基于几何特征的点云拼合研究
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摘要
点云拼合是逆向工程的一个重要环节,其拼合精度直接影响着后面的模型重建或精度检测。目前的研究主要集中在点云的直接拼合上,利用最近点迭代算法或点云的法矢、曲率信息实现点云数据的对齐。在实际应用中,大部分零件都包含若干直线、平面、圆柱等基本几何特征,而基于几何特征进行拼合的研究较少。本文对这种包含几何特征的点云数据的拼合进行了研究,并取得了如下成果:
1、给出了基于几何特征的点云拼合的约束条件,简单分析了基于这种拼合方式的测量规划。
2、运用坐标变换知识对几何特征进行空间转换,并结合两种不同的评价方式确定几何特征的拼合适应度,从而建立了粗、精拼优化目标函数。
3、用粒子群算法对粗、精拼优化目标函数分别给予实现,然后根据不同的收敛情况及粒子群算法的本身特点将这两种优化目标函数组装成一个优化目标函数,并用来完成发动机右半轴支架的拼合。
4、对几何特征赋予参数,解决多特征情况下对应特征的匹配问题,并利用新建特征代替原有特征解决相似特征问题,从而使优化模型的适用范围具有普遍性。
以机体和连杆两个实例对本文算法的优势进行研究:与点云的直接拼合相比,避免了点云的规模及重叠问题对拼合的影响,而且拼合时间短,拼合精度高;与逆向工程软件中特征对齐相比,能够自动匹配特征,避免了对应特征选择时带来的麻烦,而且拼合结果更精确。
Point clouds registration, the precision of which determines the model reconstruction and the accuracy detecting, plays an important role in reverse engineering. At present, the reported work on registration mainly aims at direct registration by ICP algorithm, as well as cloud normal vector or curvature information. In actual application, most of the parts have special features, such as lines, planes and cylinders. However, there are few studies of registration based on feature. So this dissertation focuses on such registration, and gets achievements as following:
1. Give the constraint condition of registration based on feature, and make simple analysis on the measure plan based on such study.
2. Adopt coordinate transformation to analyze feature, and determine the fitness of registration by two different assessment modes, then establish gross and accurate registration optimizations.
3. The gross and accurate registration optimizations are solved by particle swarm optimization(PSO). Then analyze their different characteristics and assembly them into one optimization to finish the registration of engine right support.
4. The features are endowed parameters to solve the matching problem, when there are many constraint features in the part. This thesis also gives a way to solve the similar feature problem by constructing a new feature instead of the original one. Then the registration algorithm can be used generally.
The advantages of this algorithm are investigated by two examples-engine block and connecting rod. Compared with the direct registration of point cloud, this algorithm avoids registration affects caused by the cloud scale and the overlap problem, shortens registration time, and improves registration accuracy. Compared with feature alignment in reverse software, this method realizes intelligent registration and avoids the time-consuming work of picking corresponding feature, also has higher registration accuracy.
1、给出了基于几何特征的点云拼合的约束条件,简单分析了基于这种拼合方式的测量规划。
2、运用坐标变换知识对几何特征进行空间转换,并结合两种不同的评价方式确定几何特征的拼合适应度,从而建立了粗、精拼优化目标函数。
3、用粒子群算法对粗、精拼优化目标函数分别给予实现,然后根据不同的收敛情况及粒子群算法的本身特点将这两种优化目标函数组装成一个优化目标函数,并用来完成发动机右半轴支架的拼合。
4、对几何特征赋予参数,解决多特征情况下对应特征的匹配问题,并利用新建特征代替原有特征解决相似特征问题,从而使优化模型的适用范围具有普遍性。
以机体和连杆两个实例对本文算法的优势进行研究:与点云的直接拼合相比,避免了点云的规模及重叠问题对拼合的影响,而且拼合时间短,拼合精度高;与逆向工程软件中特征对齐相比,能够自动匹配特征,避免了对应特征选择时带来的麻烦,而且拼合结果更精确。
Point clouds registration, the precision of which determines the model reconstruction and the accuracy detecting, plays an important role in reverse engineering. At present, the reported work on registration mainly aims at direct registration by ICP algorithm, as well as cloud normal vector or curvature information. In actual application, most of the parts have special features, such as lines, planes and cylinders. However, there are few studies of registration based on feature. So this dissertation focuses on such registration, and gets achievements as following:
1. Give the constraint condition of registration based on feature, and make simple analysis on the measure plan based on such study.
2. Adopt coordinate transformation to analyze feature, and determine the fitness of registration by two different assessment modes, then establish gross and accurate registration optimizations.
3. The gross and accurate registration optimizations are solved by particle swarm optimization(PSO). Then analyze their different characteristics and assembly them into one optimization to finish the registration of engine right support.
4. The features are endowed parameters to solve the matching problem, when there are many constraint features in the part. This thesis also gives a way to solve the similar feature problem by constructing a new feature instead of the original one. Then the registration algorithm can be used generally.
The advantages of this algorithm are investigated by two examples-engine block and connecting rod. Compared with the direct registration of point cloud, this algorithm avoids registration affects caused by the cloud scale and the overlap problem, shortens registration time, and improves registration accuracy. Compared with feature alignment in reverse software, this method realizes intelligent registration and avoids the time-consuming work of picking corresponding feature, also has higher registration accuracy.
引文
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[2]吴敏,周来水,王占东等.测量点云数据的多视拼合技术研究.南京航空航天大学学报,2003, 35(5): 552～557.
[3]许志龙.逆向工程中多视角点云数据拼合技术.组合机床与自动化加工技术,2006, 7:26～29.
[4]黄小平.逆向工程中数据云处理关键技术研究.博士学位论文,湖北:华中科技大学,2002.
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[12]李春玲.复杂曲面激光测量与重构相关技术研究.硕士学位论文,山东:山东大学,2005.
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[14]程俊延,赵灿,王从军等.基于参考点和ICP算法的点云数据重定位研究.计算机测量与控制,2006,14(9):1222～1224.
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[16]郑成志.逆向工程分块数据自动拼合等关键技术研究.硕士学位论文,辽宁:辽宁工程技术大学,2005.
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[24]PARKJ,CHOI K,ALLSTOT D J.Parasitic-aware RF circuit design and Optimization[J].IEEE Transactions on Circuit s and Systems I:Fundamental Theory and Applic-ations,2004,51 (10) :1953-1966.
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[29]庞巍,王康平,周春光.模糊离散粒子群优化算法求解旅行商问题[J].小型微型计算机系统,2005,26 (8) :1331-1334.
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[37]卢晋人,黄元庆.激光三角法测量表面形貌.厦门大学学报, 2004, 43(1):50～53
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[2]吴敏,周来水,王占东等.测量点云数据的多视拼合技术研究.南京航空航天大学学报,2003, 35(5): 552～557.
[3]许志龙.逆向工程中多视角点云数据拼合技术.组合机床与自动化加工技术,2006, 7:26～29.
[4]黄小平.逆向工程中数据云处理关键技术研究.博士学位论文,湖北:华中科技大学,2002.
[5]许智钦,孙长库等.3D逆向工程技术.计量科学出版社.2002:56～59.
[6]李同方.逆向工程中点数据多视对齐软件系统的研究.硕士学位论文,四川:四川大学,2005.
[7] Besl P J, McKay N D. A method for registration of 3-Dshapes [J]. IEEE Transa-ctions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256.
[8] PAUL J B,NEIL D M.A method for registration of 3D shapes[J]. IEEE Transacti-ons on Pattern Analysiy and Machine Intelligence,1992,14(2):239～256.
[9] Horn B K P.Closed-form Solution of absolute orientatoin using unit quaternions[J].Journal Optical of Society American A,1987,4(4):629～642.
[10]刘宇,熊有伦.基于法矢的点云拼合方法.机械工程学报,2007,43(8):7～11.
[11]孙世为,梁培志,李志刚.基于曲率RGB的多视点云拼合方法.中国机械工程,2005,16 (10):882～884.
[12]李春玲.复杂曲面激光测量与重构相关技术研究.硕士学位论文,山东:山东大学,2005.
[13]孙殿柱,孙肖霞,李延瑞等.一种基于最小二乘固定球法的多视点云拼合技术.机械设计与研究,2006,22(3):57～59.
[14]程俊延,赵灿,王从军等.基于参考点和ICP算法的点云数据重定位研究.计算机测量与控制,2006,14(9):1222～1224.
[15]王霄.逆向工程技术及其应用.北京:化学工业出版社教材出版中心.2004.
[16]郑成志.逆向工程分块数据自动拼合等关键技术研究.硕士学位论文,辽宁:辽宁工程技术大学,2005.
[17]Eberhart R C,Kennedy J.A New Optimizer Using Particle Swarm Theory[C],Pro-ceedings of the Sixth International Symposium on Micro Machine and Human Sc-ience, Nagoya, Japan. Piscataway, Nj: IEEE Service Center, 1995, 39～43.
[18]雷开友.粒子群算法及其应用研究.博士学位论文,重庆:西南大学,2006.
[19]Crepinsek, M, Mernik, M, and Zummer,V. Using flocks for solving numericalopt-imization problems. In Proceedings of the 24th International Conference on Infor-mation Technology Interfaces,2002:395～400.
[20]Reynold, C. W. Flocks, herds and schools: A distributed behavioral model. Comp-uter Graphics, 1987, 21(4): 25～34.
[21]王芳.粒子群算法的研究.博士学位论文,重庆:西南大学,2006.
[22]马金玲.改进粒子群优化算法的研究.硕士学位论文,四川:电子科技大学,2008.
[23]YIN Pengyeng,YU Shiuhsheng,WANG Peipei,etal. Ahybrid particle swarm optimiz-ation algorithm for optimal task assignment in distributed systems[J]. Computer S-tandards and Interfaces.2005,28 (4) :441-450.
[24]PARKJ,CHOI K,ALLSTOT D J.Parasitic-aware RF circuit design and Optimization[J].IEEE Transactions on Circuit s and Systems I:Fundamental Theory and Applic-ations,2004,51 (10) :1953-1966.
[25]王晓丽.粒子群优化算法的研究及其应用.硕士学位论文,山西:太原科技大学,2008.
[26]李宁,邹彤.基于粒子群的多目标优化算法[J].计算机工程与应用,2005,41(23) :43-47.
[27]刘华蓥,林玉娥,齐名军.求解约束优化问题的改进粒子群算法[J].大庆石油学院学报,2005 ,29 (4) :73-75.
[28]刘钊,康立山,蒋良孝.用粒子群优化改进算法求解混合整数非线性规划问题[J].小型微型计算机系统,2005,26 (8) :991-994.
[29]庞巍,王康平,周春光.模糊离散粒子群优化算法求解旅行商问题[J].小型微型计算机系统,2005,26 (8) :1331-1334.
[30]严庆光.面向多点成形的逆向工程关键技术及应用研究.博士学位论文,吉林:吉林大学,2005.
[31]张孝林.逆向工程中自由曲面的测量规划与建模技术研究.硕士学位论文,陕西:西安理工大学,2008.
[32]聂恒卫.基于激光测量系统的数据测量和数据处理技术研究.硕士学位论文,江苏:江南大学,2006.
[33]梁荣茗.三坐标测量机的设计使用维修与检定.北京:中国计量出版社, 2001.
[34]慈瑞梅.基于CMM测量数据的曲面重构关键技术研究与实现.博士学位论文,南京:南京理工大学,2005.
[35]Shuh-Ren Liang, Alan C. Lin. Probe-radius compensation for 3D data points in reverse engineering.Computers in Industry, 2002, 48(1):241～251.
[36]Hon-yuen Tam. Toward the uniform coverage of surface by scanning curves.Com-puter Aided Design, 1999, 31(3):585～596.
[37]卢晋人,黄元庆.激光三角法测量表面形貌.厦门大学学报, 2004, 43(1):50～53
[38]许智钦,闫明,张宝峰等.逆向工程技术三维激光扫描测量.天津大学学报, 2001,34(3):404～407.
[39]李剑.基于激光测量的自由曲面数字制造基础技术研究.博士学位论文,杭州:浙江大学,2001, 12.
[40]胡寅.三维扫描仪与逆向工程关键技术研究.博士学位论文,湖北:华中科技大学,2005.
[41]王红敏.逆向工程测量技术研究.硕士学位论文,山东:山东大学, 2007.
[42]孙中升.关节臂测量机在大尺寸测量中的应用.江苏现代计量, 2008:38～39.
[43]吴剑锋,王文,陈子辰.激光三角法测量误差分析与精度提高研究.机电工程,2003,20(5):89～91.
[44]熊有伦.机器人技术基础.湖北:华中科技大学出版社,1996.
[45]Angeline PJ. Evolutionary optimization versus particle swarm optimization: philos-ophy and performance difference. Proceedings of Annual Conference on Evolutio-nary Programming, San Diego, 1998
[46]周驰.粒子群优化算法应用研究.硕士学位论文,湖北:华中科技大学,2007.
[47]何妮,吴燕仙.粒子群优化算法的研究.科技信息, 2008, 6:179～180.
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