DocumentCode :
3087874
Title :
Projective distribution entropy and point clouds mosaic algorithm
Author :
Lu Min ; Tan Zhiguo ; Guo Yulan ; Zuo Chao ; Yu Huiying
Author_Institution :
Sch. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China
fYear :
2012
fDate :
16-18 Dec. 2012
Firstpage :
146
Lastpage :
151
Abstract :
The multi-view point clouds mosaic IS an important technology in ladar data preprocessing, and it is also an open problem in 3D information processing. In the paper, a projective distribution entropy based point clouds mosaic algorithm is proposed. First, a unique coordinate system is used to estimate the space transformation between the multi-view point clouds, and to perform the coarse mosaic. Second, the Iterative Closest Point (ICP) method is applied for the fine mosaic. In order to enhance the robustness of the ICP method, the deterministic annealing algorithm is used. Experiments on complex point clouds demonstrated that the algorithm was reliable and effective.
Keywords :
clouds; entropy; iterative methods; object detection; optical radar; radar imaging; 3D information processing; ICP method; annealing algorithm; iterative closest point method; multiview point cloud; point cloud mosaic algorithm; projective distribution entropy; space transformation; unique coordinate system; Accuracy; Annealing; Iterative closest point algorithm; 3D point cloud; annealing ICP; laser optics; point cloud scenes mosaic; projective distribution entropy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision in Remote Sensing (CVRS), 2012 International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4673-1272-1
Type :
conf
DOI :
10.1109/CVRS.2012.6421250
Filename :
6421250
Link To Document :
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