Daniel Peter Bland

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algorithm is developed that makes the best use of color and intensity information in an image to determine the position and orientation of the horizon, and infer the aircraft's attitude. The technique is accurate, reliable, and fully capable of real-time operation. Furthermore, it can be incorporated into any existing vision system, irrespective of the way(More)
—This study describes a novel, vision-based system for guidance of UAVs. The system uses two cameras, each associated with a specially-shaped reflective surface, to obtain stereo information on the height above ground and the distances to potential obstacles. The camera-mirror system has the advantage that it remaps the world onto a cylindrical coordinate(More)
— This study describes a novel, vision-based system for guidance of UAVs. The system uses two coaxially aligned cameras, each associated with a specially-shaped reflective surface, to obtain stereo information on the height above ground and the distances to potential obstacles. The camera-mirror system has the advantage that it remaps the world onto a(More)
1 We present a technique for measuring, controlling, and stabilizing the attitude of a UAV by using a camera to monitor the visual horizon. A vision-based algorithm incorporating color and intensity information is used to detect the horizon by segmenting the ground from the sky. The attitude of the aircraft is then measured using the position, shape, and(More)
This study describes a novel, vision-based method for automatically obtaining the 3-DOF attitude of an aircraft. A very wide-angle view of the environment is captured and the input images are classified into fuzzy sky and ground regions using the spectral and intensity properties of the pixels. A novel approach to obtaining the 2-DOF attitude of the(More)
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