Obstacle detection for aircraft based on layered model

Dazhi Zhang; Shichun Peng; Yongtao Wang; Jinwen Tian

doi:10.1117/12.716952

24 October 2006 Obstacle detection for aircraft based on layered model

Dazhi Zhang, Shichun Peng, Yongtao Wang, Jinwen Tian

Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 635718 (2006) https://doi.org/10.1117/12.716952
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

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.

Citation Download Citation

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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KEYWORDS

Expectation maximization algorithms

Motion models

3D modeling

Visual process modeling

Data modeling

Sensors

Affine motion model

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