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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    Title :

    Obstacle detection for aircraft based on layered model


    Contributors:
    Zhang, Dazhi (author) / Peng, Shichun (author) / Wang, Yongtao (author) / Tian, Jinwen (author)

    Conference:

    Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence ; 2006 ; Beijing,China


    Published in:

    Publication date :

    2006-10-24





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English