1 VISION-ONLY AIRCRAFT FLIGHT CONTROL Christophe De Wagter 1 Delft University of Technology, Delft, The Netherlands Alison A. Proctor 2 and Eric N. Johnson 3 Georgia Institute of Technology, Atlanta, GA, 30332 1 Graduate Research Assistant Email: C.deWagter@student.tudelft.nl 2 Graduate Research Assistant Email: alison_proctor@ae.gatech.edu 3 Lockheed Martin Assistant Professor of Avionics Integration Email: Eric.Johnson@aerospace.gatech.edu Abstract Building aircraft with navigation and control systems that can complete flight tasks is complex, and often involves integrating information from multiple sensors to estimate the state of the vehicle. This paper describes a method, in which a glider can fly from a starting point to a predetermined end location (target) precisely using vision only. Using vision to control an aircraft represents a unique challenge, partially due to the high rate of images required in order to maintain tracking and to keep the glider on target in a moving air mass. Second, absolute distance and angle measurements to the target are not readily available when the glider does not have independent measurements of its own position. The method presented here uses an integral image representation of the video input for the analysis. The integral image, which is obtained by integrating the pixel intensities across the image, is reduced to a probable target location by performing a cascade of feature matching functions. The cascade is designed to eliminate the majority of the potential targets in a first pruning using computationally inexpensive process. Then, the more exact and computationally expensive processes are used on the few remaining candidates; thereby, dramatically decreasing the processing required per image. The navigation algorithms presented in this paper use a Kalman filter to estimate attitude and glideslope required based on measurements of the target in the image. The effectiveness of the algorithms is demonstrated through simulation of a small glider instrumented with only a simulated camera. Nomenclature ∆ angle between the camera centerline and the line through the center of the window δ actuator deflection γ track angle θ pitch angle φ roll angle ψ heading angle with respect to the desired flight path C lδa aileron effectiveness C mδe elevator effectiveness C nδr rudder effectiveness C INT model error integration coefficient d distance to the window dh vertical off-track position dt discrete time step dy lateral off-track position EKF Extended Kalman Filter FOV Field of View GN&C Guidance Navigation and Control S width of window I Grayscale Image Intensity II Integral Image of Pixel Intensities k iteration number