Abstract
We proposed a vision-based methodology as an aid for an unmanned aerial vehicle (UAV) landing on a previously unsurveyed area. When the UAV was commanded to perform a landing mission in an unknown airfield, the learning procedure was activated to extract the surface features for learning the obstacle appearance. After the learning process, while hovering the UAV above the potential landing spot, the vision system would be able to predict the roughness value for confidence in a safe landing. Finally, using hybrid optical flow technology for motion estimation, we successfully carried out the UAV landing without a predefined target. Our work combines a well-equipped flight control system with the proposed vision system to yield more practical versatility for UAV applications.
Highlights
Unmanned aerial vehicles (UAVs) are widely used in many fields, from military to civilian to commercial
We conducted the UAV landing with a hybrid optical flow scheme
We proposed a vision-aided system to aid the UAV landing in an unsurveyed environment
Summary
Unmanned aerial vehicles (UAVs) are widely used in many fields, from military to civilian to commercial. The planar area can be extracted through the homography estimation.[8,9] Without the need for a region extraction process, the roughness estimation from the optical flow field was proposed to measure the planarity of the surface.[10] In. After determining an adequate landing site in an unvisited area, the follow-up is to complete the landing process guided by either the positioning system [e.g., global positioning system (GPS)] or the vision-based motion estimation system. The major function of the proposed system is to classify the obstacle appearance on the ground and provide an accurate measure of motion during the landing To achieve these aims, we introduced the SSL to model the relationship between visual appearance and surface roughness and developed a classifier to determine if the land is safe for landing by recognizing the predicted roughness (yes/no question).
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