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Maximizing Water Surface Target Localization Accuracy Under Sunlight Reflection with an Autonomous Unmanned Aerial Vehicle

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Abstract

Reflected sunlight can significantly impact the effectiveness of vision-based object detection and tracking algorithms, especially ones developed for an aerial platform operating over a marine environment. These algorithms often fail to detect water surface objects due to sunlight glitter or rapid course corrections of unmanned aerial vehicles (UAVs) generated by the laws of aerodynamics. In this paper, we propose a UAV path planning method that maximizes the stationary or mobile target detection likelihood during localization and tracking by minimizing the sunlight reflection influences. In order to better reduce sunlight reflection effects, an image-based sunlight reflection reception adjustment is also proposed. We validate our method using both stationary and mobile target tracking tests.

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References

  1. Beard, R., McLain, T.W.: Small Unmanned Aircraft: Theory and Practice. Princeton University Press, Princeton (2011)

    Google Scholar 

  2. Bellingham, J., Richards, A., How, J.P.: Receding horizon control of autonomous aerial vehicles. In: Proc. of the American Control Conference (2002)

  3. Clancy, L.J.: Aerodynamics. Pitman Publishing Limited, London (1975)

    Google Scholar 

  4. Cox, C., Munk, W.: Measurement of the roughness of the sea surface from photographs of the sun’s glitter. J. Opt. Soc. Am. 44(11), 838–850 (1954)

    Article  Google Scholar 

  5. Dobrokhodov, V.N., Kaminer, I.I., Jones, K.D., Ghabcheloo, R.: Vision-based tracking and motion estimation for moving targets using small UAVs. In: Proc. of the 2006 American Control Conference (2006)

  6. Droge, G., Egerstedt, M.: Adaptive look-ahead for robotic navigation in unknown environments. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2011)

  7. Fred, N.: Directional reflectance and emissivity of an opaque surface. Appl. Opt. 4(7), 767–775 (1965)

    Article  Google Scholar 

  8. Kwon, H., Yoder, J., Baek, S., Gruber, S., Pack, D.: Multiple target detection on water surfaces using background modeling while removing sunlight reflection and land surface area. In: Proc. of the AIAA Infotech@Aerospace Conference (2013)

  9. Oren, M., Nayar, S.K.: Generalization of Lambert’s reflectance model. In: Proc. of SIGGRAPH (1994)

  10. Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18(6), 311–317 (1975)

    Article  Google Scholar 

  11. Quigley, M., Griffiths, S., Beard, R.W.: Target acquisition, localization, and surveillance using a fixed-wing mini-UAV and gimbaled camera. In: Proc. of the IEEE International Conference on Robotics and Automation (2005)

  12. Rafi, F., Khan, S., Shafiq, K., Shah, M.: Autonomous target following by unmanned aerial vehicles. In: Proc. of the SPIE Defense and Security Symposium (2006)

  13. Skoglar, P.: Planning Methods for Aerial Exploration and Ground Target Tracking. Ph.D. thesis, Linkoping University (2009)

  14. Theodorakopoulos, P., Lacroix, S.: A strategy for tracking a ground target with a UAV. In: Proc. of the IEEE Intelligent Robots and Systems (2008)

  15. Torrance, K.E., Sparrow, E.M.: Theory of off-specular reflection from roughened surfaces. J. Opt. Soc. Am. 57(9), 1105–1114 (1967)

    Article  Google Scholar 

  16. Wolff, L.B., Oren, M., Nayar, S.K.: Improved diffuse reflection models for computer vision. Int. J. Comput. Vis. 30(1), 55–71 (1998)

    Article  Google Scholar 

  17. Zengin, U., Dogan, A.: Real-time target tracking for autonomous UAVs in adversarial environments: a gradient search algorithm. IEEE Trans. Robot. 23(2), 294–307 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Academy Center for UAS Research, United States Air Force Academy, USAF Academy, CO, 80840, USA

    Hyukseong Kwon, Josiah Yoder, Stanley Baek & Scott Gruber

  2. Department of Electrical and Computer Engineering, University of Texas in San Antonio, San Antonio, TX, 78249, USA

    Daniel Pack

Authors
  1. Hyukseong Kwon
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  2. Josiah Yoder
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  3. Stanley Baek
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  4. Scott Gruber
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  5. Daniel Pack
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Corresponding author

Correspondence to Hyukseong Kwon.

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Distribution A. Approved for Public Release: Distribution Unlimited. The views expressed in this article are those of the authors and not necessarily those of the U.S. Air Force Academy, the U.S. Air Force, the Department of Defense, or the U.S. Government.

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Kwon, H., Yoder, J., Baek, S. et al. Maximizing Water Surface Target Localization Accuracy Under Sunlight Reflection with an Autonomous Unmanned Aerial Vehicle. J Intell Robot Syst 74, 395–411 (2014). https://doi.org/10.1007/s10846-013-9944-1

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  • DOI: https://doi.org/10.1007/s10846-013-9944-1

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