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http://dx.doi.org/10.7780/kjrs.2021.37.3.17

Analysis on Mapping Accuracy of a Drone Composite Sensor: Focusing on Pre-calibration According to the Circumstances of Data Acquisition Area  

Jeon, Ilseo (Department of Geoinformatics, University of Seoul)
Ham, Sangwoo (Department of Geoinformatics, University of Seoul)
Lee, Impyeong (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.37, no.3, 2021 , pp. 577-589 More about this Journal
Abstract
Drone mapping systems can be applied to many fields such as disaster damage investigation, environmental monitoring, and construction process monitoring. To integrate individual sensors attached to a drone, it was essential to undergo complicated procedures including time synchronization. Recently, a variety of composite sensors are released which consist of visual sensors and GPS/INS. Composite sensors integrate multi-sensory data internally, and they provide geotagged image files to users. Therefore, to use composite sensors in drone mapping systems, mapping accuracies from composite sensors should be examined. In this study, we analyzed the mapping accuracies of a composite sensor, focusing on the data acquisition area and pre-calibration effect. In the first experiment, we analyzed how mapping accuracy varies with the number of ground control points. When 2 GCPs were used for mapping, the total RMSE has been reduced by 40 cm from more than 1 m to about 60 cm. In the second experiment, we assessed mapping accuracies based on whether pre-calibration is conducted or not. Using a few ground control points showed the pre-calibration does not affect mapping accuracies. The formation of weak geometry of the image sequences has resulted that pre-calibration can be essential to decrease possible mapping errors. In the absence of ground control points, pre-calibration also can improve mapping errors. Based on this study, we expect future drone mapping systems using composite sensors will contribute to streamlining a survey and calibration process depending on the data acquisition circumstances.
Keywords
Composite sensor; UAV Photogrammetry; Georeferencing; Pre-calibration; Camera calibration; Mounting parameters;
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1 Xiufeng, X., Z.Y. Long, Z. Chengyu, and W. Xuewei, 2020. Analysis of related factors of the precision of large-scale surveying of unmanned aerial vehicles (UAV), Proc. of 2020 IOP Conference Series: Earth and Environmental Science, Dali, CN, July 17-19, vol. 565, p. 012088.
2 Reshetyuk, Y. and S. Martensson, 2016. Generation of highly accurate digital elevation models with unmanned aerial vehicles, The Photogrammetric Record, 31(154): 143-165   DOI
3 Ellum, C. and N. El-Sheimy, 2002. The calibration of image-based mobile mapping systems, Proc. of 2002 Symposium on Geodesy for Geotechnical and Structural Engineering, Berlin, DE, May 21-24, pp. 21-24.
4 Faugeras, O.D., Q.T. Luong, and S.J. Maybank, 1992. Camera self-calibration: theory and experiments, Proc. of 1992 European Conference on Computer Vision, Santa Margherita Ligure, IT, May. 19-22, vol. 588, pp. 321-334.
5 Coveney, S. and K. Roberts, 2017. Lightweight UAV digital elevation models and orthoimagery for environmental applications: data accuracy evaluation and potential for river flood risk modelling, International Journal of Remote Sensing, 38(8-10): 3159-3180.   DOI
6 Cheon, J., K. Choi, and I. Lee, 2018. Development of image-map generation and visualization system based on UAV for real-time disaster monitoring, Korean Journal of Remote Sensing, 34(2-2): 407-418 (in Korean with English abstract).   DOI
7 Ferrer-Gonzalez, E., F. Agura-Vega, F. Carvajal-Ramirez, and P. Martinez-Carricondo, 2020. UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points, Remote Sensing, 12(15): 2447.   DOI
8 Jeon, E., K. Choi, and I. Lee, 2015. A high-speed automatic mapping system based on a multi-sensor micro UAV System, Journal of Korea Spatial Information Society, 23(3): 91-100 (in Korean with English abstract).   DOI
9 Kim, S., B. Song, S. Cho, and G. We, 2019. Applicability of drone mapping for natural disaster damage investigation, Journal of Korean Society for Geospatial Information Science, 27(2): 13-21 (in Korean with English abstract).
10 Lee, J., K. Choi, and I. Lee, 2012. Calibration of a UAV based low altitude multi-sensor photogrammetric system, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 30(1): 31-38 (in Korean with English abstract).   DOI
11 Sanz-Ablanedo, E., J. Chandler, J.R. Rodriguez-Perez, and C. Ordonez, 2018. Accuracy of Unmanned Aerial Vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used, Remote Sensing, 10(1606): 1-19.   DOI
12 Tahar, K.N., 2013. An evaluation on different number of ground control points in unmanned aerial vehicle photogrammetric block, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40: 93-98.
13 Agisoft, 2021. Agisoft Metashape, https://www.agisoft.com/, Accessed on Jun. 22, 2021.
14 Aguera-Vega, F., F. Carvajal-Ramirez, and P. Martinez-Carricondo, 2017. Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle, Measurement, 98: 221-227   DOI
15 Oh, J.H., Y.J. Jang, and C.N. Lee, 2018. Accuracy analysis of low-cost UAV photogrammetry for corridor mapping, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(6): 565-572 (in Korean with English abstract).   DOI
16 Chung, D., M. Lee, H. Kim, J. Park, and I. Lee, 2020. Development of forest fire monitoring system using a long-term enduarance solar powered drone and deep learning, Journal of Korean Society for Geospatial Information Science, 28(2): 29-38 (in Korean with English abstract).
17 Choi, K., J. Lee, and I. Lee, 2011. Development of close-range real-time aerial monitoring system based on a low altitude unmanned air vehicle, Spatial Information Research, 19(4): 21-31.
18 Jimenez-Jimenez, S.I., W. Ojeda-Bustamante, M.D.J. Marcial-Pablo, and J. Enciso, 2021. Digital terrain models generated with low-cost UAV photogrammetry: methodology and accuracy, ISPRS International Journal of Geo-Information, 10(5): 285.   DOI
19 Martinez-Carricondo, P., F. Aguera-Vega, F. Carvajal-Ramirez, F. Mesas-Carrascosa, A. Garcia-Ferrer, and F. Perez-Porras, 2018. Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points, International Journal of Applied Earth Observation and Geoinformation, 72:1-10   DOI