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Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching

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Abstract

In order to improve the low positioning accuracy and execution efficiency of the robot binocular vision, a binocular vision positioning method based on coarse-fine stereo matching is proposed to achieve object positioning. The random fern is used in the coarse matching to identify objects in the left and right images, and the pixel coordinates of the object center points in the two images are calculated to complete the center matching. In the fine matching, the right center point is viewed as an estimated value to set the search range of the right image, in which the region matching is implemented to find the best matched point of the left center point. Then, the similar triangle principle of the binocular vision model is used to calculate the 3D coordinates of the center point, achieving fast and accurate object positioning. Finally, the proposed method is applied to the object scene images and the robotic arm grasping platform. The experimental results show that the average absolute positioning error and average relative positioning error of the proposed method are 8.22 mm and 1.96% respectively when the object's depth distance is within 600 mm, the time consumption is less than 1.029 s. The method can meet the needs of the robot grasping system, and has better accuracy and robustness.

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References

  1. B. Moore, E. Oztop. Robotic grasping and manipulation through human visuomotor learning. Robotics and Autonomous Systems, vol. 60, no. 3, pp. 441–451, 2012. DOI: 10.1016/j.robot.2011.09.002.

    Article  Google Scholar 

  2. L. Y. Xu, Z. Q. Cao, P. Zhao, C. Zhou. A new monocular vision measurement method to estimate 3D positions of objects on floor. International Journal of Automation and Computing, vol. 14, no. 2, pp. 159–168, 2017. DOI: 10.1007/ s11633-016-1047-6.

    Article  Google Scholar 

  3. H. Li, Y. L. Chen, T. H. Chang, X. Y. Wu, Y. S. Ou, Y. S. Xu. Binocular vision positioning for robot grasping. In Proceedings of 2011 IEEE International Conference on Robotics and Biomimetics, IEEE, Karon Beach, Thailand, pp. 1522–1527, 2011. DOI: 10.1109/ROBIO.2011.6181505.

    Chapter  Google Scholar 

  4. H. Shi, H. Zhu. Stereo matching based on adaptive matching windows and multi-feature fusion. Pattern Recognition and Artificial Intelligence, vol. 29, no. 3, pp. 193–202, 2016. DOI: 10.16451/j.cnki.issn1003-6059.201603001. (in Chinese)

    Google Scholar 

  5. J. Liu, J. X. Zhang, Y. Dai. Dense stereo matching based on region growing. Robot, vol. 39, no. 2, pp. 182–188, 2017. DOI: 10.13973/j.cnki.robot.2017.0182. (in Chinese)

    Google Scholar 

  6. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. DOI: 10.1023/B:VISI. 0000029664.99615.94.

    Article  Google Scholar 

  7. L. R. Zhao, W. Zhu, Y. G. Cao, Y. H. Liu, J. X. Sun. Application of improved SURF algorithm to feature matching. Optics and Precision Engineering, vol. 21, no. 12, pp. 3263–3271, 2013. DOI: 10.3788/OPE.20132112.3263. (in Chinese)

    Article  Google Scholar 

  8. R. J. Zhu, Y. H. Zhu, L. Wang, W. Lu, H. Luo, Z. C. Zhang. Cotton positioning technique based on binocular vision with implementation of scale-invariant feature transform algorithm. Transactions of the Chinese Society of Agricultural Engineering, vol. 32, no. 6, pp. 182–188, 2016. DOI: 10.11975/j.issn.1002-6819.2016.06.025. (in Chinese)

    Google Scholar 

  9. M. Ozuysal, M. Calonder, V. Lepetit, P. Fua. FAST key-point recognition using random ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 448–461, 2010. DOI: 10.1109/TPAMI.2009.23.

    Article  Google Scholar 

  10. Y. Luo, J. Xiang, M. J. Yan, J. H. Hou. Online target tracking based on mulitiple instance learning and random ferns detection. Journal of Electronics & Information Technology, vol. 36, no. 7, pp. 1605–1611, 2014. DOI: 10.3724/SP.J.1146.2013.01358. (in Chinese)

    Google Scholar 

  11. Y. Zhao, J. J. Li, H. P. Li, D. Yang. Real-time tracking and registration algorithm of scenarios of augmented reality based on improved random fern. Journal of Northeastern University (Natural Science), vol. 37, no. 5, pp. 614-618, 2016. DOI: 10.3969/j.issn.1005-3026.2016.05. 002. (in Chinese)

  12. J. M. Liu, Y. Liang, H. X. Sun, Y. Duan, X. Liu. Real-time face tracking based on detecting and tracking. Journal of Image and Graphics, vol. 20, no. 11, pp. 1473–1481, 2015. DOI: 10.11834/jig.20151106. (in Chinese)

    Google Scholar 

  13. Y. Luo, Y. Fu, Y. Zhang. A monocular-vision real-time matching algorithm based on FAST corners and affine-im-proved random ferns. Robot, vol. 36, no. 3, pp. 271–278, 2014. DOI: 10.3724/SP.J.1218.2014.00271. (in Chinese)

    Google Scholar 

  14. F. Zheng, G. I. Webb. A comparative study of semi-naive Bayes methods in classification learning. In Proceedings of the Fourth Australasian Data Mining Conference, University of Technology, Sydney, Australia, pp. 141–156, 2005.

    Google Scholar 

  15. E. Rosten, T. Drummond. Machine learning for high-speed corner detection. In Proceedings of the 9th European Conference on Computer Vision, Springer, Graz, Australia, pp. 430–443, 2006. DOI: 10.1007/1174402334.

    Google Scholar 

  16. M. Hu, X. J. He, X. H. Wang. Fast image matching algorithm with area centroid. Journal of Electronic Measurement and Instrument, vol. 25, no. 5, pp. 455–462, 2011. DOI: 10.3724/SP.J.1187.2011.00455. (in Chinese)

    Article  Google Scholar 

  17. Y. Yang, F. Qiu, H. Li, L. Zhang, M. L. Wang, M. Y. Fu. Large-scale 3D semantic mapping using stereo vision. International Journal of Automation and Computing, vol. 15, no. 2, pp. 194–206, 2018. DOI: 10.1007/s11633-018-1118-y.

    Article  Google Scholar 

  18. H. B. Zhang, S. R. Liu, B. T. Zhang. Binocular vision position algorithm using hue-saturation histogram back-project combined with feature point extraction. Control Theory & Applications, vol. 31, no. 5, pp. 614–623, 2014. DOI: 10.7641/CTA.2014.30679. (in Chinese)

    MATH  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61125101)

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Authors and Affiliations

  1. Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou, 730000, China

    Wei-Ping Ma, Wen-Xin Li & Peng-Xia Cao

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  1. Wei-Ping Ma
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  2. Wen-Xin Li
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  3. Peng-Xia Cao
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Correspondence to Wei-Ping Ma.

Additional information

Wei-Ping Ma received the B. Eng. degree in electronic information science and technology from Xi'an University of Science and technology, China in 2011, and M. Eng. degree in communication and information system from Xi'an University of Science and technology, China in 2015. Currently, she is a Ph. D. degree candidate in space electronics at Lanzhou Institute of Physics, China Academy of Space Technology (CAST).

Her research interests include space electronic technology, computer vision and intelligent robotics.

Wen-Xin Li received the M. Eng. degree in applied mathematics from Northwestern Polytechnical University, China in 1993, and Ph. D. degree in automatic control from Northwestern Polytechnical University, China in 2011. Currently, he is a researcher at Lanzhou Institute of Physics, CAST.

His research interests include space electronic technology, software reuse technology, system simulation and reconstruction technology.

Peng-Xia Cao received the B. Eng. degree in communication engineering from Hunan International Economics University, China in 2011, and M. Eng. degree in circuits and systems from Hunan Normal University, China in 2015. Currently, she is a Ph. D. degree candidate in space electronics at Lanzhou Institute of Physics, CAST.

Her research interests include space electronic technology, computer vision and augmented reality.

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Ma, WP., Li, WX. & Cao, PX. Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching. Int. J. Autom. Comput. 17, 562–571 (2020). https://doi.org/10.1007/s11633-020-1226-3

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