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Chinese Conference on Image and Graphics Technologies

IGTA 2017: Advances in Image and Graphics Technologies pp 104–112Cite as

Pose Measurement of Drogue via Monocular Vision for Autonomous Aerial Refueling

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 757))

Abstract

In the probe-and-drogue refueling system, pilots need to operate carefully to dock probe with drogue, autonomous aerial refueling technology can assistant pilots to accomplish this operation. In this paper, we proposed a novel framework to measure pose of drogue via monocular vision, pose information of drogue can further lead control system accomplish aerial refueling automatically. This framework is consisted of three parts: detecting landmarks of drogue, locating contour of drogue in image, figuring out pose of drogue. Experiment results indicate that this pose measurement system is both accurate and efficient.

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References

  1. Doebbler, J.: Boom and receptacle autonomous air refueling using a visual pressure snake optical sensor. J. Guid. Control Dyn. 30(6), 1753–1769 (2007)

    Article  Google Scholar 

  2. Mammarella, M., Campa, G., Napolitano, M.R., Fravolini, M.L., Gu, Y., Perhinschi, M.G.: Machine Vision/GPS integration using EKF for the UAV aerial refueling problem. IEEE Trans. Syst. Man Cybern. 38(6), 791–801 (2008)

    Article  Google Scholar 

  3. Campa, G., Fravolini, M.L., Ficola, A., et al.: Autonomous aerial refueling for UAVs using a combined GPS-machine vision guidance. In: AIAA Guidance, Navigation, and Control Conference and Exhibit (2004)

    Google Scholar 

  4. Song, C.H., Zhao, J.T., Liu, H.C., et al.: Relative position calculation based on near-infrared for autonomous aerial refueling. Power Electron. 47(1), 41–43 (2013)

    Google Scholar 

  5. Chen, C.: Drogue tracking using 3D flash lidar for autonomous aerial refueling. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 8037(1), pp. 2362–2375 (2011)

    Google Scholar 

  6. Kimmett, J., Valasek, J., Junkins, J.: Autonomous aerial refueling utilizing a vision based navigation system. In: AIAA Guidance, Navigation, and Control Conference and Exhibit. (2013)

    Google Scholar 

  7. Yin, Y., Xu, D., Wang, X., et al.: Detection and tracking strategies for autonomous aerial refueling tasks based on monocular vision. Int. J. Adv. Rob. Syst. 11(4), 399–412 (2014)

    Google Scholar 

  8. Martinez, C., Richardson, T., Thomas, P., du Bois, J.L., Campoy, P.: A vision-based strategy for autonomous aerial refueling tasks. Robot. Auton. Syst. 61(8), 876–895 (2013)

    Article  Google Scholar 

  9. 尹英杰. 自主空中加油的目标视觉检测与跟踪策略研究. 中国科学院大学 (2016)

    Google Scholar 

  10. Cootes, T.F., Taylor, C.J., Cooper, D.H., et al.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  11. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

    Article  Google Scholar 

  12. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)

    Google Scholar 

  13. Chen, Z., Huang, J.B.: A vision-based method for the circle pose determination with a direct geometric interpretation. IEEE Trans. Robot. Autom. 15(6), 1135–1140 (2000)

    Article  MathSciNet  Google Scholar 

  14. Ren, S., Cao, X., Wei, Y., et al.: Face Alignment at 3000 FPS via regressing local binary features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685–1692 (2014)

    Google Scholar 

Download references

Acknowledgment

This work is supported by National Natural Science Foundation (Grant No. 61573349), National Natural Science Foundation—Outstanding Youth Foundation (Grant No. 5140051852) and The National High Technology Research and Development Program of China (863 Program) (Grant No. 2015AA042308).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yun Ye, Yingjie Yin, Wenqi Wu & Xingang Wang

  2. Beijing Information Science & Technology University, Beijing, 100192, China

    Zhaohui Zhang & Chaochao Qian

Authors
  1. Yun Ye
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  2. Yingjie Yin
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  3. Wenqi Wu
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  4. Xingang Wang
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  5. Zhaohui Zhang
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  6. Chaochao Qian
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Corresponding author

Correspondence to Yun Ye .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Tsinghua University, Beijing, China

    Shengjin Wang

  3. Beijing Institute of Technology, Beijing, China

    Yue Liu

  4. Beijing Institute of Technology, Beijing, China

    Jian Yang

  5. School of EECS, Center for Information Science, Peking University, Beijing, China

    Xiaoru Yuan

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  7. La Trobe University, Melbourne, Victoria, Australia

    Henry Been-Lirn Duh

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© 2018 Springer Nature Singapore Pte Ltd.

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Cite this paper

Ye, Y., Yin, Y., Wu, W., Wang, X., Zhang, Z., Qian, C. (2018). Pose Measurement of Drogue via Monocular Vision for Autonomous Aerial Refueling. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_11

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  • DOI: https://doi.org/10.1007/978-981-10-7389-2_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7388-5

  • Online ISBN: 978-981-10-7389-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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