HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration of Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.
Citation:
HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration of Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.
HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration of Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.
Citation:
HAN Xianquan, ZHU Qing, DING Yulin, ZHOU Dongbo. Precise Registration of Scattered Cloud Data Based on the Particle Swarm Optimization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1214-1220.
1State Key Laboratory of Information Engineerin g in Surveyin g Mapp in g and Remote Sensing Wuhan UniversityWuhan 430079 China;2 Engineerin g Safety and Disaster Prevention Department Yangtze River Scientific Research InstituteWuhan 430010China;3 Faculty of Geosciences and Environmental Engineerin gSouthwest Jiaoton g UniversityChengdu 610031China;4 Institute of Space and Earth Information Sciencethe Chinese University of Hong KongHong Kong 999077 China;5 National Engineerin g Research Center for E-Learning Science HallHuazhong Normal UniversityWuhan 430079 China
As one of the core ste ps in point cloud data processingthe re gistration result has great influences on the subsequent data operations．Traditional precise re gistration methods mainly depend on artificial tar gets and feature points．These methods are limited by the external environmentinitial conditionsfeature points are not eas y to find and so on．To overcome the limitation this paper proposes an improved Particle Swarm optimization PSOal gorithm．Using the sum of normal vectors cross products to define the fitness function the current al gorithm applies an efficient Universal Searchand implements scattered cloud data re gistration based on the best re gistration points．By the experiment with the cloud data received by a multi－station scanning of a hi gh stee p slo pe rock and comparing the result with the classical al gorithms such as ICPthe improved PSO al gorithm is proved to be feasibleefficient and stable．It can effectivel y solve the problem of the tar gets or the feature points are not eas y to find in re gistration process．
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