JOURNAL BROWSE
Search
Advanced SearchSearch Tips
The Development of a Multi-sensor Payload for a Micro UAV and Generation of Ortho-images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
The Development of a Multi-sensor Payload for a Micro UAV and Generation of Ortho-images
Han, Seung Hee;
  PDF(new window)
 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.
 Keywords
Geospatial information;Autonomous flight;UAS;UAV;Autopilot;
 Language
Korean
 Cited by
1.
회전익 UAS 영상기반 고밀도 측점자료의 위치 정확도 평가,이용창;

한국지형공간정보학회지, 2015. vol.23. 2, pp.39-48 crossref(new window)
1.
Assessing the Positioning Accuracy of High density Point Clouds produced from Rotary Wing Quadrocopter Unmanned Aerial System based Imagery, Journal of Korean Society for Geospatial Information System, 2015, 23, 2, 39  crossref(new windwow)
 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. crossref(new window)

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.