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24 October 2006 Obstacle detection for aircraft based on layered model
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Abstract
An airborne vehicle such as a tactical missile must avoid obstacles like towers, tree branches, mountains and building across the flight path. So the ability to detect and locate obstacles using on-board sensors is an essential step in the autonomous navigation of aircraft low-altitude flight. This paper describes a novel method to detect and locate obstacles using a sequence of images from a passive sensor (TV, FLIR). We model 3D scenes in the field-of-view (FOV) as a collection of approximately planar layers that corresponds to the background and obstacles respectively. So each pixel within a layer can have the same 2D affine motion model which depends on the relative depth of the layer. We formulate the prior assumptions about the layers and scene within a Bayesian decision making framework which is used to automatically determine the assignment of individual pixels to layers. Then, a generalized expectation maximization (EM) method is used to find the MAP solution. Finally, simulation results demonstrate that this method is successful.
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Dazhi Zhang, Shichun Peng, Yongtao Wang, and Jinwen Tian "Obstacle detection for aircraft based on layered model", Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 635718 (24 October 2006); https://doi.org/10.1117/12.716952
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