带工程约束且可剔除错误点船体分段点集匹配方法
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摘要
为了有效提高船体分段测量点集与理论点集的精准匹配技术,提出了一种自动剔除错误点且考虑工程约束的船体分段点集匹配方法.该方法分为粗匹配与精匹配两步,粗匹配采用改进的随机抽样一致性算法实现了快速剔除错误点,同时基于奇异值分解法确定了刚性变换矩阵,获得了较准确的匹配初值;精匹配利用层次分析法自动获得了工程约束权值,采用权值向量将工程约束引入多目标函数中,通过求解多目标模型获得匹配结果.实例表明,该方法可快速自动剔除错误点,在考虑工程约束的条件下获得较精确的合理结果,为船体分段后续的搭载提供了依据.
In order to effectively improve the precision matching technique of measurement point set and design point set for hull blocks,apoint set matching method for hull blocks with engineering constraints and automatic error points elimination is presented.The method is divided into two steps:coarse matching and fine matching.In the coarse matching stage,the improved random sample consensus(RANSAC)algorithm is used to eliminate error points rapidly,at the same time the rigid transformation matrix is determined by singular value decomposition(SVD)in this algorithm,so more accurate initial matching values are obtained.In the fine matching stage,the analytic hierarchy process(AHP)is used to obtain the weight value of each engineering constraint automatically,and then the engineering constraints with weight vector are introduced into the multi-objective function.By solving the multi-objective model,the more reasonable matching results are obtained.The examples prove that this method can quickly and automatically eliminate error points,and get more accurate and reasonable results meeting engineering constraints.It can provide the basis for the subsequent assembly of hull blocks.
In order to effectively improve the precision matching technique of measurement point set and design point set for hull blocks,apoint set matching method for hull blocks with engineering constraints and automatic error points elimination is presented.The method is divided into two steps:coarse matching and fine matching.In the coarse matching stage,the improved random sample consensus(RANSAC)algorithm is used to eliminate error points rapidly,at the same time the rigid transformation matrix is determined by singular value decomposition(SVD)in this algorithm,so more accurate initial matching values are obtained.In the fine matching stage,the analytic hierarchy process(AHP)is used to obtain the weight value of each engineering constraint automatically,and then the engineering constraints with weight vector are introduced into the multi-objective function.By solving the multi-objective model,the more reasonable matching results are obtained.The examples prove that this method can quickly and automatically eliminate error points,and get more accurate and reasonable results meeting engineering constraints.It can provide the basis for the subsequent assembly of hull blocks.
引文
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[13]王庆璨.三维点云数据配准算法研究[D].西安:西安电子科技大学,2015.WANGQingcan. Researchonregistration algorithm of 3Dpoint cloud data[D].Xi′an:Xidian University,2015.(in Chinese)
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[15]管官,林焰,申玫,等.考虑工程约束的船体分段测量点集匹配方法[J].哈尔滨工程大学学报,2013,34(5):541-548.GUAN Guan,LIN Yan,SHEN Mei,et al.Point registration algorithm for hull block measurement with engineering constraints[J].Journal of Harbin Engineering University,2013,34(5):541-548.(in Chinese)
[16]SAATY T L.How to make a decision:The analytic hierarchy process[J]. European Journal of Operational Research,1990,48(1):9-26.
[2]林龙海.船体分段总组与合拢精度管理研究及应用[D].大连:大连理工大学,2015.LIN Longhai.Research and application on accuracy management for pre-erection and erection of ship hull[D].Dalian:Dalian University of Technology,2015.(in Chinese)
[3] ARUN K S,HUANG T S,BLOSTEIN S D.Least-squares fitting of two 3-D point sets[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(5):698-700.
[4] WOLD S,ESBENSEN K,GELADI P.Principal component analysis[J]. Chemometricsand Intelligent Laboratory Systems,1987,2(1/2/3):37-52.
[5] BESL P J, MCKAY N D. A method for registration of 3-D shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(2):239-256.
[6]张蕾,冀治航,普杰信,等.约束改进的ICP点云配准方法[J].计算机工程与应用,2012,48(18):197-200.ZHANG Lei,JI Zhihang,PU Jiexin,et al.ICP method improved by constraints for point cloud registration[J]. ComputerEngineeringand Applications,2012,48(18):197-200.(in Chinese)
[7] GUAN Guan,LIN Yan,JI Zhuoshang.Laser measurements registration algorithm for hull blocks with shipbuilding constraints[J].International Shipbuilding Progress,2014,61(1/2):1-15.
[8] LOWE D G.Distinctive image features from scaleinvariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[9] MYRONENKO A,SONG Xubo, CARREIRAPERPINAN M A.Non-rigid point set registration:Coherent point drift[C]//Advances in Neural Information Processing Systems 19—Proceedings of the2006 Conference. Vancouver:Neural Information Processing Systems Foundation,2007:1009-1016.
[10]任刚.船体分段三维测量及对位系统控制策略研究[D].大连:大连理工大学,2013.REN Gang. Research on 3D measurement and positioningcontrolmethodforhullblock construction[D].Dalian:Dalian University of Technology,2013.(in Chinese)
[11]FISCHLER M A,BOLLES R C.Random sample consensus:a paradigm for model fitting with application to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395.
[12]JIA Songmin,WANG Ke,LI Xiuzhi,et al.A novelimprovedprobability-guidedRANSAC algorithm for robot 3Dmap building[J].Journal of Sensors,2016,2016:3243842.
[13]王庆璨.三维点云数据配准算法研究[D].西安:西安电子科技大学,2015.WANGQingcan. Researchonregistration algorithm of 3Dpoint cloud data[D].Xi′an:Xidian University,2015.(in Chinese)
[14]ZHU Wenqiu,SUN Wenjing, WANG Yexiang,et al.An improved RANSAC algorithm based on similar structure constraints[C]//Proceedings—2016 International Conference on Robots and Intelligent System,ICRIS 2016.Piscataway:IEEE,2016:94-98.
[15]管官,林焰,申玫,等.考虑工程约束的船体分段测量点集匹配方法[J].哈尔滨工程大学学报,2013,34(5):541-548.GUAN Guan,LIN Yan,SHEN Mei,et al.Point registration algorithm for hull block measurement with engineering constraints[J].Journal of Harbin Engineering University,2013,34(5):541-548.(in Chinese)
[16]SAATY T L.How to make a decision:The analytic hierarchy process[J]. European Journal of Operational Research,1990,48(1):9-26.