Digital Library of the European Council for Modelling and Simulation

Title:	UAV Navigation On The Basis Of The Feature Points Detection On Underlying Surface
Authors:	Ivan A. Konovalenko, Alexander B. Miller, Boris M. Miller, Dmitry P. Nikolaev
Published in:	(2015).ECMS 2015 Proceedings edited by: Valeri M. Mladenov, Grisha Spasov, Petia Georgieva, Galidiya Petrova, European Council for Modeling and Simulation. doi:10.7148/2015 ISBN: 978-0-9932440-0-1 29^th European Conference on Modelling and Simulation, Albena (Varna), Bulgaria, May 26^th – 29^th, 2015
Citation format:	Ivan A. Konovalenko, Alexander B. Miller, Boris M. Miller, Dmitry P. Nikolaev (2015). UAV Navigation On The Basis Of The Feature Points Detection On Underlying Surface, ECMS 2015 Proceedings edited by: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova  European Council for Modeling and Simulation. doi:10.7148/2015-0499
DOI:	http://dx.doi.org/10.7148/2015-0499
Abstract:	This work relates to the intelligent systems tracking such as UAV’s (unmanned aviation vehicle) navigation in GPS-denied environment. Generally it considers the tracking of the UAV path on the basis of bearing-only observations including azimuth and elevation angles. It is assumed that UAV’s cameras are able to capture the angular position of reference points and to measure the directional angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Meanwhile, there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings, though these method has a bias. Recently it was shown that the application of the modified filter based on the pseudomeasurements approach provides the reliable UAV control on the basis of the observation of reference points nominated before the flight. This approach uses the known coordinates of reference points and then applies the optimal linear Kalman type filter. The principal difference with the usage of location of reference points nominated in advance is that here we use the observed reference points detected on-line during the flight. This approach permits to reduce the necessary on-board memory up to reasonable size. In this article the modified pseudomeasurement method without bias for estimation of the UAV position has been suggested. On the basis of this estimation the control algorithm which provides the tracking of reference path in case of external perturbation and the angles measurements errors has been developed. Another principal novelty of this work is the usage of RANSAC approach to detection of reference landmarks which used further for estimation of the UAV position.
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