标定技术研究及其在机器视觉测控中的应用
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摘要
伴随着现代工业科技的快速发展,工业对测量精度要求越来越高,视觉测量技术在工业生产中有重要地位。它具有速度快、精度高、非接触、自动化程度高等优势,该技术近年来在非接触测量领域发挥着巨大作用。视觉测量技术不仅包含机器视觉的一般内容,如视觉感知、图像处理、图像分析和模式识别等,也包含着测量领域的特殊性,如空间几何尺寸的精确检测、定位和识别等。
摄像机标定技术源于20世纪中叶,经过几十年发展,该技术已经很成熟,从传统的标定技术发展到自标定技术,在制造业、电子信息、印刷行业、钢铁冶金、国防等行业应用。随着自动化水平的快速发展,摄像机标定技术在国内市场有较大发展潜力,本文研究重点是摄像机标定技术在机器视觉测量中应用。
本课题来源于产学研合作项目“嵌入式机器视觉控制器的研究与开发”(合同号:07398),基于嵌入式机器视觉平台进行研究,主要用于工业视觉测量领域。结合工程思想,本课题提出一种基于单幅图像二维平面标定方法。该方法首先进行光路调整,主要分为两个步骤:光强调整,能有效防止X靶标边缘信息丢失,而导致X靶标亚像素角点检测的不准确;光轴与物面垂直度调整,有效防止摄像机CCD平面与被测物面之间存在夹角引起测量误差。本方法采用了改进的Harris算子提取像素级角点,有效降低算法的运算量;再利用空间矩方法进行X靶标亚像素角点的提取,精度比较高,亚像素精度能达到0.1像素。该标定方法运算量小,精度高,稳定性好,已经在嵌入式机器视觉工业现场进行在线测量,测量精度能够达到0.05mm。
基于单幅图像的标定方法存在不足之处——要求测量平面与标定平面必须在同一个平面,本课题提出基于多个特征点的标定方法。该方法不需要辅助设施——X靶标,虽然需要从不同角度连续拍摄多幅图片,但操作较为方便,成本低,能够解决不同平面以及摄像机与被测量平面之间存在一定夹角情况下的标定,在工业测量行业使用更为广泛。后续将针对三维测量进一步研究,利用结构光作为辅助设施,进行工业生产的三维测量与三维重建,具有较大发展空间。
With the rapid development of modern industrial technology, vision measurement technology plays an increasingly important role in industry. It has lost of advantages, such as the fast speed, high precision, non-contact, high automation. In recent years, it plays an increasing role in the field of non-contact measurement. Vision measurement includes not only the general content in the machine vision, such as visual perception, image processing, image analysis and pattern recognition and so on, but also has the particularity of measurement, such as the precise detection of space geometry, location and identification.
Camera calibration originated from the mid-20th century, and after decades of development, it has been more and more mature, developed from traditional calibration technique to self-calibration technique. Calibration can been applied in the manufacturing, electronic information, the printing industry, iron and steel metallurgy, national defense and so on. With the rapid development of automation, calibration has great development potential. The paper focused on calibration in machine vision applications.
This topic came from the research cooperation project "Research and development of embedded machine vision controller" (contract number: 07398). With engineering thinking, this paper presented a new calibration method based on a single image. First, the calibration needed the optical path adjustment. It was divided into two steps: light intensity adjustment system can effectively prevent the edge of the target information losing, which can lead to sub-pixel corner detection inaccurate. Verticality adjusting system between optical axis and object surface can effectively prevent the angle between the CCD plane and the object surface, which lead to measurement error. Corners detections of this calibration used a modified Harris operator extracted pixel corners. It effectively reduced the algorithm computation. Then it used the space moment method to sub-pixel corner extraction. It had high accuracy and the accuracy of the sub-pixel corner can reach 0.1 pixels. Calibration technique had high precision, good stability, low computation. It has been applied in the embedded machine vision industry. Measurement accuracy can reach 0.05mm.
The calibration based a single image had a defect that the measuring plane must be in the same flat with the calibration plane. So the topic proposed a new calibration based on a number of feature points. The plan did not require auxiliary facilities-X target. Although it needed to capture multiple images, it had easy operation and lower costs. In future, I need to further research three-dimensional measurements. We will use structured light as auxiliary facilities to three-dimensional measurement and three-dimensional reconstruction.
摄像机标定技术源于20世纪中叶,经过几十年发展,该技术已经很成熟,从传统的标定技术发展到自标定技术,在制造业、电子信息、印刷行业、钢铁冶金、国防等行业应用。随着自动化水平的快速发展,摄像机标定技术在国内市场有较大发展潜力,本文研究重点是摄像机标定技术在机器视觉测量中应用。
本课题来源于产学研合作项目“嵌入式机器视觉控制器的研究与开发”(合同号:07398),基于嵌入式机器视觉平台进行研究,主要用于工业视觉测量领域。结合工程思想,本课题提出一种基于单幅图像二维平面标定方法。该方法首先进行光路调整,主要分为两个步骤:光强调整,能有效防止X靶标边缘信息丢失,而导致X靶标亚像素角点检测的不准确;光轴与物面垂直度调整,有效防止摄像机CCD平面与被测物面之间存在夹角引起测量误差。本方法采用了改进的Harris算子提取像素级角点,有效降低算法的运算量;再利用空间矩方法进行X靶标亚像素角点的提取,精度比较高,亚像素精度能达到0.1像素。该标定方法运算量小,精度高,稳定性好,已经在嵌入式机器视觉工业现场进行在线测量,测量精度能够达到0.05mm。
基于单幅图像的标定方法存在不足之处——要求测量平面与标定平面必须在同一个平面,本课题提出基于多个特征点的标定方法。该方法不需要辅助设施——X靶标,虽然需要从不同角度连续拍摄多幅图片,但操作较为方便,成本低,能够解决不同平面以及摄像机与被测量平面之间存在一定夹角情况下的标定,在工业测量行业使用更为广泛。后续将针对三维测量进一步研究,利用结构光作为辅助设施,进行工业生产的三维测量与三维重建,具有较大发展空间。
With the rapid development of modern industrial technology, vision measurement technology plays an increasingly important role in industry. It has lost of advantages, such as the fast speed, high precision, non-contact, high automation. In recent years, it plays an increasing role in the field of non-contact measurement. Vision measurement includes not only the general content in the machine vision, such as visual perception, image processing, image analysis and pattern recognition and so on, but also has the particularity of measurement, such as the precise detection of space geometry, location and identification.
Camera calibration originated from the mid-20th century, and after decades of development, it has been more and more mature, developed from traditional calibration technique to self-calibration technique. Calibration can been applied in the manufacturing, electronic information, the printing industry, iron and steel metallurgy, national defense and so on. With the rapid development of automation, calibration has great development potential. The paper focused on calibration in machine vision applications.
This topic came from the research cooperation project "Research and development of embedded machine vision controller" (contract number: 07398). With engineering thinking, this paper presented a new calibration method based on a single image. First, the calibration needed the optical path adjustment. It was divided into two steps: light intensity adjustment system can effectively prevent the edge of the target information losing, which can lead to sub-pixel corner detection inaccurate. Verticality adjusting system between optical axis and object surface can effectively prevent the angle between the CCD plane and the object surface, which lead to measurement error. Corners detections of this calibration used a modified Harris operator extracted pixel corners. It effectively reduced the algorithm computation. Then it used the space moment method to sub-pixel corner extraction. It had high accuracy and the accuracy of the sub-pixel corner can reach 0.1 pixels. Calibration technique had high precision, good stability, low computation. It has been applied in the embedded machine vision industry. Measurement accuracy can reach 0.05mm.
The calibration based a single image had a defect that the measuring plane must be in the same flat with the calibration plane. So the topic proposed a new calibration based on a number of feature points. The plan did not require auxiliary facilities-X target. Although it needed to capture multiple images, it had easy operation and lower costs. In future, I need to further research three-dimensional measurements. We will use structured light as auxiliary facilities to three-dimensional measurement and three-dimensional reconstruction.
引文
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5.邱茂林,马颂德.计算机视觉中摄像机标定综述[J].自动化学报,2000.1,26(l):43-55
6. B Hallert.Notes on Calibration of Cameras and Photographs in Photogrammetry[J].In: Photogrametria,1968.9,23(5):163-178
7. Faig W.Calibration of Close-range Photogrammetry Systems:Mathematical Formulation[J]. In:Photogrammetric Engineering and Remote sensing,1975,41(12):1479-1486
8. Tsai R.A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses[J].In:IEEE Journal of Robotics and Automation,1987.8,3(4):323-344
9. Zhang Z. Flexible Camera Calibration by Viewing a Plane from Unknown Orientations[C]. In:Proceedings of the 7th International Conference on Computer Vision.Greece:Corfu,1999: 666-673
10.雷成,吴福朝,胡占义.一种新的基于主动视觉系统的摄像机自标定方法[J].计算机学报,2000.11,23(11):1l30-1139
11.朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010.5,30(5): 1298-1303
12. S D Ma.A Self-Calibration Technique for Active Vision System[J].In:IEEE Transactions on Robotics and Automation,1996.2,12(1):114-120
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15. Faugeras O,Long Quan,Strum P.Self-Calibration of a 1D Projective Camera and its Application to the Self-Calibration of a 2D Projective Camera[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,2000.10,22(10):1179-1185
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20. Heyden A,Astrom K.Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point[C].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Juan,1997.6:438-443
21. Sturm P,Tfiggs B.A Factorization based Algorithm for Multi-Image Projective Structure and Motion [C].In:Proceedings of the Fourth European Conference on Computer Vision. England:Cambridge,1996:709-720
22. Hartley R,Zisserman A著.韦穗,杨尚骏译.计算机视觉中的多视图几何[M].安徽大学出版社,2003
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25.于起峰.基于图像的精密测量与运动测量[M].科学出版社,2002
26.权铁汉,于起峰.摄影测量系统的高精度标定与修正[J].自动化学报,2000.11,26(6): 748-755
27. Harris C,Stephens M.A Combined Corner and Edge Detector[C].In:Proceedings of the Fourth Alvey Vision Conference.Manchester,1988:147-154
28.王崴,唐一平,任娟莉等.一种改进的Harris角点提取算法[J].光学精密工程,2008.10, 16(10):1995-2001.
29. O Gorman L.Sub-Pixel Precision of Straight Edged Shapes for Registration and Measurement[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,1996.7,18(7):746-751
30. Lyvers E P,Mitchell O R.Subpixel Measurements Using a Moment-Based Edge Operator[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(12): 1293-1309
31.王社阳,强文义,陈兴林等.基于空间矩的角特征提取[J].控制与决策,2005,20(3): 325-328
32. Demigny D.On Optimal Linear Filtering for Edge Detection[J].In:IEEE Transactions on Image Processing,2002.7,11(7):728-737
33.孟利波,金观昌,姚学峰.DSCM中摄像机光轴与物面不垂直引起的误差分析[J].清华大学学报(自然科学版),2006,46(11):1930-1932
34.骆新新,刘秉琦,孙东平等.关于激光测距机光轴平行性校正方法的改进[J].应用光学,2009,30(3):519-522
35. Jung Rae Ryoo,Tae-Yong Doh.Auto-Adjustment of the Objective Lens Neutral Position in Optical Disc Drives[J].In:IEEE Transactions on Consumer Electronics,2007:1463-1468
36.章毓晋.图像工程(第二版)[M].清华大学出版社,2007
37.贾云得.机器视觉[M].科学出版社,2004
38.孙军华,刘震,张广军等.基于柔性立体靶标的摄像机标定[J].光学学报,2009.12,29(12): 3433-3439
39.杨珍,孙军华,吴子彦等.基于双一维靶标的摄像机标定方法[J].光电子·激光,2010.3, 21(3):412-414
40. Donald W Marqardt.An Algorithm for Least Squares Estimation of Nonlinear Parameters[J].In:Journal of the Society for Industrial and Applied Mathematics,1963,11(2): 431-441
41. M Lourakis. A Brief Description of the Levenberg-Marquardt Algorithm Implemened by Levmar [EB/OL]-http: //www.ics.forth.gr/~lourakis/levmar
42.杨柳,陈艳萍.一种新的Levenberg-Marquardt算法的收敛性[J].计算数学,2005.2,27(1): 55-62
43.曾慧,邓小明,赵训坡等.基于线对应的单应矩阵估计及其在视觉测量中的应用[J].自动化学报,2007.5,33(5):449-455
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2.中国视觉网[J/OL].http://www.china-vision.net/
3.张广军.视觉测量[M].科学出版社,2008
4. Carsten Steger and Markus Ulrich著.杨少荣,吴迪靖,段德山译.机器视觉算法与应用[M].清华大学出版社,2009
5.邱茂林,马颂德.计算机视觉中摄像机标定综述[J].自动化学报,2000.1,26(l):43-55
6. B Hallert.Notes on Calibration of Cameras and Photographs in Photogrammetry[J].In: Photogrametria,1968.9,23(5):163-178
7. Faig W.Calibration of Close-range Photogrammetry Systems:Mathematical Formulation[J]. In:Photogrammetric Engineering and Remote sensing,1975,41(12):1479-1486
8. Tsai R.A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses[J].In:IEEE Journal of Robotics and Automation,1987.8,3(4):323-344
9. Zhang Z. Flexible Camera Calibration by Viewing a Plane from Unknown Orientations[C]. In:Proceedings of the 7th International Conference on Computer Vision.Greece:Corfu,1999: 666-673
10.雷成,吴福朝,胡占义.一种新的基于主动视觉系统的摄像机自标定方法[J].计算机学报,2000.11,23(11):1l30-1139
11.朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010.5,30(5): 1298-1303
12. S D Ma.A Self-Calibration Technique for Active Vision System[J].In:IEEE Transactions on Robotics and Automation,1996.2,12(1):114-120
13. Faugeras O D,Lustman F.Motion and Structure from Motion in a Piecewise Planar Environment[J].In:International Journal of Pattern Recognition and Artificial Intelligence, 1988,2(3):485-508
14. Luong Q T,Faugeras O D.Self-Calibration of a Camera Using Multiple Images[C].In: Proceedings of 11th IAPR International Conference on Computer Vision and Applications. The Hague,1992,1(30):9-12
15. Faugeras O,Long Quan,Strum P.Self-Calibration of a 1D Projective Camera and its Application to the Self-Calibration of a 2D Projective Camera[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,2000.10,22(10):1179-1185
16. S J Maybank,O D Faugeras.A Theory of Self-Calibration of a Moving Camera[J].In: International Journal of Computer Vision,1992.4.16,8(2):123-151
17. Hartley R I.In Defence of the Eight-Point Algorithm[C].In:Proceedings of the Fifth IEEE Inernational Conference on Computer Vision.Cambridge,Massachusetts.USA:MIT,1995.6: 1064-1070
18. Triggs B.Auto-Calibration and Absolute Quadric[C].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.USA:Los Alamitos,1997: 604-614
19. Marc Pollefeys,Reinhard Koch,Lus Van Gool.Self-Calibration and Metric Reconstruction in spite of Varying Unknown Internal Camera Parameters[C].In:Proceedings of the Sixth Inernational Conference on Computer Vision.Bombay,1998:90-95
20. Heyden A,Astrom K.Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point[C].In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Juan,1997.6:438-443
21. Sturm P,Tfiggs B.A Factorization based Algorithm for Multi-Image Projective Structure and Motion [C].In:Proceedings of the Fourth European Conference on Computer Vision. England:Cambridge,1996:709-720
22. Hartley R,Zisserman A著.韦穗,杨尚骏译.计算机视觉中的多视图几何[M].安徽大学出版社,2003
23. Weng J,Cohen P,Herniou M.Camera Calibration with Distortion Models and Accuracy Evaluation[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,1992.10, 14(10):965-980
24. Jianhua Wang,Fanhuai Shi,Jing Zhang.A New Calibration Model of Camera Lens Distortion[C].In:Proceedings of IEEE Inernational Conference on Intelligent Robots and Systems.China:Beijing,2006.10:5713-5718
25.于起峰.基于图像的精密测量与运动测量[M].科学出版社,2002
26.权铁汉,于起峰.摄影测量系统的高精度标定与修正[J].自动化学报,2000.11,26(6): 748-755
27. Harris C,Stephens M.A Combined Corner and Edge Detector[C].In:Proceedings of the Fourth Alvey Vision Conference.Manchester,1988:147-154
28.王崴,唐一平,任娟莉等.一种改进的Harris角点提取算法[J].光学精密工程,2008.10, 16(10):1995-2001.
29. O Gorman L.Sub-Pixel Precision of Straight Edged Shapes for Registration and Measurement[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,1996.7,18(7):746-751
30. Lyvers E P,Mitchell O R.Subpixel Measurements Using a Moment-Based Edge Operator[J].In:IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(12): 1293-1309
31.王社阳,强文义,陈兴林等.基于空间矩的角特征提取[J].控制与决策,2005,20(3): 325-328
32. Demigny D.On Optimal Linear Filtering for Edge Detection[J].In:IEEE Transactions on Image Processing,2002.7,11(7):728-737
33.孟利波,金观昌,姚学峰.DSCM中摄像机光轴与物面不垂直引起的误差分析[J].清华大学学报(自然科学版),2006,46(11):1930-1932
34.骆新新,刘秉琦,孙东平等.关于激光测距机光轴平行性校正方法的改进[J].应用光学,2009,30(3):519-522
35. Jung Rae Ryoo,Tae-Yong Doh.Auto-Adjustment of the Objective Lens Neutral Position in Optical Disc Drives[J].In:IEEE Transactions on Consumer Electronics,2007:1463-1468
36.章毓晋.图像工程(第二版)[M].清华大学出版社,2007
37.贾云得.机器视觉[M].科学出版社,2004
38.孙军华,刘震,张广军等.基于柔性立体靶标的摄像机标定[J].光学学报,2009.12,29(12): 3433-3439
39.杨珍,孙军华,吴子彦等.基于双一维靶标的摄像机标定方法[J].光电子·激光,2010.3, 21(3):412-414
40. Donald W Marqardt.An Algorithm for Least Squares Estimation of Nonlinear Parameters[J].In:Journal of the Society for Industrial and Applied Mathematics,1963,11(2): 431-441
41. M Lourakis. A Brief Description of the Levenberg-Marquardt Algorithm Implemened by Levmar [EB/OL]-http: //www.ics.forth.gr/~lourakis/levmar
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