Vision based stabilization for aircraft in unknown environment without GPS signal

Abstract

A data fusion method is extensively investigated to stabilize the motion of aircraft with the visual and inertial measurements in the unknown environment without the signal of the global positioning system and with the unknown inertial measurement biases. There is no information on the landmarks on the ground. What can be used are the sequence of collected images. The main idea of the method is to apply the correspondence between the two successive images based on a new formulation of the extended Kalman filtering to fuse the visual and inertial measurements to predict the necessary feedback information and the unknown inertial measurement biases. Neither computation nor decomposition of any essential matrix is needed. The observability of the new formulation of the extended Kalman filtering and the robustness against the measurement errors of the resulting closed-loop system are analyzed in simulation. The simulation results demonstrate that the attitude angles are observable, however they have no robustness against the visual measurement errors. The motion of aircraft can be stabilized with the 3D visual and inertial measurements as well as the measurable Euler angles in presence of the measurement errors. Keywords: data fusion, extended Kalman filtering, filtering and estimations, vision based applications, vision based flight control. © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.