Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Improvement of UAV Attitude Information Estimation Performance Using Image Processing and Kalman Filter

영상처리와 칼만필터를 이용한 UAV의 자세 정보 추정 성능 향상

  • Ha, Seok-Wun (Department of Aerospace and Software/RECAPT, Gyeongsang National University) ;
  • Paul, Quiroz (Graduate School of Speciallized Aerospace Engineering, Gyeongsang National University) ;
  • Moon, Yong-Ho (Department of Aerospace and Software/RECAPT, Gyeongsang National University)
  • 하석운 (경상대학교 항공우주및소프트웨어공학전공/항공기부품기술연구소) ;
  • 폴 퀴로즈 (경상대학교 항공우주특성화대학원) ;
  • 문용호 (경상대학교 항공우주및소프트웨어공학전공/항공기부품기술연구소)
  • Received : 2018.10.02
  • Accepted : 2018.12.20
  • Published : 2018.12.31
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Abstract

In recent years, researches utilizing UAV for military purposes such as precision tracking and batting have been actively conducted. In order to track the preceding flight, there has been a previous research on estimating the attitude information of the flight such as roll, pitch, and yaw using images taken from the rear UAV. In this study, we propose a method to estimate the attitude information more precisely by applying the Kalman filter to the existing image processing technique. By applying the Kalman filter to the estimated attitude data using image processing, we could reduce the estimation error of the attitude angle significantly. Through the simulation experiments, it was confirmed that the estimation using the Kalman filter can estimate the posture information of the aircraft more accurately.

최근에 정밀 추적이나 타격 등의 군사 목적으로 UAV를 활용하는 연구가 매우 활발하게 진행되고 있다. 앞서가는 비행체를 추적하기 위해 후방에서 촬영한 영상을 활용하여 롤, 피치, 요와 같은 그 비행체의 자세 정보를 추정하는 기존의 연구가 진행되었다. 본 연구에서는 기존의 영상처리기법을 이용한 연구에 칼만 필터를 적용함으로써 자세 정보를 더욱 정밀하게 추정하는 방법을 제시한다. 영상처리를 사용해서 추정한 비행 자세 데이터에 칼만 필터를 적용함으로써 기존의 방식에서 발생했던 자세 각도의 추정오차 범위를 크게 줄일 수 있었다. 시뮬레이션 실험을 통해서, 칼만 필터를 적용할 경우 비행체의 자세 정보를 더욱 정확하게 추정할 수 있음을 확인할 수 있었다.

Keywords

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Fig. 1. Overall Structure of the Proposed System

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Fig. 2. Block Diagram of UAV Attitude Estimation System Proposed by Paul and others

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Fig. 3. Process of Kalman Filter

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Fig. 4. Overall Configuration of The Proposed System using Kalman Filter

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Fig. 5. Software Configuration that Performs Kalman Filter Processing

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Fig. 6. Mean and Maximum Error Values for The Estimated Before and After Kalman Filter Processing

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Fig. 7. Comparison of RMSE measurement results after image processing and Kalman filter processing

Table 1. Comparisons among the Measured and the Estimated Data

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Table 2. Comparison of Mean and Max RMSE Values for Three Attitude Angles

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References

  1. E. N. Barmpounakis, E. I. Vlahogianni & J. C. Golias.(2016). Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges. International Journal of Transportation Science and Technology, 5(3), 111-122. DOI : 10.1016/j.ijtst.2017.02.001
  2. J. P. Lee, J. W. Lee & K. H. Lee. (2016). A Scheme of Security Drone Convergence Service using Cam-Shift Algorithm. Journal of the Korea Convergence Society, 7(5), 29-34. DOI : 10.15207/jkcs.2016.7.5.029
  3. H. J. Mun. (2016). Countermeasure to Underlying Security Threats in IoT communication. Journal of IT Convergence Society for SMB, 6(2), 37-44. DOI : 10.22156/cs4smb.2016.6.2.037
  4. K. O. Park & J. K. Lee. (2017). A Countermeasure Technique for Attack of Reflection SSDP in Home IoT. Journal of Convergence for Information Technology, 7(2), 1-9. DOI : 10.22156/cs4smb.2017.7.2.001
  5. K. B. Kim, G. M. Geum & C. B. Jang. (2017). Research on the Convergence of CCTV Video Information with Disaster Recognition and Real-time Crisis Response System. Journal of the Korea Convergence Society, 8(3), 15-22. DOI : 10.15207/jkcs.2017.8.3.015
  6. Y. B. Sebbane. (2016). Smart Autonomous Aircraft: Flight Control and Planning for UAV. New York: CRC Press.
  7. Y. Wang, J. Mangnus, D. Kostic, H. Nijmeijer & S. T. H. Jansen. (2011). Vehicle State Estimation Using GPS/IMU Integration. 2011 IEEE SENSORS Proceedings (pp. 28-31). USA : IEEE. DOI : 10.1109/icsens.2011.6127142
  8. Q. Paul, J. H. Hyeon, Y. H. Moon & S. W. Ha. (2017). A Study on Attitude Estimation of UAV Using Image Processing. Journal of the Korea Convergence Society, 7(5), 29-34. DOI : 10.22156/CS4SMB.2017.7.5.137
  9. H. D. Cheng, X. H. Jiang, Y. Sun & J. Wang. (2001). Color Image Segmentation: Advances and Prospects. Pattern Recognition, 34(12), 2259-2281. DOI : 10.1016/s0031-3203(00)00149-7
  10. W. S. Hwang & M. R. Choi. (2016). Convergence Research of Low-Light Image Enhancement Method and Vehicle Recorder. Journal of the Korea Convergence Society, 7(6), 1-6. DOI : 10.15207/jkcs.2016.7.6.001
  11. J. C. Yoo & C. W. Ahn. (2013). Template Matching of Occluded Object under Low PSNR. Digital Signal Processing, 23(3), 870-878. DOI : 10.1016/j.dsp.2012.12.004
  12. L. Ding & A. Goshtasby. (2001). On the Canny Edge Detector. Pattern Recognition, 34(3), 721-725. https://doi.org/10.1016/S0031-3203(00)00023-6
  13. J. Cui, J. Xie, T. Liu, X. Guo & Z. Chen. (2014). Corners Detection on Finger Vein Images using the Improved Harris Algorithm. International Journal for Light and Electron Optics, 125(17), 4668-4671. DOI : 10.1016/j.ijleo.2014.05.026
  14. P. Mukhopadhyay & B. B. Chaudhuri. (2015). A Survey of Hough Transform. Pattern Recognition, 48(3), 993-1010. DOI : 10.1016/j.patcog.2014.08.027
  15. S. M. Ross. (2017). Introductory Statistics: Chapter 12 - Linear Regression. USA : Academic Press. 519-584
  16. Y. I. Kim. (2017). An Oral Health Promotion Behavior Model for Adolescents. The Korean Journal of health service management, 11(2), 129-142.
  17. G. Welch and G. Bishop. (2001). An Introduction to the Kalman Filter. SIGGRAPH. ACM, Inc..
  18. Laminal Research. (2017). How X_Plane Works. X-Plane. http://www.x-plane.com/desktop/how-x-plane-works/
  19. Wikipedia. (2018). Root-mean-square deviation. https://en.wikipedia.org/wiki/Root-mean-square_deviation.