微工作台运动姿态视觉测量与跟踪方法研究
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摘要
随着现代机械加工工艺的深入发展,机械加工对象逐渐微型化、精密化,由此发展起来的微机械加工技术为机械制造开辟了一块崭新的领域。微机械加工广泛应用于航天国防、工业制造、精密仪器、生物医学等方面,是一个国家现代工业发展水平的重要标志之一。在微机械加工过程中,需要对微机械加工对象的几何尺寸进行实时地检测与跟踪,并反馈给运动控制系统,从而提高加工精度。但微机械结构及操作平台的承载能力都比较小,传统的几何测量传感器不适用于加工过程的微机械工件的尺寸检测,因此,微机械加工对象的几何尺寸检测只能采用非接触式测量方法。
本文提出了一种基于机器视觉的微工作台三维运动姿态检测与跟踪的方法。该方法将微机械加工平台物理形状抽象为一个长方体,并选取该长方体上的四个角点作为特征点,利用双目视觉测量原理实现特征点的三维世界坐标测量及表征点的动态匹配跟踪,同时使用特征点的三维坐标变化表征微工作台的位姿变化,实现对微工作台运动姿态实时、非接触的快速测量及跟踪检测,并且能够降低微工作台三维运动误差检测的复杂性。
本文的主要研究成果如下:
1.利用针孔成像原理,建立摄像机成像的投影变换模型,获取空间点的三维坐标值的计算方法,结合双目视觉测量原理建立了微工作台三维姿态测量的算法。
2.利用数字图像处理的基本方法,建立了基于特征点匹配的图像跟踪算法和基于模板搜索方法的匹配算法,实现了微工作台的运动位姿的动态检测和跟踪。
3.利用摄像机和三坐标测量仪搭建微工作台姿态测量整体系统,该系统分为硬件设计部分和软件设计部分,并且通过实验证明了该测量系统的可靠性。
With the ever-changing development of modern machine manufacturing technology, micro-machining has been attached gradually. Micro-machining technology is supporting the development of new MEMS technology, involving electronic, mechanical, chemical, biological, and other disciplines, and it is widely used in national defense, aerospace, medical, biomedical, industrial manufacturing, consumption and other areas of civil, it is also considered as one of the most promising and revolutionary high-tech in the twenty-first century. However, in the micro-machining processing, because the micro-mechanical structure and operation of platform carrying capacity are relatively small, the traditional geometric detection sensor does not apply to detect geometry of workpiece in micro-mechanical machining processing, therefore, the geometric size of micro-mechanical testing only use non-contact measurement method.
Based on machine binocular vision measurement principle ,this paper presents a new method of micro-table three-dimensional motion attitude measurement and tracking , in this method ,the micro-machining platform is abstracted as a rectangular shape object, and the four corners of the rectangular shape are selected as feature points, the binocular vision measurement principle is used to achieve the world coordinate measurement and characterization of three-dimensional dynamic match and point tracking of the feature points ,and the change of three-dimensional coordinates of feature points is used to descript the posture change of feature points of micro-table , achieving real-time, non-contact measurement of micro-table posture and the rapid tracking and detection, and this method can reduce the complexity of three-dimensional micro-table motion error detection.
The main research results are as follows:
1. Based on pinhole imaging principles, established the projective transformation model of camera imaging process, the calculation method of three-dimensional coordinates of space point, combined with binocular vision measurement method to obtain three-dimensional posture measurement method of micro-table.
2. Using basic methods of digital image processing, the tracking algorithm is designed based on feature point matching and matching algorithm is got based on template search to implement the micro-table posture dynamic tracking.
3. To use the camera and the CMM, set up micro-table posture measurement system, the system hardware design and software design are also completed, and the experiment proved the feasibility of the measurement system.
本文提出了一种基于机器视觉的微工作台三维运动姿态检测与跟踪的方法。该方法将微机械加工平台物理形状抽象为一个长方体,并选取该长方体上的四个角点作为特征点,利用双目视觉测量原理实现特征点的三维世界坐标测量及表征点的动态匹配跟踪,同时使用特征点的三维坐标变化表征微工作台的位姿变化,实现对微工作台运动姿态实时、非接触的快速测量及跟踪检测,并且能够降低微工作台三维运动误差检测的复杂性。
本文的主要研究成果如下:
1.利用针孔成像原理,建立摄像机成像的投影变换模型,获取空间点的三维坐标值的计算方法,结合双目视觉测量原理建立了微工作台三维姿态测量的算法。
2.利用数字图像处理的基本方法,建立了基于特征点匹配的图像跟踪算法和基于模板搜索方法的匹配算法,实现了微工作台的运动位姿的动态检测和跟踪。
3.利用摄像机和三坐标测量仪搭建微工作台姿态测量整体系统,该系统分为硬件设计部分和软件设计部分,并且通过实验证明了该测量系统的可靠性。
With the ever-changing development of modern machine manufacturing technology, micro-machining has been attached gradually. Micro-machining technology is supporting the development of new MEMS technology, involving electronic, mechanical, chemical, biological, and other disciplines, and it is widely used in national defense, aerospace, medical, biomedical, industrial manufacturing, consumption and other areas of civil, it is also considered as one of the most promising and revolutionary high-tech in the twenty-first century. However, in the micro-machining processing, because the micro-mechanical structure and operation of platform carrying capacity are relatively small, the traditional geometric detection sensor does not apply to detect geometry of workpiece in micro-mechanical machining processing, therefore, the geometric size of micro-mechanical testing only use non-contact measurement method.
Based on machine binocular vision measurement principle ,this paper presents a new method of micro-table three-dimensional motion attitude measurement and tracking , in this method ,the micro-machining platform is abstracted as a rectangular shape object, and the four corners of the rectangular shape are selected as feature points, the binocular vision measurement principle is used to achieve the world coordinate measurement and characterization of three-dimensional dynamic match and point tracking of the feature points ,and the change of three-dimensional coordinates of feature points is used to descript the posture change of feature points of micro-table , achieving real-time, non-contact measurement of micro-table posture and the rapid tracking and detection, and this method can reduce the complexity of three-dimensional micro-table motion error detection.
The main research results are as follows:
1. Based on pinhole imaging principles, established the projective transformation model of camera imaging process, the calculation method of three-dimensional coordinates of space point, combined with binocular vision measurement method to obtain three-dimensional posture measurement method of micro-table.
2. Using basic methods of digital image processing, the tracking algorithm is designed based on feature point matching and matching algorithm is got based on template search to implement the micro-table posture dynamic tracking.
3. To use the camera and the CMM, set up micro-table posture measurement system, the system hardware design and software design are also completed, and the experiment proved the feasibility of the measurement system.
引文
[1]关胜晓.机器视觉及其应用发展.自动化博览,2005,22(3):5-9.
[2]江虹,郭树旭.基于机器视觉的车灯定位误差检测方法研究.气象水文海洋仪器2005年Z1期.
[3]章炜.机器视觉技术发展及其工业应用.红外,2006,27(2):31-34.
[4]邓进军,苑伟政,李晓莹.微机械器件形状与尺寸的图像测量研究[J].机械工程学报,2002,38(增刊):101-103.
[5] Kai W, Dieter R, Schafer. An Approach to Computer-aided Quality Control Based on 3D Coordinate Metrology[J]. Journal of Materials Processing Technology,2000,107: 96-110.
[6]刘盛,刘家豪,傅建中.面向MEMS构件在线检测和三维外型测量的立体视觉系统[J].中国机械工程,2003,14(22):1936-1938.
[7]孔明,王式民.共轴法立体视觉三维测量的研究[J].计量学报,2004,25(4):294-297.
[8] Sano T,Yamamoto H. Study of Micromanipulation Using Stereoscopic Microscope[J]. IEEE Transactions on Instrumentation and Measurement,2002, 51(2): 182-187.
[9] Christian Rembe ,Richard S. Muller.Measurement System for Full Three-Dimensional Motion Characterization of MEMS[J]. Journal of Microelectromechanical System,2002, 11(5): 367-377.
[10]刘岩.基于白光显微干涉术的微轮廓检测与三维重建[D].南昌:南昌航空大学,2007:1-5.
[11] Feng Y L, Li D C, Jin C Y. A MEMS Testing System Using Computer Microvision[J]. Micronanoelectronic Technology,2003, (7): 221-223.
[12] Guo T, Hu X D, Li D C. Application of Microscopic Interferometry in Dynamic Testing System of MEMS[J]. Micronanoelectronic Technology,2003, (7): 218-220.
[13]郝颖明,朱枫.目标位姿测量中的三维视觉方法[J].中国图像图形学报, 2002, 7(12):1247-1251.
[14]郭术义.三维视觉测量系统研究[D].北京:北京机械工业学院,1999:32-34.
[15] Song Baoquan, Zhou Zongtan. A New Computational Model of Biological Vision for Stereopsis[J]. Spromger-Verlag Heidelberg Volume, 2004, 27(3): 30-34.
[16]刘进.基于视觉的三维建模相关技术研究[D].南京:南京航空航天大学,2002:7-8.
[17]杨世民,王春海,丁红岩.运用摄像法实现对刚体六自由度低频运动的观测[J].天津大学学报,1997,30(3):380-383.
[18] Elias N. Malamasa, Euripides G.M. Petrakisa. A survey on industrial vision systems,applications and tools[J]. Image and Vision Computing. 2003, 22 (21): 171-188.
[19]周鑫.基于模型的位姿测量研究[D].沈阳:中国科学院沈阳自动化研究所,2003:2-3.
[20]刘祥峰.基于机器视觉技术的零件尺寸检测系统的研究[D].哈尔滨:哈尔滨工业大学,2005,1-12.
[21] Dworkin S.B., Nye T.J. Image processing for machine vision measurement of hot formed parts[J]. Journal of Materials Processing Technology, 2006, 18 (174):1–6.
[22]李洪洲.视觉技术在小尺寸机械零件形位公差检测中的应用[D].长春:长春工业大学,2007,1-7.
[23] Elias N. Malamasa, Euripides G.M. Petrakisa. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing, 2003, 22 (21): 171-188.
[24]朱铮涛,黎绍发.视觉测量技术及其在现代制造业中的应用[J].现代制造工程,2004, (4):59-61.
[25] Cla′udio Rosito Jung, Christian Roberto Kelber. Lane following and lane departure using a linear-parabolic model[J]. Image and Vision Computing, 2005, 19 (23): 1192-1202.
[26]阮利峰,王庚,盛焕烨.基于标志点识别的三维位姿测量方法[J].计算机应用,2008,11(28):2857-2862.
[27]王芳.基于机器视觉的微工作台运动误差三点测量方法研究[D].河南:河南科技大学,2008,22-31.
[28] Leiyan zhang, HuiJie Zhao, Hongzhi Jiang.A Three-Dimensional Measurement Method by Combining Binocular and Monocular Vision System[J].Acta Optica Sinica,2008,28(7):1338-1342.
[29]张世杰,曹喜滨,陈闽.非合作航天器间相对位姿的单目视觉确定算法[J].南京理工大学学报,2006,30(5):564-568.
[30]秦丽娟,胡玉兰,魏英姿.基于矩形的三维物体位姿估计研究[J].计算机工程与科学, 2009,31(4):49-51.
[31]刘昶,朱枫,欧锦军.基于门形三条直线的P3L问题的闭式解[J].沈阳理工大学学报,2009,28(4):11-14.
[32]王振宁,钱东海,陈振华.基于直线段提取及其参数化的矩形重构方法研究[J].计算机工程与应用,2005,41(18):77-80.
[33]王晓剑,潘顺良,邱力为.基于双平行线特征的位姿估计解析算法[J].仪器仪表学报,2009,29(3):600-604.
[34] Vanden Heuvel, Frank A. Exterior orientation using coplanar parallel lines[C]. Proceedings of the 10th Scandinavian Conference on Image Analysis. Lappeenranta, 1997, 71-78.
[35] Abdel-Aziz Y L, Karara H M. Direct line transformation into object space coordinates inclose-range photogrammetry[C]. Urbana: Proceeding SymP Close-Range Photogrammrtry. 1971, 1-18.
[36] Wong K W. Mathematical foundation and digital analysis in close-range photogrammrtry[C]. Photogrammetric Engineering and Remote sensing.1975,44:1355-1373
[37] Karara H M. Handbook of close-range photogrammetry[M]. America Society of Photogrammetry, 1979.
[38] Faig W. Calibration of close-range photogrammetry system: Mathematical Formation[J]. Photogramm. Eng. Remote Seris. 1975, 41(12):1479-1486.
[39] Sid-Ahmed M A, Mohamed T B. Dual camera calibration for 3-D machine vision metrology[J]. IEEE Transactions on Instrumentation and Measurement, 1990, 39(3):512-516.
[40] Song Baoquan, Zhou Zongtan. A New Computational Model of Biological Vision for Stereopsis[J]. Spromger-Verlag Heidelberg Volume, 2004, 27(3): 30-34.
[41] Jonsson K., Kittler J. Support vector machines for face authentication[J]. Image and Vision Computing.2002, 9 (20):369-375.
[42] Wei G, Ma S. Implicit and explicit camera calibration: Theory and Experiments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1994, 16(5):469-480.
[43] Zhang Z. A flexible new technique for camera calibration[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000, 22(11):1330-1334.
[44] Hartley R I. In defence of the 8-points algorithm[C]. Cambridge (MA): Proceeding 5th International Conference on Computer vision.1995, 1064-1070.
[45] Maybank S, Faugeras O. A theory self-calibration of a moving camera[J]. International Journal of Computer Vision.1992, 8(2):123-151.
[46] Triggs. Autocalibration and the absolute quadric[C]. In Proceeding International Conference on Pattern Recognition. 1997, 609-614.
[47] Pollefeys M, Van Gool L, Oosterlinck M. The modulus constraint: A new constraint for self-calibration[C]. In Proceeding International Conference on Pattern Recognition. 1996, 349-353.
[48] Heyden, Astrom K. Euclidean reconstruction from image sequence with varying and unknown focal length and principal point[C]. In Proceeding International Conference on Pattern Recognition. 1997, 438-443.
[49] Pollefeys M, Koch R, Van G L. Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters[C]. Proceeding of the 5th International Conference on Computer vision.1998, 90-95.
[50] Hartley. Self- calibration of stationary cameras[J]. International Journal of Computer Vision.1997, 22(1):5-23.
[51] Ma S D. A self-calibration technique for active vision system[J]. IEEE Trans on robot automation. 1996, 12(1):114-120.
[52] Moons T, Gool L, Proesmans M, Pauwels E. Affine reconstruction from perspective image pairs with a relative object-camera translation in between[J]. IEEE Trans. PAMI. 1996, 18(1):77-83.
[53] Faugeras Q, Quan L, Sturm P. Self-calibration of 1D projective camera and its application to the self-calibration of 2D projective camera[C]. Proceeding of the ECCV98. Germany: Freiburg, 1998, 36-52.
[2]江虹,郭树旭.基于机器视觉的车灯定位误差检测方法研究.气象水文海洋仪器2005年Z1期.
[3]章炜.机器视觉技术发展及其工业应用.红外,2006,27(2):31-34.
[4]邓进军,苑伟政,李晓莹.微机械器件形状与尺寸的图像测量研究[J].机械工程学报,2002,38(增刊):101-103.
[5] Kai W, Dieter R, Schafer. An Approach to Computer-aided Quality Control Based on 3D Coordinate Metrology[J]. Journal of Materials Processing Technology,2000,107: 96-110.
[6]刘盛,刘家豪,傅建中.面向MEMS构件在线检测和三维外型测量的立体视觉系统[J].中国机械工程,2003,14(22):1936-1938.
[7]孔明,王式民.共轴法立体视觉三维测量的研究[J].计量学报,2004,25(4):294-297.
[8] Sano T,Yamamoto H. Study of Micromanipulation Using Stereoscopic Microscope[J]. IEEE Transactions on Instrumentation and Measurement,2002, 51(2): 182-187.
[9] Christian Rembe ,Richard S. Muller.Measurement System for Full Three-Dimensional Motion Characterization of MEMS[J]. Journal of Microelectromechanical System,2002, 11(5): 367-377.
[10]刘岩.基于白光显微干涉术的微轮廓检测与三维重建[D].南昌:南昌航空大学,2007:1-5.
[11] Feng Y L, Li D C, Jin C Y. A MEMS Testing System Using Computer Microvision[J]. Micronanoelectronic Technology,2003, (7): 221-223.
[12] Guo T, Hu X D, Li D C. Application of Microscopic Interferometry in Dynamic Testing System of MEMS[J]. Micronanoelectronic Technology,2003, (7): 218-220.
[13]郝颖明,朱枫.目标位姿测量中的三维视觉方法[J].中国图像图形学报, 2002, 7(12):1247-1251.
[14]郭术义.三维视觉测量系统研究[D].北京:北京机械工业学院,1999:32-34.
[15] Song Baoquan, Zhou Zongtan. A New Computational Model of Biological Vision for Stereopsis[J]. Spromger-Verlag Heidelberg Volume, 2004, 27(3): 30-34.
[16]刘进.基于视觉的三维建模相关技术研究[D].南京:南京航空航天大学,2002:7-8.
[17]杨世民,王春海,丁红岩.运用摄像法实现对刚体六自由度低频运动的观测[J].天津大学学报,1997,30(3):380-383.
[18] Elias N. Malamasa, Euripides G.M. Petrakisa. A survey on industrial vision systems,applications and tools[J]. Image and Vision Computing. 2003, 22 (21): 171-188.
[19]周鑫.基于模型的位姿测量研究[D].沈阳:中国科学院沈阳自动化研究所,2003:2-3.
[20]刘祥峰.基于机器视觉技术的零件尺寸检测系统的研究[D].哈尔滨:哈尔滨工业大学,2005,1-12.
[21] Dworkin S.B., Nye T.J. Image processing for machine vision measurement of hot formed parts[J]. Journal of Materials Processing Technology, 2006, 18 (174):1–6.
[22]李洪洲.视觉技术在小尺寸机械零件形位公差检测中的应用[D].长春:长春工业大学,2007,1-7.
[23] Elias N. Malamasa, Euripides G.M. Petrakisa. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing, 2003, 22 (21): 171-188.
[24]朱铮涛,黎绍发.视觉测量技术及其在现代制造业中的应用[J].现代制造工程,2004, (4):59-61.
[25] Cla′udio Rosito Jung, Christian Roberto Kelber. Lane following and lane departure using a linear-parabolic model[J]. Image and Vision Computing, 2005, 19 (23): 1192-1202.
[26]阮利峰,王庚,盛焕烨.基于标志点识别的三维位姿测量方法[J].计算机应用,2008,11(28):2857-2862.
[27]王芳.基于机器视觉的微工作台运动误差三点测量方法研究[D].河南:河南科技大学,2008,22-31.
[28] Leiyan zhang, HuiJie Zhao, Hongzhi Jiang.A Three-Dimensional Measurement Method by Combining Binocular and Monocular Vision System[J].Acta Optica Sinica,2008,28(7):1338-1342.
[29]张世杰,曹喜滨,陈闽.非合作航天器间相对位姿的单目视觉确定算法[J].南京理工大学学报,2006,30(5):564-568.
[30]秦丽娟,胡玉兰,魏英姿.基于矩形的三维物体位姿估计研究[J].计算机工程与科学, 2009,31(4):49-51.
[31]刘昶,朱枫,欧锦军.基于门形三条直线的P3L问题的闭式解[J].沈阳理工大学学报,2009,28(4):11-14.
[32]王振宁,钱东海,陈振华.基于直线段提取及其参数化的矩形重构方法研究[J].计算机工程与应用,2005,41(18):77-80.
[33]王晓剑,潘顺良,邱力为.基于双平行线特征的位姿估计解析算法[J].仪器仪表学报,2009,29(3):600-604.
[34] Vanden Heuvel, Frank A. Exterior orientation using coplanar parallel lines[C]. Proceedings of the 10th Scandinavian Conference on Image Analysis. Lappeenranta, 1997, 71-78.
[35] Abdel-Aziz Y L, Karara H M. Direct line transformation into object space coordinates inclose-range photogrammetry[C]. Urbana: Proceeding SymP Close-Range Photogrammrtry. 1971, 1-18.
[36] Wong K W. Mathematical foundation and digital analysis in close-range photogrammrtry[C]. Photogrammetric Engineering and Remote sensing.1975,44:1355-1373
[37] Karara H M. Handbook of close-range photogrammetry[M]. America Society of Photogrammetry, 1979.
[38] Faig W. Calibration of close-range photogrammetry system: Mathematical Formation[J]. Photogramm. Eng. Remote Seris. 1975, 41(12):1479-1486.
[39] Sid-Ahmed M A, Mohamed T B. Dual camera calibration for 3-D machine vision metrology[J]. IEEE Transactions on Instrumentation and Measurement, 1990, 39(3):512-516.
[40] Song Baoquan, Zhou Zongtan. A New Computational Model of Biological Vision for Stereopsis[J]. Spromger-Verlag Heidelberg Volume, 2004, 27(3): 30-34.
[41] Jonsson K., Kittler J. Support vector machines for face authentication[J]. Image and Vision Computing.2002, 9 (20):369-375.
[42] Wei G, Ma S. Implicit and explicit camera calibration: Theory and Experiments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1994, 16(5):469-480.
[43] Zhang Z. A flexible new technique for camera calibration[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000, 22(11):1330-1334.
[44] Hartley R I. In defence of the 8-points algorithm[C]. Cambridge (MA): Proceeding 5th International Conference on Computer vision.1995, 1064-1070.
[45] Maybank S, Faugeras O. A theory self-calibration of a moving camera[J]. International Journal of Computer Vision.1992, 8(2):123-151.
[46] Triggs. Autocalibration and the absolute quadric[C]. In Proceeding International Conference on Pattern Recognition. 1997, 609-614.
[47] Pollefeys M, Van Gool L, Oosterlinck M. The modulus constraint: A new constraint for self-calibration[C]. In Proceeding International Conference on Pattern Recognition. 1996, 349-353.
[48] Heyden, Astrom K. Euclidean reconstruction from image sequence with varying and unknown focal length and principal point[C]. In Proceeding International Conference on Pattern Recognition. 1997, 438-443.
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