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Method for visual localization of oil and gas wellhead based on distance function of projected features

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Abstract

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead (OGWH). Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.

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Acknowledgement

The authors would like to thank Gang Rao and Dan Huang of Tsinghua University for their help in the real image experiment.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Ying Xie, Xiang-Dong Yang, Zhi Liu, Shu-Nan Ren & Ken Chen

  2. Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Beijing, 100084, China

    Ying Xie, Xiang-Dong Yang, Zhi Liu, Shu-Nan Ren & Ken Chen

  3. State Key Laboratory of Tribology, Beijing, 100084, China

    Ying Xie, Xiang-Dong Yang, Zhi Liu, Shu-Nan Ren & Ken Chen

Authors
  1. Ying Xie
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  2. Xiang-Dong Yang
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  3. Zhi Liu
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  4. Shu-Nan Ren
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  5. Ken Chen
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Corresponding author

Correspondence to Xiang-Dong Yang.

Additional information

This work was supported by National Natural Science Foundation of China (No. 61403226), and the State Key Laboratory of Tribology of China (No. SKLT09A03).

Recommended by Associate Editor De Xu

Ying Xie received the B. Sc. degree in mechanical engineering from Shanghai Jiao Tong University, China in 2010. He is currently a Ph. D. degree candidate in the Department of Mechanical Engineering, Tsinghua University, China.

His research interests include robot vision, robot localization, and robot calibration.

ORCID iD: 0000-0002-2812-9311

Xiang-Dong Yang received the B. Sc., M. Sc. and Ph.D. degree degrees in mechanical engineering from Tsinghua University, China in 1989, 1990 and 1995, respectively. Currently, he is an associate professor in the Department of Mechanical Engineering at Tsinghua University, China. He is a senior member of Chinese Mechanical Engineering Society (CMES).

His research interests include robotics and automatic control system.

ORCID iD: 0000-0002-9976-8591

Zhi Liu received the B. Sc. degree in mechanical engineering from Central South University, China in 2010. He is currently a Ph.D. degree candidate in mechanical engineering of Department of Mechanical Engineering, Tsinghua University, China.

His research interests include robot assembly, robot control, and robot vision.

Shu-Nan Ren received the B. Sc. degree in mechanical engineering from Harbin Institute of Technology, China in 2011. He is currently a Ph. D. degree candidate in mechanical engineering of Department of Mechanical Engineering, Tsinghua University, China.

His research interests include robot painting, robot control and planning.

Ken Chen received the B. Sc. degree in mechanical engineering from Sichuan University, China in 1981, the M. Sc. and Ph.D. degrees in mechanical engineering from Zhejiang University, China in 1984 and 1987, respectively. Currently, he is a professor in the Department of Mechanical Engineering at Tsinghua University, China. He has published over 200 journal and conference papers. He is a member of ASME and IEEE, senior member of Chinese Mechanical Engineering Society (CMES), and member of Life-electronic Sciences Committee and National Mechanical Higher education Direction Committee.

His research interests include robotics and bionics, special manipulating and mobile robots, humanoid robots and bio-robots, integration and automation for mechatronic system.

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Xie, Y., Yang, XD., Liu, Z. et al. Method for visual localization of oil and gas wellhead based on distance function of projected features. Int. J. Autom. Comput. 14, 147–158 (2017). https://doi.org/10.1007/s11633-017-1063-1

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  • DOI: https://doi.org/10.1007/s11633-017-1063-1

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