Calibration of non-metric UAV camera using different test fields
The paper deals with the calibration of a non-metric digital camera Nikon EOS 6D with a 50 mm lens that could be adapted as a potential UAV sensor for the purposes of aerial inspections. The determination of the internal orientation parameters and the image errors of the non-metric digital camera involved self-calibration with Agisoft Metashape software solving the network of the images obtained from different test fields: a chessboard field, a professional laboratory field and a spatially diverse research area. The results of the control measurement for the examined object distance of 6 meters do not differ significantly. The RMSE from the control measurement for the second analyzed object distance of 15 meters was calculated on the basis of the internal orientation elements. The images from the laboratory field, the spatial test area and the chessboard field were used, and the obtained results amounted to 7.9, 9.9 and 11.5 mm, respectively. The conducted studies showed that in the case of very precise photogrammetric measurements performed by means of the Nikon EOS 6D camera equipped with a 50 mm lens, it is optimal to conduct calibration in a laboratory test field. The greatest RMSE errors were recorded for the control images with the elements of the internal camera orientation calculated on the basis of the chessboard area. The results of the experiments clearly show a relation between the accuracy of the Nikon EOS 6D camera calibrations and the percentage of the frame area filled with the test field. This explains why the weakest calibration results were obtained from the chessboard test field.
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Cramer, M., Przybilla, H.-J., & Zurhorst, A. (2017). UAV cameras: ovierview and geometric calibration benchmark. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W6, 85–92. https://doi.org/10.5194/isprs-archives-XLII-2-W6-85-2017
Deng, C., Wang, S., Huang, Z., Tan, Z., & Liu, J. (2014). Unmanned aerial vehicles for power line inspection: A cooperative way in platforms and communications. Journal of Communications, 9(9), 687–692. https://doi.org/10.12720/jcm.9.9.687-692
Gašparović, M., & Gajski, D. (2016). Two-step camera calibration method developed for micro UAV’s. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B1, 829–833. https://doi.org/10.5194/isprs-archives-XLI-B1-829-2016
Han, D., Park, J. B., & Huh, J. (2018). Orientation analysis between UAV video and photos for 3D measurement of bridges. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(6), 451–456. https://doi.org/10.7848/ksgpc.2018.36.6.451
Jung, S. H., Lim, H. M., & Lee, J. K. (2009). Analysis of the accuracy of the UAV photogrammetric method using digital camera. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 27(6), 741–747.
Jung, S. H., Lim, H. M., & Lee, J. K. (2010). Acquisition of 3D spatial information using UAV photogrammetric method. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 28(1), 161–168.
Kolecki, J., & Rzonca, A. (2015). Accuracy analysis of automatic distortion correction. Geodesy and Cartography, 64(1), 3–14. https://doi.org/10.1515/geocart-2015-0002
Kraus, K. (2007). Photogrammetry – Geometry from images and laser scans (pp. 47–63). de Gruyter. https://doi.org/10.1515/9783110892871
Kurczyński, Z. (2014). Photogrammetry (pp. 353–361, 378–401, 423–439, 503–506). PWN.
Lim, P. C., Seo, J., Son, J., & Kim, T. (2019). Analysis of orientation accuracy of an UAV image according to camera calibration. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W13, 437– 442. https://doi.org/10.5194/isprs-archives-XLII-2-W13-437-2019
Mikoláš, M., Jadviščok, P., & Molčák, V. (2014). Application of terrestrial photogrammetry to the creation of a 3D model of the Saint Hedwig Chapel in the Kaňovice. Geodesy and Cartography, 40(1), 8–13. https://doi.org/10.3846/20296991.2014.906923
Pérez, M., Agüera, F., & Carvajal, F. (2011). Digital camera calibration using images taken from an unmanned aerial vehicle. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1/C22, 167–171. https://doi.org/10.5194/isprsarchives-XXXVIII-1-C22-167-2011
Sužiedelytė‐Visockienė, D., & Bručas, J. (2009). Influence of digital camera errors on the photogrammetric image processing. Geodesy and Cartography, 35(1), 29–33. https://doi.org/10.3846/1392-1541.2009.35.29-33
Tokarczyk, R., & Huppert, M. (2006). Automatyczna detekcja i pomiar markerów w fotogrametrycznym systemie trójwymiarowego pozycjonowania ciała dla celów rehabilitacji leczniczej. Geodezja, AGH Biannual, 12(2/1) (in Polish).
Yanagi H., & Chikatsu, H. (2015). Camera calibration in 3D modelling for UAV application. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-4/W5, 223–226. https://doi.org/10.5194/isprsarchives-XL-4-W5-223-2015
Yusoff, A. R., Ariff, M. F. M., Idris, K. M., Majid, Z., & Chong, A. K. (2017). Camera calibration accuracy at different UAV flying heights. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W3, 595–600. https://doi.org/10.5194/isprs-archives-XLII-2-W3-595-2017
Zhou, Y., Rupnik, E., Meynard, C., Thom, C., & Pierrot-Deseilligny, M. (2020). Simulation and analysis of photogrammetric UAV image blocks – Influence of camera calibration error. Remote Sensing, 12, 22. https://doi.org/10.3390/rs12010022