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Vision-Based Detection and Tracking of Airborne Obstacles in a Cluttered Environment

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Abstract

This paper proposes an image processing algorithm for ‘sense-and-avoid’ of aerial vehicles in short-range at low altitude and shows flight experiment results. Since it can suppress the negative effects cause cluttered environment in image sequence such as the ground seen or sensitivity of threshold value during low-altitude flight, proposed algorithm has better performance of collision avoidance. Furthermore, proposed algorithm can perform better than simple color-based detection and tracking methods because it takes the characteristics of vehicle dynamics into account in image plane. The performance of proposed algorithm is validated by post image processing using video clip taken from flight test and actual flight test with simple avoidance maneuver.

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References

  1. Airplane Flying Handbook: U.S. Department of Transportation Federal Aviation Administration. FAA-H-8083–3A (2004)

  2. Zeitlin, A.D.: Technology milestones - detect, sense & avoid for unmanned aircraft systems. In: AIAA Infotech@Aerospace Conference and Exhibit, AIAA-2007-2765 (2007)

  3. Seong, K.-J., Kim, E.-T., Kim, S.-P.: Development trend of the autonomous flight control technology. Current Industrial and Technological Trends in Aerospace 6(2), 143–153 (2008)

    Google Scholar 

  4. Ross, P.E.: When will we have unmanned commercial airliners? IEEE Spectrum. http://spectrum.ieee.org (2011). Accessed 1 Dec 2011

  5. Oh, S.-H., Koo, S.-O.: Requirements for the introduction of UAVs into airspace. Current Industrial and Technological Trends in Aerospace 4(2), 244–251 (2004)

    Google Scholar 

  6. Cho, S.-J., Jang, D.-S., Tahk, M.-J., Koo, H.-J., Kim, J.-S.: Collision avoidance maneuver of unmanned aerial vehicles applying TCAS-II. J. KSASS 34(4), 33–39 (2006)

    Google Scholar 

  7. Carnie, R., Walker, R., Corke, P.: Image processing algorithms for UAV sense and avoid. In: IEEE Int’ Conference on Robotics and Automation, Orlando, USA, pp. 2848–2853 (2006)

  8. Johnson, E.N., Calise, A.J., Watanabe, Y., Ha, J., Neidhoefer, J.C.: Real-time vision-based relative aircraft navigation. JACIC 4, 707–738 (2007)

    Article  Google Scholar 

  9. Lai, J., Mejias, L., Ford, J.J.: Airborne vision-based collision-detection system. J. Field Robotics 28(2), 137–157 (2011)

    Article  MATH  Google Scholar 

  10. Mejias, L., McNamara, S., Lai, J., Ford, J.J.: Visionbased detection and tracking of aerial targets for UAV collision avoidance. In: IEEE/RSJ Int’ Conference Intelligent Robots S. and Systems, 18 Oct 2010

  11. Cho, S., Huh, S., Choi, H.S., Shim, D.H.: An image processing algorithm for detection and tracking of aerial vehicles. In: 50th IEEE Conference on Decision and Control, pp. 7482–7487, Hilton Orlando Bonnet Creek Hotel, Orlando, USA, 12–15 Dec 2011

  12. Shakernia, O., Chen, W.-Z., Graham, S., Zvanya, J., White, A., Weingarten, N., Raska, V.M.: Sense-and-Avoid (SAA) flight test and lessons learned. In: AIAA Infotech@Aerospace, Rohnert Park, California (2007)

  13. Bradski, G., Kaehler, A.: Learning OpenCV. In: O’REILLY (2008)

  14. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, UK (2003)

    Google Scholar 

  15. Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

  16. Fusiello, A., Trucco, E., Tommasini, T., Roberto, V.: Improving feature tracking with robust statistics. Pattern Anal. Appl. 2(4), 312–3200 (1999)

    Article  Google Scholar 

  17. Bouguet, J.-Y.: Pyramidal implementation of the Lucas Kanade feature tracker. Intel Corporation, Microprocessor Research Labs. http://www.intel.com/research/mrl/research/opencv/ (2000)

  18. Streit, R.L., Luginbuhl, T.E.: Probabilistic multi-hypothesis tracking. NUWC-NPT Technical Report 10.428 (1995)

  19. Lee, D.J., Min, B.-M., Tahk, M.-J., Bang, H., Shim, D.H.: Autonomous flight control system design for a blended wing body. In: Int’ Conference on Control, Automation and Systems, Seoul, Korea, pp. 192–196 (2008)

  20. You, D.-I., Shim, D.H.: Autonomous formation flight test of multi-micro aerial vehicles. J. Intell. Robot. Syst. 61(1–4), 321–337 (2011)

    Article  Google Scholar 

  21. Huh, S., Shim, D.H.: A vision-based landing system for small unmanned aerial vehicles using an airbag. Control. Eng. Pract. 18(7), 812–823 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Aerospace Engineering, KAIST, Daejeon, South Korea

    Sungwook Cho, Sungsik Huh & David Hyunchul Shim

  2. Korea Aerospace Research Institute (KARI), Daejeon, South Korea

    Hyoung Sik Choi

Authors
  1. Sungwook Cho
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  2. Sungsik Huh
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  3. David Hyunchul Shim
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  4. Hyoung Sik Choi
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Corresponding author

Correspondence to Hyoung Sik Choi.

Additional information

Manuscript received (15 Feb, 2012). This work was supported by grant No. 2009–09-sunggwa-7 from the Korea Aerospace Research Institute (KARI) and Brain Korea 21 Project, Korea Advanced Institute of Science and Technology (KAIST).

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Cho, S., Huh, S., Shim, D.H. et al. Vision-Based Detection and Tracking of Airborne Obstacles in a Cluttered Environment. J Intell Robot Syst 69, 475–488 (2013). https://doi.org/10.1007/s10846-012-9702-9

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  • DOI: https://doi.org/10.1007/s10846-012-9702-9

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