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Orientation Measurement for Objects with Planar Surface Based on Monocular Microscopic Vision

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Abstract

Orientation measurement of objects is vital in micro assembly. In this paper, we present a novel method based on monocular microscopic vision for 3-D orientation measurement of objects with planar surfaces. The proposed methods aim to measure the orientation of the object, which does not require calibrating the intrinsic parameters of microscopic camera. In our methods, the orientation of the object is firstly measured with analytical computation based on feature points. The results of the analytical computation are coarse because the information about feature points is not fully used. In order to improve the precision, the orientation measurement is converted into an optimization process base on the relationship between deviations in image space and in Cartesian space under microscopic vision. The results of the analytical computation are used as the initial values of the optimization process. The optimized variables are the three rotational angles of the object and the pixel equivalent coefficient. The objective of the optimization process is to minimize the coordinates differences of the feature points on the object. The precision of the orientation measurement is boosted effectively. Experimental and comparative results validate the effectiveness of the proposed methods.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61733004 and 61873266).

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Authors and Affiliations

  1. Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ying Li, Xi-Long Liu, De Xu & Da-Peng Zhang

  2. School of Computer and Control Engineering, University of Chinses Academy of Sciences, Beijing, 101408, China

    Ying Li, Xi-Long Liu, De Xu & Da-Peng Zhang

Authors
  1. Ying Li
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  2. Xi-Long Liu
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  3. De Xu
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  4. Da-Peng Zhang
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Corresponding author

Correspondence to De Xu.

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Recommended by Associate Editor Jangmyung Lee

Ying Li received the B. Sc. degree in automation from North China Electric Power University (Baoding), China in 2016. He is a Ph. D. degree candidate in control science and engineering at the Institute of Automation, Chinese Academy of Sciences (IACAS), China.

His research interests include visual measurement, visual control, micro-assembly and machine learning.

Xi-Long Liu received the B. Sc. degree in electrical engineering and automation from Beijing Jiaotong University, China in 2009, and the Ph. D. degree in control science and engineering from Institute of Automation, Chinese Academy of Sciences (IACAS), China in 2014. He is an associate professor at the Institute of Automation, Chinese Academy of Sciences (IACAS), China.

His research interests include image processing, visual measurement and service robot.

De Xu received the B. Sc. and M. Sc. degrees in control science and engineering from Shandong University of Technology, China in 1985 and 1990, respectively, and the Ph. D. degree in control science and engineering from Zhejiang University, China in 2001. He is a professor at the Institute of Automation, Chinese Academy of Sciences (IACAS), China.

His research interests include visual measurement, visual control, intelligent control, visual positioning, microscopic vision, and micro-assembly.

Da-Peng Zhang received the B. Sc. and M. Sc. degrees in mechatronic engineering from Hebei University of Technology, China in 2003 and 2006, respectively, and the Ph. D. degree in mechatronic engineering from the Beijing University of Aeronautics and Astronautics, China in 2011. He is an associate professor at the Institute of Automation, Chinese Academy of Sciences (IACAS), China.

His research interests include visual control, micro-assembly and medical robot.

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Li, Y., Liu, XL., Xu, D. et al. Orientation Measurement for Objects with Planar Surface Based on Monocular Microscopic Vision. Int. J. Autom. Comput. 17, 247–256 (2020). https://doi.org/10.1007/s11633-019-1202-y

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  • DOI: https://doi.org/10.1007/s11633-019-1202-y

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