Fixed-Wing Attitude Estimation Using Computer Vision Based Horizon Detection

The last decade has seen a dramatic expansion in the deployment of Unmanned Airborne Vehicles (UAVs) as witnessed by deployments to Bosnia, Afghanistan and Iraq. Many low-cost UAVs operate without redundant attitude sensors and are therefore highly vulnerable to the failure of such sensors. It is common for low-cost UAVs to carry a vision sensor as its primary payload. Given that a human pilot is trained to control an aircraft with respect to a visual horizon under Visual Meteorological Conditions (VMC), it is logical to suggest that a similar capability be developed for a UAV in the event of the failure of the primary attitude system. In addition, it potentially gives the capability to estimate the attitude of a gimballed camera, without specifically equipping the gimballed platform with an angular sensor. In this paper, we develop a method for estimating the flight critical parameters of pitch angle, roll angle and the three body rates using horizon detection and optical flow. We achieve this through the use of an image processing front-end to detect candidate horizon lines through the use of morphological image processing and the Hough transform. The optical flow of the image for each candidate line is calculated, and using these measurements, we are able to estimate the body rates of the aircraft. Using an Extended Kalman Filter (EFK), the candidate horizon lines are propagated and tracked through successive image frames, with statistically unlikely horizon candidates eliminated. Results are shown for a number of different datasets taken with cameras ranging from low-cost webcams to high-quality machine vision cameras are presented. Preliminary results show that although the front-end is adequate in many different scenarios, utilising temporal information results in a more robust performance of the detection algorithm which is well suited for use in attitude estimation.