A range image for micro UAV (unmanned air vehicle) collision avoidance is derived by processing a sequence of conventional
images from a single camera on board the UAV. The range image will warn of looming collisions immediately ahead
and also provide the 3-D situational awareness over a wide field of view needed for semi-autonomous or autonomous
operation of the UAV. This single-camera technique is potentially applicable for other robotic vehicles that may not be
large enough for two-camera stereo. The range image is generated by tracking the motion of scene detail along optic flow
lines. Performance is estimated in terms of the minimum and maximum ranges of scene detail that can be sensed as a function
of its position within the field of view.
Human vision derives and uses an entirely different type and quantity of visual information than physical vision sensors. Autonomous visual systems that seek to emulate the outstanding capability of human vision should also use this very different information. It is shown how this information can be applied in a micro UAV to sense range for collision avoidance.
KEYWORDS: Cameras, Optical flow, 3D modeling, Vision geometry, Polonium, 3D image processing, 3D vision, Image processing, Visual process modeling, Transform theory
In many cases much more information can be extracted from two different images of a common object scene than from just a single image. One important example is the derivation of the 3-D structure of the scene from a stereo pair of images.
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