D.V. Voloshyn

Èlektron. model. 2018, 38(3):109-118


The paper considers a problem of autonomous navigation for an unmanned aerial vehicle (UAV). The main steps of the model construction are described and a method for finding its parameters is presented which is based on ASIFT algorithm that uses video stream as input source. The results of experimental simulation of navigation without GPS signal are presented which confirm practical potential of the model implementation.


autonomous navigation, unmanned aerial vehicle, ASIFT, computer vision.


1. Dufrene Jr, W.R. (2003), “Application of artificial intelligence techniques in uninhabited aerial vehicle flight”, The 22nd Digital Avionics Systems Conference DASC’03, IEEE Transactions, Vol. 2, pp. 3-8.
2. Cir, I. (2011), 328 AN/190 , Unmanned Aircraft Systems (UAS) Circular.
3. Sukkarieh, S., Nebot, E.M. and Durrant-Whyte, H.F. (1999), “A high integrity IMU/GPS navigation loop for autonomous land vehicle applications”, Robotics and Automation. IEEE Transactions, Vol. 15, no. 3, pp. 572-578.
4. Voloshyn, D. (2012), “Kalman filtering methods for eliminating noises in multi-agent system with incomplete information”, Theoretical and Applied Aspects of Cybernetics, Proceedings of the 2nd International Scientific Conference of Students and Young Scientists, Kyiv, Bukrek, 2012, pp. 204-261.
5. Sinopoli, B., Micheli, M., Donato, G. and Koo, T.J. (2001), “Vision based navigation for an unmanned aerial vehicle”, Robotics and Automation, Proceedings of ICRA IEEE International Conference, Vol. 2, pp. 1757-1764.
6. Samadzadegan, F., Hahn, M. and Saeedi, S. (2007), “Position estimation of aerial vehicle based on a vision aided navigation system”, Proceedings of Visualization and Exploration of Geospatial Data, Stuttgart, 2007.
7. Yu, G. and Morel, J.-M. (2011), “Asift: An algorithm for fully affine invariant comparison”, Image Processing On Line, Vol. 1.
8. Lowe, D.G. (1999), “Object recognition from local scale-invariant features”, Computer vision, Proceedings of the 7th IEEE International Conference IEEE 1999, Vol. 2, pp. 1150-1157.
9. Mikolajczyk, K., Tuytelaars, T., Schmid C. and et al. (2005), “A comparison of affine region detectors”, International Journal of Computer Vision, Vol. 65, no. 1-2, pp. 43-72.
10. Moisan, L. and Stival, B. (2004), “A probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix”, International Journal of Computer Vision, Vol. 57, no. 3, pp. 201-218.
11. Fischler, M.A. and Bolles, R.C. (1981), “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Communications of the ACM, Vol. 24, no. 6, pp. 381-395.
12. Hartley, R. and Zisserman, A. (2005), “Multiple view geometry in computer vision”, Robotica, Vol. 23, no. 2, pp. 271-271.
13. Golub, G.H. and Reinsch, C. (1970), “Singular value decomposition and least squares solutions”, Numerische mathematik, Vol. 14, no. 5, pp. 403-420.

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