A registration method from three-dimensional depth image to three-dimensional geometric models was proposed aiming at laser scanning point cloud data from multi angle of the complex curved surface object. The effective initial matching points array was constructed with curvature invariant features and normalized zero-mean cross-correlation coefficient (NZCC)according to the invariant characteristics of the relative position of the space points in the condition of rigid body transformation. The coordinate transformation of the matching feature points was solved based on the unit quaternion. Then the data coarse registration was completed. The method and procedure for determining the modified coefficient were discussed, and mean error of different correction factors was calculated in order to get the best modified coefficient. The different perspectives of clouds were optimally matched in three-dimensional space by using fine matching technology based on adaptive distance function and the improved iterative closest surface. The registration error was calculated according to the matching results, and the registration accuracy and speed were analyzed. The numerical experiments results show that the method can effectively improve the efficiency of registration in the guarantee of the registration accuracy.
Received: 01 August 2016
Published: 27 September 2017
ZHANG Mei, PENG Xing-xing. Fine registration with adaptive distance function and iterative closest surface. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(10): 1920-1927.
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