UAV Navigation On The Basis Of The Feature Points Detection On Underlying Surface

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.

[1]  B. S. Manjunath,et al.  The multiRANSAC algorithm and its application to detect planar homographies , 2005, IEEE International Conference on Image Processing 2005.

[2]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[3]  Ivan Konovalenko,et al.  Experimental comparison of methods for estimation of the observed velocity of the vehicle in video stream , 2015, Other Conferences.

[4]  V. Aidala,et al.  Biased Estimation Properties of the Pseudolinear Tracking Filter , 1982, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Alexander B. Miller,et al.  UAV control on the basis of bearing-only observations , 2014, 2014 4th Australian Control Conference (AUCC).

[6]  Richard W. Osborne,et al.  Bias estimation for optical sensor measurements with targets of opportunity , 2013, Proceedings of the 16th International Conference on Information Fusion.

[7]  P. Dunne,et al.  Stochastic Differential Systems , 1988 .

[8]  Yaakov Bar-Shalom,et al.  Efficient data association for 3D passive sensors: If i have hundreds of targets and ten sensors (or more) , 2011, 14th International Conference on Information Fusion.

[9]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[10]  Thiagalingam Kirubarajan,et al.  Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem , 2002, SPIE Defense + Commercial Sensing.

[11]  Yaakov Bar-Shalom,et al.  Statistical Efficiency of Composite Position Measurements from Passive Sensors , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[12]  N. Aouf,et al.  Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering , 2010, IEEE Sensors Journal.

[13]  Xueyuan Guan,et al.  A GPU accelerated real-time self-contained visual navigation system for UAVs , 2012, 2012 IEEE International Conference on Information and Automation.

[14]  Denis Pillon,et al.  Leg-by-leg Bearings-Only Target Motion Analysis Without Observer Maneuver , 2011, J. Adv. Inf. Fusion.

[15]  Jinling Wang,et al.  Adaptive Filter Design for UAV Navigation with GPS/INS/Optic Flow Integration , 2010, 2010 International Conference on Electrical and Control Engineering.

[16]  Alexander B. Miller,et al.  Tracking of the UAV trajectory on the basis of bearing-only observations , 2014, 53rd IEEE Conference on Decision and Control.

[17]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Brian D. O. Anderson,et al.  Optimality analysis of sensor-target localization geometries , 2010, Autom..

[19]  V. Pugachev,et al.  Stochastic Differential Systems Analysis and Filtering , 1987 .

[20]  A. Tsourdos,et al.  Robust nonlinear filtering for INS/GPS UAV localization , 2008, 2008 16th Mediterranean Conference on Control and Automation.