结构光三维测量与点云配准的研究
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摘要
三维测量及配准技术是计算机视觉技术的一个分支,是计算机视觉和计算机图像图形处理相结合的一个研究方向,它在生产自动化、机器人视觉、文化遗产保护、虚拟现实和计算机辅助医学等领域都有着广泛的应用前景。在三维测量及配准技术中,如何寻找一种即简便又快捷的实现方式是一项重要而有难度的研究课题。
本文在深入研究结构光三维测量相关理论、方法及关键技术的基础上,设计与开发了基于格雷码结合相移技术的双目立体结构光三维测量系统。在分析总结了目前国内外通常所采用的点云数据配准方法的基础上,形成了本文中所采用的配准流程。最后,在实验室环境下利用自行搭建的硬件平台与开发的软件图像处理系统,进行了可行性方面的实验。
首先,本文阐述了双摄像机结构光法实现的物体三维重构的原理,然后建立了该方法的数学模型。其次,重点论述了系统的构成和双摄像机定标,得到了摄像机的内外参数和编码图案。再次,本文论述了三维点云重构的软件设计,根据图像处理算法,对编码图案进行图像信息的提取、滤波和二值化,利用编码图案、外极线约束和三角测量原理,获得物体三维坐标。最后,在研究经典ICP算法的基础上,利用点到点的ICP算法实现点云数据配准,得到了较好的收敛效果。
Three-Demension measurement and registration is a branch of computer vision. It is a research direction that integrates computer vision with computer image and graph processing.It has a wide application in the fields of automation process, machine vision, culture relic conservation, virtual reality, computer aided medicine and so on. In 3D measurement and registration technology, a fast and easy realization way is a big problem.
Based on deep study of relevant theories and key technologies in the dissertation, a 3D measurement system based on combining Gray Code with Phase Shifting method using double CCD structured light is developed. After analyzing and comparing the widely used point cloud data registration methods, we came to a registration pipeline. At last,hardware platform is built and software of image processing system is developed in lab environment, and we experimentalize the idea.
Firstly, the principle of double CCD strctured light 3D measurement methed is presented. Then the mathematical model using this method is established. Secondly, the structure of the system and the dual-camera calibration are introduced. The parameters of the camera and the coded pattern pictures are obtained. Thirdly,the software flow is established. The whole image processing flow chart include stripes extracting,image filtering,binary transformation.Then the 3D coordinate can be obtained using coded pattern pictures, epipolar constraint and the principle of optical triangulation. Finally, based on the study of the classical ICP algorithm, the registration of point clouds is implemented using point-to-point ICP algorithm. A good convergence is gained.
本文在深入研究结构光三维测量相关理论、方法及关键技术的基础上,设计与开发了基于格雷码结合相移技术的双目立体结构光三维测量系统。在分析总结了目前国内外通常所采用的点云数据配准方法的基础上,形成了本文中所采用的配准流程。最后,在实验室环境下利用自行搭建的硬件平台与开发的软件图像处理系统,进行了可行性方面的实验。
首先,本文阐述了双摄像机结构光法实现的物体三维重构的原理,然后建立了该方法的数学模型。其次,重点论述了系统的构成和双摄像机定标,得到了摄像机的内外参数和编码图案。再次,本文论述了三维点云重构的软件设计,根据图像处理算法,对编码图案进行图像信息的提取、滤波和二值化,利用编码图案、外极线约束和三角测量原理,获得物体三维坐标。最后,在研究经典ICP算法的基础上,利用点到点的ICP算法实现点云数据配准,得到了较好的收敛效果。
Three-Demension measurement and registration is a branch of computer vision. It is a research direction that integrates computer vision with computer image and graph processing.It has a wide application in the fields of automation process, machine vision, culture relic conservation, virtual reality, computer aided medicine and so on. In 3D measurement and registration technology, a fast and easy realization way is a big problem.
Based on deep study of relevant theories and key technologies in the dissertation, a 3D measurement system based on combining Gray Code with Phase Shifting method using double CCD structured light is developed. After analyzing and comparing the widely used point cloud data registration methods, we came to a registration pipeline. At last,hardware platform is built and software of image processing system is developed in lab environment, and we experimentalize the idea.
Firstly, the principle of double CCD strctured light 3D measurement methed is presented. Then the mathematical model using this method is established. Secondly, the structure of the system and the dual-camera calibration are introduced. The parameters of the camera and the coded pattern pictures are obtained. Thirdly,the software flow is established. The whole image processing flow chart include stripes extracting,image filtering,binary transformation.Then the 3D coordinate can be obtained using coded pattern pictures, epipolar constraint and the principle of optical triangulation. Finally, based on the study of the classical ICP algorithm, the registration of point clouds is implemented using point-to-point ICP algorithm. A good convergence is gained.
引文
[1] Y. C. Shan, et al. A new technique to extract range information from stereo images. IEEE Trans. On pattern analysis and machine intelligence. 1989. 11(7):768-773
[2] YYHung, L. Lin, H.M.Shang, B.G.Park. Practical three-dimensional computer vision techniques for full-filled surface measurement. Opt.Eng 2000.39(1):143-149
[3] Chang-Hua Hu, Yu-Wen Qin. Digital color encoding and its application to the moire technique. APPLIED OPTICS. 2003.36(16):3682-3685
[4] K. Bieruan, K. Harding. 3D imaging using a unique refractive optic design to combine moier and stereo. Proc.SPIE. 1997.3204(2):10-11
[5] Joseph C. Marron, Kurt W. Gleichman. Three-dimensional imaging using a tunable laser source. Opt.Eng. 2000.39(1):47-51
[6] T. D. Ditto, D. A. Lyon.Moly. A prototype hand-held three-dimensional digitizer with diffraction optics. Opt.Eng. 2000.39(1):10-22
[7]张广军,王红.结构光三维视觉系统研究.航空学报,1999:365-367
[8]林学闰.基于双外极线的结构光深度信息快速获取.清华大学学报,1998:96-99
[9] R.A.Jarvis. A perspective on range finding technique for computer vision. IEEE Tmns. PAMI. 1983. 5(2),p122～139
[10]马颂德,张正友计算机视觉-计算理论与算法基础.科学出版社.2003
[11] R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049. 2003.
[12]吴福朝.计算机视觉中的数学方法.科学出版社,2008
[13]李小松.摄像机标定技术的研究[J].机械工程学报,2002,38(3):149-151
[14] http://www.vision.caltech.edu/bouguetj/calib_doc.
[15] Z. Zhang. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. 22(11):1330-1334
[16] Mouaddib E B, Salvi J. Recent progress in structured light in order to solve the correspondence problem in stereo vision. IEEE International Conference on Robotics and Automation. Albuquerque, NM, USA:IEEE.1997.1:130-136
[17] J. L. Posdamer and M. D. Altschuler. Surface measurement by space-encoded projected beam systems. Computer Graphics and Image Processing. 1982. 18(1):1-17
[18] R. J. Valkenburg and A. M. McIvor. Accurate 3d measurement using a structuredlight system. linage and Vision Computing. February 1998. 16(2):99-110
[19] E. Horn and N. Kiryati. Toward optimal structured light patterns. Image and Vision Computing. February 1999. 17(2):87-97
[20] W. Krattenthaler, K. J. Mayer and H. P. Duwe. 3D-surface measurement with coded light approach. In Proceedings Oesterr. Arbeitsgem. MustererKennung. 1993.volume 12. pages 103-114
[21] Jens G-uhring. Dense 3-d surface acquisition by structured light using off-the-shelf components. Videometrics and Optical Methods for 3D Shape Measurement. 2001. 4309:220-231
[22] D. Bergmann. New approach for automatic surface reconstruction with coded light. In Proceedings of Remote Sensing and Reconstruction for Three-Dimensional Objects and Scenes. August 1995. volume 2572. pages 2-9. SPIE.
[23] O. Hall-Holt and S. Rusinkiewicz. Stripe boundary codes for real-time structured-light range scanning of moving objects. In The 8th IEEE International Conference on Computer Vision. 2001. pages 11: 359-366
[24] M. Maruyama and S. Abe. Range sensing by projecting multiple slits with random cuts. Pattern Analysis and Machine Intelligence. June 1993. 15(6):647-651
[25] N. G. Durdle, J. Thayyoor, V. J. Raso. An improved structured light technique for surface reconstruction of the human trunk. IEEE Conference on Electrical and Computer Engineering. 1998. Vol.2, pp:874-877
[26] P. Vuylsteke and A. Oosterlinck. Range image acquisition with a single binary-encoded light pattern. Pattern Analysis and Machine Intelligence. 1990. 12(2):148-163
[27] J. Salvi, J. Batlle, and E. Mouaddib. A robust-coded pattern projection for dynamic 3d scene measurement. Pattern Recognition Letters. September 1998. (19):1055-1065
[28] L. Zhang, B. Curless, and S. M. Seitz. Rapid shape acquisition using color structured light and multi-pass dynamic programming. In Int. Symposium on 3D Data Processing Visualization and Transmission, Padova, Italy, June 2002.
[29] R. A. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov. Structured light using pseudorandom codes. Pattern Analysis and Machine Intelligence. March 1998. 20(3): 322-327
[30] P. Vuylsteke and A. Oosterlinck. Range image acquisition with a singlebinary-encoded light pattern. Pattern Analysis and Machine Intelligence. 1990. 12(2): 148-163
[31] Berthold K. P. Horn. Closed-form solution of absolute orientation using unit quaternions. J.Opt.Soc.Amer.Apr.1987. vol.4,no.4. pp.629-642
[32] K. S. Arun, T. S. Huang, S. D. Blostein. Least-Squares Fitting of Two {3-D} Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence. September, 1987.9(5):698--700
[33]郑德华.三维激光扫描数据处理的理论与方法.同济大学博士学位,2005.
[34] Besl, P. and McKay, N. A Method for Registration of 3-D Shapes. Trans. PAMI. 1992. Vol. 14, No. 2.
[35] Y. Chen, G. Medioni. Object Modeling by Registration of Multiple Range Images. Image and Vision Computing. 1992.vol.10, no.3. pp.145–155
[36] A. Johnson and S. Kang. Registration and integration of textured 3-D data.Tech. Report CRL96/4, Digital Equipment Corporation. Cambridge Research Lab. 1996. R
[37] C.Schutz, T.Jost, and H.Hugli. Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration. Proceeding of International Conference on Pattern Recognition. 1998. Vol. 2.pp.982-984
[38] M.Greenspan and G.Godin. A Nearest Neighbor Method for Efficient ICP. Proceed-ings of the 3rd International Conference on 3-D Digital Imaging and Modeling. Quebec City, Canada. May 2001. pp. 161-168
[39] Z. Zhang. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision. 1994.Vol. 13. pp.119-152
[2] YYHung, L. Lin, H.M.Shang, B.G.Park. Practical three-dimensional computer vision techniques for full-filled surface measurement. Opt.Eng 2000.39(1):143-149
[3] Chang-Hua Hu, Yu-Wen Qin. Digital color encoding and its application to the moire technique. APPLIED OPTICS. 2003.36(16):3682-3685
[4] K. Bieruan, K. Harding. 3D imaging using a unique refractive optic design to combine moier and stereo. Proc.SPIE. 1997.3204(2):10-11
[5] Joseph C. Marron, Kurt W. Gleichman. Three-dimensional imaging using a tunable laser source. Opt.Eng. 2000.39(1):47-51
[6] T. D. Ditto, D. A. Lyon.Moly. A prototype hand-held three-dimensional digitizer with diffraction optics. Opt.Eng. 2000.39(1):10-22
[7]张广军,王红.结构光三维视觉系统研究.航空学报,1999:365-367
[8]林学闰.基于双外极线的结构光深度信息快速获取.清华大学学报,1998:96-99
[9] R.A.Jarvis. A perspective on range finding technique for computer vision. IEEE Tmns. PAMI. 1983. 5(2),p122～139
[10]马颂德,张正友计算机视觉-计算理论与算法基础.科学出版社.2003
[11] R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049. 2003.
[12]吴福朝.计算机视觉中的数学方法.科学出版社,2008
[13]李小松.摄像机标定技术的研究[J].机械工程学报,2002,38(3):149-151
[14] http://www.vision.caltech.edu/bouguetj/calib_doc.
[15] Z. Zhang. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. 22(11):1330-1334
[16] Mouaddib E B, Salvi J. Recent progress in structured light in order to solve the correspondence problem in stereo vision. IEEE International Conference on Robotics and Automation. Albuquerque, NM, USA:IEEE.1997.1:130-136
[17] J. L. Posdamer and M. D. Altschuler. Surface measurement by space-encoded projected beam systems. Computer Graphics and Image Processing. 1982. 18(1):1-17
[18] R. J. Valkenburg and A. M. McIvor. Accurate 3d measurement using a structuredlight system. linage and Vision Computing. February 1998. 16(2):99-110
[19] E. Horn and N. Kiryati. Toward optimal structured light patterns. Image and Vision Computing. February 1999. 17(2):87-97
[20] W. Krattenthaler, K. J. Mayer and H. P. Duwe. 3D-surface measurement with coded light approach. In Proceedings Oesterr. Arbeitsgem. MustererKennung. 1993.volume 12. pages 103-114
[21] Jens G-uhring. Dense 3-d surface acquisition by structured light using off-the-shelf components. Videometrics and Optical Methods for 3D Shape Measurement. 2001. 4309:220-231
[22] D. Bergmann. New approach for automatic surface reconstruction with coded light. In Proceedings of Remote Sensing and Reconstruction for Three-Dimensional Objects and Scenes. August 1995. volume 2572. pages 2-9. SPIE.
[23] O. Hall-Holt and S. Rusinkiewicz. Stripe boundary codes for real-time structured-light range scanning of moving objects. In The 8th IEEE International Conference on Computer Vision. 2001. pages 11: 359-366
[24] M. Maruyama and S. Abe. Range sensing by projecting multiple slits with random cuts. Pattern Analysis and Machine Intelligence. June 1993. 15(6):647-651
[25] N. G. Durdle, J. Thayyoor, V. J. Raso. An improved structured light technique for surface reconstruction of the human trunk. IEEE Conference on Electrical and Computer Engineering. 1998. Vol.2, pp:874-877
[26] P. Vuylsteke and A. Oosterlinck. Range image acquisition with a single binary-encoded light pattern. Pattern Analysis and Machine Intelligence. 1990. 12(2):148-163
[27] J. Salvi, J. Batlle, and E. Mouaddib. A robust-coded pattern projection for dynamic 3d scene measurement. Pattern Recognition Letters. September 1998. (19):1055-1065
[28] L. Zhang, B. Curless, and S. M. Seitz. Rapid shape acquisition using color structured light and multi-pass dynamic programming. In Int. Symposium on 3D Data Processing Visualization and Transmission, Padova, Italy, June 2002.
[29] R. A. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov. Structured light using pseudorandom codes. Pattern Analysis and Machine Intelligence. March 1998. 20(3): 322-327
[30] P. Vuylsteke and A. Oosterlinck. Range image acquisition with a singlebinary-encoded light pattern. Pattern Analysis and Machine Intelligence. 1990. 12(2): 148-163
[31] Berthold K. P. Horn. Closed-form solution of absolute orientation using unit quaternions. J.Opt.Soc.Amer.Apr.1987. vol.4,no.4. pp.629-642
[32] K. S. Arun, T. S. Huang, S. D. Blostein. Least-Squares Fitting of Two {3-D} Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence. September, 1987.9(5):698--700
[33]郑德华.三维激光扫描数据处理的理论与方法.同济大学博士学位,2005.
[34] Besl, P. and McKay, N. A Method for Registration of 3-D Shapes. Trans. PAMI. 1992. Vol. 14, No. 2.
[35] Y. Chen, G. Medioni. Object Modeling by Registration of Multiple Range Images. Image and Vision Computing. 1992.vol.10, no.3. pp.145–155
[36] A. Johnson and S. Kang. Registration and integration of textured 3-D data.Tech. Report CRL96/4, Digital Equipment Corporation. Cambridge Research Lab. 1996. R
[37] C.Schutz, T.Jost, and H.Hugli. Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration. Proceeding of International Conference on Pattern Recognition. 1998. Vol. 2.pp.982-984
[38] M.Greenspan and G.Godin. A Nearest Neighbor Method for Efficient ICP. Proceed-ings of the 3rd International Conference on 3-D Digital Imaging and Modeling. Quebec City, Canada. May 2001. pp. 161-168
[39] Z. Zhang. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision. 1994.Vol. 13. pp.119-152