KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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The Development of a Multi-sensor Payload for a Micro UAV and Generation of Ortho-images

마이크로 UAV 다중영상센서 페이로드개발과 정사영상제작

  • 한승희 (공주대학교 건설환경공학부)
  • Received : 2014.05.31
  • Accepted : 2014.07.29
  • Published : 2014.10.01
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Abstract

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.

대부분의 지형정보획득을 위한 영상에는 RGB, 근적외선, 열영상이 주로 사용된다. 이 멀티밴드영상은 위성이나 유인항공기에 탑재되어 획득되고 있으나 주기해상도, 비용, 공간해상도, 그리고 구름의 영향 등으로 사용자를 만족시키기 어렵다. 자동항법UAV에 적합한 페이로드와 콘트롤러를 개발한다면 원하는 시간과 주기로 고해상도 멀티밴드영상을 획득할 수 있다. 본 연구에서는 멀티밴드 영상획득을 위한 센서와 페이로드의 개발을 통해 저가의 고해상 영상획득시스템을 구축하고 이를 이용하여 geo-referencing data와 함께 RGB, NIR과 열영상을 획득하였다. 획득한 RGB영상으로 정사모자익영상을 제작하여 검사점에 대한 위치정확도를 분석한 결과 수평좌표에서 0.181m, 수직좌표에서 0.203m의 편차를 얻을 수 있었다. 이는 1:1,000~5000수치지도제작과 소규모지역에 대한 원격탐측이 가능한 공간정확도를 만족하므로 페이로드의 활용성을 검증할 수 있었으며 활용이 기대된다.

Keywords

References

  1. Austin, M. J. (2009). gRAID: A geospatial real-time aerial image display for a low-cost autonomous multispectral remote sensing, Master Thesis, Utah State University.
  2. Baumann, M. (2007). Imager development and image processing for small UAV-based realtime multispectral remote sensing, Master's Thesis, University of Applied Sciences Ravensburg- Weingarten and Utah State University, Logan.
  3. Chao, H. Y. (2010). Cooperative remote sensing and actuation using network unmanned vehicles, Ph.D Degree Thesis, Utah state University, Logan, Utah,
  4. Chao, H. Y., Cao, Y. C. and Chen, Y. Q. (2010). "Autopilots for small unmanned aerial vehicles: A survey." International Journal of Control, Automation, and Systems, Vol. 8, No. 1, pp. 36-44. https://doi.org/10.1007/s12555-010-0105-z
  5. Echard, P. and Lamarre, Gosselin, P. (2005). "Data & Image fusion for multisensor UAV payload." In Advanced Sensory Payloads for UAV, Meeting Proceedings RTO-MP-SET-092, Paper 12. Neuilly-sur-Seine, France: RTO.
  6. Eck, C. (2001). "Navigation algorithms with applications to unmanned helicopters." Dissertation at the Swiss federal institute of technology Zurich.
  7. GPHOTO (2014). Available at: http://gphoto.org, http://gphoto.org/doc/manual/ (Accessed: May 9, 2014)
  8. Han, S. H. (2013). "A design proposal for economical autopiloted UAVs for acquiring geospatial information(I)." International Conference on Geospatial Information Science, proceeding, pp. 138-139.
  9. Han, S. H. (2014). "A design proposal for economical autopiloted UAVs for acquiring geospatial information(II)." Proceeding of Korean society of surveying Geodesy, Phorogrammetry and Cartography, pp. 183-186.
  10. Han, Y. (2009). An autonomous unmanned aerial vehicle-based imagery system development and remote sensing images classification for agricultural applications, Master's Thesis, Utah State Univeristy.
  11. Henri, E. (2004). "A mini unmanned aerial vehicle (UAV): System overview and image acquisition." International Workshop on processing and visualization using high resolution imagery, pp. 18-20.
  12. World Wind Java SDK 2.0 (2014). Available at: http://builds.worldwind.arc.nasa.gov/download-release.asp (Accessed: May 9, 2014)
  13. Hu, S. G., Chao, H. Y., Coopmans, C., Han, J. L. Mac McKeec and Chen, Y. Q. (2010). "Low-Cost UAV-Based thermal infrared remote sensing: Platform, Calibration and applications." 978-1-4244-7101-0 IEEE, pp. 38-43.
  14. NASA (2007). World wind software, Available at: http://worldwind.arc.nasa.gov/index.html (Accessed: April 12, 2014).
  15. Przybilla, H. J. and Wester-Ebbinghaus, W. (1979). "Bildflug mit ferngelenktem kleinflugzeug. Bildmessung und luftbildwesen. Zeitschrift fuer photogrammetrie und fernerkundung." Herbert Wichman Verlag. Karlsruhe.
  16. UVS International (2011). Available at: www.uvs-international.org (Accessed: Jan. 13, 2014).
  17. Wester-Ebbinghaus, W. (1980). "Aerial photography by radio controlled model helicopter." The Photogrammetric Record, England, London, Vol. X, No. 55.

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