Vision-Based Self-Motion Estimation in a Fixed-Wing Aerial Vehicle

Abstract

This paper describes a complete algorithm to estimate the motion of a fixed-wing aircraft given a series of digitized flight images. The algorithm was designed for fixed-wing aircraft because carefully procured flight images and corresponding navigation data were available to us for testing. After image pre-processing, optic flow data is determined by automatically finding and tracking good features between pairs of images. The image coordinates of matched features are then processed by a rigid-object linear optic flow-motion estimation algorithm. Input factors are weighed to provide good testing techniques. Error analysis is performed with simulation data keeping these factors in mind to determine the effectiveness of the optic flow algorithm. The output of this program is an estimate of rotation and translation of the imaged environment in relation to the camera, and thereby the airplane. Real flight images from NASA test flights are used to confirm the accuracy of the algorithm. Where possible, the estimated motion parameters are compared with recorded flight instrument data to confirm the correctness of the algorithm. Results show that the algorithm is accurate to within a degree provided that enough optic flow feature points are tracked.