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Automatic Global Registration for Terrestrial Laser Scanner Data  

Kim, Chang-Jae (연세대학교)
Eo, Yang-Dam (건국대학교 신기술융합학과)
Han, Dong-Yeob (전남대학교 공학대학 건설환경공학부)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.28, no.2, 2010 , pp. 281-287 More about this Journal
Abstract
This study compares transformation algorithms for co-registration of terrestrial laser scan data. Pair-wise transformation which is used for transformation of scan data from more than two different view accumulates errors. ICP algorithm commonly used for co-registration between scan data needs initial geometry information. And it is difficult to co-register simultaneously because of too many control points when managing scan at the same time. Therefore, this study perform global registration technique using matching points. Matching points are extracted automatically from intensity image by SIFT and global registration is performed using GP analysis. There are advantages for operation speed, accuracy, automation in suggested global registration algorithm. Through the result from it, registration algorithms can be developed by considering accuracy and speed.
Keywords
Global Registration; Terrestrial Laser Scanner; SIFT; ICP;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Jason G. and Philip J. (2008), Multiview range-image registration for forested scenes using explicitly-matched tie points estimated from natural surfaces, ISPRS Journal of Photogrammetry and Remote Sensing, ISPRS, Vol. 63, No. 1, pp. 68-83.   DOI   ScienceOn
2 Kortgen, M. (2006), Robust Automatic Registration of Range Images with Reflectance, Master's thesis, Computer Graphics Institute, University of Bonn, Germany.
3 Wang, Y. and Wang G. (2008), Integrated Registration of Range Images from Terrestlial LIDAR, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS, Beijing, pp. 361-365.
4 Hom, K.P. (1987), Closed-fonn solution of absolute orientation using unit quatemions, Journal of the Optical Society of America A, Optical Society of America, Vol. 4, No.4, pp. 629-642.   DOI
5 Fischler, M.A. and Bolles, R.C. (1981), Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the Association for Computing Machinery, ACM, Vol. 24, No.6, pp. 381-395.   DOI   ScienceOn
6 Beinat, A. and CrosiIla, F. (2002), A generalized factored stochastic model for the optimal global registration of LIDAR range images, IAPRS, ISPRS, 34(3B), pp. 36-39.
7 Williams, J. and Bennamoun, M. (2001), Simultaneous Registration of Multiple Corresponding Point Sets, Computer Vision and Image Understanding, CVIU, Vol. 81, No.1,pp.117-142.   DOI   ScienceOn
8 Lowe, D.G. (2005), Demo Software: SIFT Keypoint Detector, http://people.cs.ubc.ca/-Iowe/keypoints
9 Gruen, A. and Akca, D. (2005), Least squares 3D surface and curvc matching, ISPRS Journal of Photogrammetry and Remote Sensing, ISPRS, Vol. 59, No.3, pp. 151-174.   DOI   ScienceOn
10 전민철, 어양담, 한동엽, 강남기, 편무욱 (2010), 강도영상과 거리영상에 의한 건물 스캐닝 점군간 3차원 정합실험, 대한원격탐사학회지, 대한원격탐사학회, 제 26권, 제 1호, pp.39-45.   과학기술학회마을   DOI
11 Besl, P. and McKay, N. (1992), A Method for Registration of 3-D Shapes, IEEE Transaction on Pattern Analysis And Machine Intelligence, IEEE, Vol. 14, No.2, pp. 239-256.   DOI   ScienceOn