, M. O. Altan1, O. Akyılmaz1, and D. Akca2 1Istanbul Technical University (ITU), Faculty of Civil Engineering, Department of Geomatics Engineering, 34469 Maslak, Istanbul, Turkey
2Isik University, Faculty of Engineering, Department of Civil Engineering, 34980 Sile, Istanbul, Turkey
Keywords: Laser scanning, point cloud, registration, matching, estimation Abstract. 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 the literature, one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D least squares (LS) matching methods as well. In most of the co-registration methods, 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. This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a new method where the stochastic properties of both (template and search) surfaces are considered under an errors-in-variables (EIV) model. The experiments have been carried out using a close range laser scanning data set and the results of the conventional and EIV types of the ICP matching methods have been compared.
Conference paper (PDF, 833 KB)


Citation: Aydar, U., Altan, M. O., Akyılmaz, O., and Akca, D.: CO-REGISTRATION OF 3D POINT CLOUDS BY USING AN ERRORS-IN-VARIABLES MODEL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B5, 151-155, doi:10.5194/isprsarchives-XXXIX-B5-151-2012, 2012.

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