AbstractThis paper describes a novel approach for vision-based passive navigation of unmanned aerial vehicles (UAVs) suitable for use in outdoor environments. The researchers chose a number of coordinate points on a flat earth model as waypoints. At each waypoint, a number of objects were chosen as landmarks which provided a unique polygonal constellation. Features of these landmarks and waypoints were computed in advance and stored in the database. A 6 degree of freedom kinematic model of a UAV flew from one waypoint to the next waypoint in a detailed simulation which included real aerial imagery. An image of the terrain was captured while approaching the waypoint. An illumination, scale, and rotation invariant algorithm was used to extract landmarks and waypoint features. These features were compared with those in the database. Position drift was computed at each waypoint and used to update the current position of the UAV prior to heading towards the next waypoint. The drift calculated by the vision-based algorithm was used to estimate the error caused primarily by wind and thus estimate the wind speed and direction. Experiments with both computer generated images and real images taken from the UAV flight trial have demonstrated the technique in the presence of wind and Gaussian noise. These results show accuracy of the drift computation algorithm and reliability of the feature matching algorithm under various environmental conditions. These algorithms were compared against other popular algorithms in the field and demonstrate higher performance.
An Algorithm for Autonomous Aerial Navigation Using Landmarks
Journal of aerospace engineering ; 29 , 3
2016
Article (Journal)
English
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