光学显微定位系统定位精度分析
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摘要
为了提高光学显微定位系统对细胞微生物识别定位的精度:一方面,必须改进手眼标定方法;另一方面,需要提高全局图像识别的准确性,因此,提出一种两步法对系统进行手眼标定。首先,通过标定固定靶标来确定系统原点,并得到视觉模块相对于系统原点的转换关系;然后,根据每次拍照的起始点位置、拍照的数量和移动的步长求解出全局图像相对于系统原点的转换关系;最后,为了进一步提高全局转换关系的准确度,提出一种基于傅里叶变换的误差矫正方法,利用傅里叶变换求解出视觉模块在移动过程中的误差,并加入系统进行补偿。实验结果表明,误差补偿之后,系统X轴方向的误差均值从10.23μm降为-0.002μm,Y轴方向的误差均值从6.9μm降为-0.50μm,显微定位系统的平均定位精度达到了99%以上。结果表明,所提方法可很好地用于光学显微定位系统对细胞微生物进行高精度的自动化抓取。
In order to improve the accuracy of identification and localization of cell microorganisms by optical micropositioning system, on the one hand, the hand-eye calibration method should be optimized, on the other hand, the accuracy of global image recognition should be improved. Aiming at those, a two-step method for hand-eye calibration of the system was proposed. Firstly, the origin of the system was determined by calibrating the fixed target, and the transformation relationship of the vision module to the origin of the system was obtained. Then, according to the starting point position of each photograph, the number of photoing and the step size of movement, the transformation relationship of the global image to the origin of the system was solved. Finally, in order to further improve the accuracy of the global transformation relationship, an error correction method based on Fourier transform was used to obtain the error of the visual module in movement,then the error was added into the system for compensation. Experimental results show that after error compensation, the micropositioning system has the error mean value in X-axis direction reduced from 10.23 μm to-0.002 μm, the error mean value in Y-axis direction reduced from 6.9 μm to-0.50 μm, and the average positioning accuracy over 99%. The results show that the proposed method can be applied to the optical micropositioning system for high-precision automated capture of cell microorganisms.
In order to improve the accuracy of identification and localization of cell microorganisms by optical micropositioning system, on the one hand, the hand-eye calibration method should be optimized, on the other hand, the accuracy of global image recognition should be improved. Aiming at those, a two-step method for hand-eye calibration of the system was proposed. Firstly, the origin of the system was determined by calibrating the fixed target, and the transformation relationship of the vision module to the origin of the system was obtained. Then, according to the starting point position of each photograph, the number of photoing and the step size of movement, the transformation relationship of the global image to the origin of the system was solved. Finally, in order to further improve the accuracy of the global transformation relationship, an error correction method based on Fourier transform was used to obtain the error of the visual module in movement,then the error was added into the system for compensation. Experimental results show that after error compensation, the micropositioning system has the error mean value in X-axis direction reduced from 10.23 μm to-0.002 μm, the error mean value in Y-axis direction reduced from 6.9 μm to-0.50 μm, and the average positioning accuracy over 99%. The results show that the proposed method can be applied to the optical micropositioning system for high-precision automated capture of cell microorganisms.
引文
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[2]ZOU X,ZOU H,LU J.Virtual manipulator-based binocular stereo vision positioning system and errors modelling[J].Machine Vision and Applications,2012,23(1):43-63.
[3]ZOU X,YE M,LUO C,et al.Fault-tolerant design of a limited universal fruit-picking end-effector based on vision-positioning error[J].Applied Engineering in Agriculture,2016,32(1):5-18.
[4]KELLY R,MARQUEZ A.Robust asymptotically stable visual servoing of planar robots[J].IEEE Transactions on Robotics and Automation,1996,12(5):759-766.
[5]田浩辰,张银龙,赵海文,等.机械手视觉系统的非均匀标定法研究[J].制造业自动化,2016,38(5):41-47.(TIAN H C,ZHANG Y L,ZHAO Y W,et al.Non-uniform calibration method research of loading manipulator system based on vision positioning[J].Manufacturing Automation,2016,38(5):41-47.)
[6]刘苏宜,王国荣,石永华.激光视觉机器人焊接中摄像机和手眼的同时标定[J].华南理工大学学报(自然科学版),2008,36(2):74-77,82.(LIU S Y,WANG G R,SHI Y H.Simultaneous calibration of camera and hand-eye in robot welding with laser vision[J].Journal of South China University of Technology(Natural Science Edition),2008,36(2):74-77,82.)
[7]胡小平,左富勇,谢珂.微装配机器人手眼标定方法研究[J].仪器仪表学报,2012,33(7):1521-1526.(HU X P,ZUO F Y,XIE K.Research on hand-eye calibration method for micro-assembly robot[J].Chinese Journal of Scientific Instrument,2012,33(7):1521-1526.)
[8]魏振忠,张博,张广军.双机器人系统的快速手眼标定方法[J].光学精密工程,2011,19(8):1895-1902.(WEI Z Z,ZHANGB,ZHANG G J.Rapid hand-eye calibration of dual robot system[J].Optics and Precision Engineering,2011,19(8):1895-1902.)
[9]温卓漫,王延杰,邸男,等.基于合作靶标的在轨手眼标定[J].仪器仪表学报,2014,35(5):1005-1012.(WEN Z M,WANGY J,DI N,et al.On-orbit hand-eye calibration using cooperative target[J].Chinese Journal of Scientific Instrument,2014,35(5):1005-1012.)
[10]张强,曲道奎,徐方,等.基于误差分布估计的机器人手眼标定方法研究[J].计算机测量与控制,2018,26(4):246-249.(ZHANG Q,QU D K,XU F,et al.Error distribution estimation based robot hand-eye calibration[J].Computer Measurement&Control,2018,26(4):246-249.)
[11]ZHANG Z.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[12]叶溯,叶玉堂,刘娟秀,等.补强片自动贴片系统高精度手眼标定方法[J].应用光学,2015,36(1):71-76.(YE S,YE YT,LIU J X,et al.High-precision hand-eye calibration method for automatic stiffness bonder[J].Journal of Applied Optics,2015,36(1):71-76.)
[13]郭永刚,葛庆平,姜长胜.基于傅里叶变换的红外热波图像拼接[J].计算机应用研究,2007,24(1):227-228.(GUO Y G,GE Q P,JIANG C S.FFT-based image mosaicing of infrared thermal wave images[J].Application Research of Computers,2007,24(1):227-228.)