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Abstract

Summary

One of the main requirements for an aerial surveying UAV is to maintain the minimum angles of inclination of the external orientation of the images. And so, the idea arose to find the correlation coefficients between them when modeling the angles of inclination α and ω relative to the fixed angle ϰ (since this angle is actually compensated with the help of an aero device). So, if there is such a dependence, then based on the values of these values, it is possible to introduce a correction in the design of a micro-UAV and thereby increase stability during horizontal flight. To implement the given task, that is, to determine the values of correlation dependencies between the angular elements of the external orientation of digital images, the authors proposed and conducted an experiment using an electronic total station, a non-metric camera and a control-measuring grid (CMG). As a result, CMG images were obtained at angles of inclination α and ω from −5° to 5°. 135 control points and 9 reference points were measured on each image. After that, external orientation is made for each picture according to the proposed algorithm.

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/content/papers/10.3997/2214-4609.2023510019
2023-10-02
2025-02-19
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References

  1. Awasthi, B., Karki, S., Regmi, P., Dhami, D., Thapa, S. & Panday, U. (2020). Analyzing the Effect of Distribution Pattern and Number of GCPs on Overall Accuracy of UAV Photogrammetric Results. In: Jain, K., Khoshelham, K., Zhu, X., Tiwari, A. (eds) Proceedings of UASG 2019. UASG 2019. Lecture Notes in Civil Engineering, 51. Springer, Cham. https://doi.org/10.1007/978-3-030-37393-1_29
    [Google Scholar]
  2. Hardin, P.J. and Hardin, T.J. (2010), Small-Scale Remotely Piloted Vehicles in Environmental Research.Geography Compass, 4: 1297–1311. https://doi.org/10.1111/j.1749-8198.2010.00381.
    [Google Scholar]
  3. Hastaoğlu, K. Ö., Gögsu, S. & Gül, Y. (2022). Determining the relationship between the slope and directional distribution of the UAV point cloud and the accuracy of various IDW interpolation.International Journal of Engineering and Geosciences.7 (2), 161–173. DOI: 10.26833/ijeg.940997.
    https://doi.org/10.26833/ijeg.940997 [Google Scholar]
  4. Hlotov, V., Fys, M., Siejka, Z. & Yurkiv, M. (2022). Accuracy assessment of external orientation elements for digital images obtained from UAVS using derivatives of implicitly specified functions.Remote Sensing Applications: Society and Environment, 25, P. 100683. https://doi.org/10.1016/j.rsase.2021.100683.
    [Google Scholar]
  5. Ma, L., Li, M., Tong, L., Wang, Y., & Cheng, L. (2013). Using unmanned aerial vehicle for remote sensing application.21st International Conference on Geoinformatics.1–5. IEEE. doi: 10.1109/Geoinformatics.2013.6626078.
    https://doi.org/10.1109/Geoinformatics.2013.6626078 [Google Scholar]
  6. Zeybek, M. (2021). Classification of UAV point clouds by random forest machine learning algorithm.Turkish Journal of Engineering.5(2), 48–57. DOI: 10.31127/tuje.669566
    https://doi.org/10.31127/tuje.669566 [Google Scholar]
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