|
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 29th
European Conference on Modelling and Simulation, Albena (Varna), Bulgaria,
May 26th – 29th,
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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