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UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition

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Abstract—The paper considers an original autonomous correction algorithm for UAV navigation system based on comparison between terrain images obtained by onboard machine vision system and vector topographic map images. Comparison is performed by calculating the homography of vision system images segmented using the convolutional neural network and the vector map images. The presented results of mathematical and flight experiments confirm the algorithm effectiveness for navigation applications.

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Authors and Affiliations

  1. KT Unmanned Systems, St. Petersburg, Russia

    A. P. Tanchenko & A. M. Fedulin

  2. Institute of Engineering Physics, Serpukhov, Russia

    R. R. Bikmaev

  3. ERA Military Innovation Technopolis, Anapa, Russia

    R. N. Sadekov

  4. National Institute of Science and Technology MISiS, Moscow, Russia

    R. N. Sadekov

Authors
  1. A. P. Tanchenko
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  2. A. M. Fedulin
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  3. R. R. Bikmaev
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  4. R. N. Sadekov
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Correspondence to R. N. Sadekov.

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Tanchenko, A.P., Fedulin, A.M., Bikmaev, R.R. et al. UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition. Gyroscopy Navig. 11, 293–299 (2020). https://doi.org/10.1134/S2075108720040100

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