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

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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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).

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  • unmanned aerial vehicle
  • ground map correction
  • neural network
  • image segmentation
  • vector and raster map comparison
  • machine vision system