Algorithm of unmanned aerial vehicles navigation based on the image of the underlying surface

Modern unmanned aerial vehicles can use various types of navigation systems to determine precise coordinates and orientation in space. An algorithm for visual navigation of an unmanned aerial vehicle based on a machine vision system and a pre-built map of the flight area is presented. A comparative analysis of approaches to navigation of unmanned aerial vehicles using computer vision is performed. Based on the results of the analysis, an approach is chosen to search for pairs of corresponding points on the terrain map and in the image from an unmanned aerial vehicle. Implementation options are considered, and based on the results of a comparative analysis, the ORB (Oriented FAST and Rotated BRIEF) algorithm with sorting of key point descriptors by Hamming distance is selected. Based on the results of field tests, an orthophotoplane is created. An algorithm is developed that outputs the latitude and longitude values with an error of up to two meters based on the compared image from an unmanned aerial vehicle with an orthophotoplane. The developed hardware and software complex can be used to determine an unmanned aerial vehicle location using a video camera.

1. Zitová B., Flusser J. Image and Vision Computing, 2003, no. 11(21), pp. 977–1000.

2. Lowe D.G. International Journal of Computer Vision, 2004, no. 2(60), pp. 91–110.

3. Simonyan K., Zisserman A. arXiv:1409.1556 [cs.CV], 2014, https://doi.org/10.48550/arXiv.1409.1556.

4. Szeliski R. Computer Vision: Algorithms and Applications, Texts in Computer Science, Springer International Publishing, 2010, рр. 183–228.

5. Fraundorfer F., Scaramuzza D., Pollefeys M. Foundations and Trends in Computer Graphics and Vision, 2012, no. 1(7), pp. 1–177.

6. Schleiss M. Remote Sensing and Spatial Information Sciences, 2019, vol. 42, рр. 575–580.

7. Silva Filho P., Shiguemori E.H., Saotome O. International Conference on Unmanned Aerial Vehicles in Geomatics, 2017, рр. 89–94.

8. Isola P., Zhu J.-Y., Zhou T., Efros A.A. Proceedings of the IEEE Conference on computer vision and pattern recognition, 2017, рр. 1125–1134.

9. Alcantarilla P.F., Nuevo J., Bartoli A. British Machine Vision Conference (BMVC), 2013, рр. 10.

10. Wang X., Kealy A., Li W., Jelfs B., Gilliam C., May S.L., Moran B.T. Electronics, 2021, no. 4(10), pp. 15.

11. Bay H., Tuytelaars T., Van Gool L. Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2006, рр. 404–417.

12. Fischler M.A., Bolles R.C. Communications of the ACM, 1981, no. 6(24), pp. 381–395.

13. Masselli A., Hanten R., Zell A. Advances in Intelligent Systems and Computing, Springer International Publishing, 2016. vol. 13, рр. 831–842.

14. Rublee E., Rabaud V., Konolige K., Bradski G. International Conference on Computer Vision, 2011, рр. 2564–2571.

15. Harvey W., Rainwater C., Cothren J. Remote Sensing, 2021, vol. 13, рр. 43.

16. Kendall A., Grimes M., Cipolla R. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015, рр. 2938–2946.

17. Krasnabayeu E.A., Chistabayeu D.V., Malyshev A.L. Computer Optics, 2019, no. 3(43), pp. 434–445. (in Russ.)