A fast and adaptive method for estimating UAV attitude from the visual horizon

This study describes a novel method for automatically obtaining the attitude of an aircraft from the visual horizon. A wide-angle view of the environment, including the visual horizon, is captured and the input images are classified into fuzzy sky and ground regions using the spectral and intensity properties of the pixels. The classifier is updated continuously using an online reinforcement strategy and is therefore able to adapt to the changing appearance of the sky and ground, without requiring prior training offline. A novel approach to obtaining the attitude of the aircraft from the classified images is described, which is reliable, accurate, and computationally efficient to implement. This method is therefore suited to real-time operation and we present results from flight tests that demonstrate the ability of this vision-based approach to outperform an inexpensive inertial system.