Motion parameters estimation for autonomous aircraft from a monocular image sequence

This work presents an estimation of motion parameters for an autonomous air vehicle using the information extracted from a monocular sequence of images. The information is obtained using the feature tracking approach. The estimation process uses a recursive algorithm based on the Extended Kalman Filter with contains the dynamics of the vehicle. The vehicle used in this paper is based on the X-RAE1 RPV model, flying in an environment that is rigid and stationary, with smooth motion. The simulation results show that the EKF estimator converges rapidly to the real values of motion parameters. A simple algorithm is also developed that avoids the correspondence and occlusion problems associated with feature tracking algorithms. Copyright (C) 1998 IFAC.