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Proceedings Paper

Using probabilistic model as feature descriptor on a smartphone device for autonomous navigation of unmanned ground vehicles
Author(s): Alok Desai; Dah-Jye Lee
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Paper Abstract

There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device’s resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.

Paper Details

Date Published: 3 February 2014
PDF: 9 pages
Proc. SPIE 9025, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, 90250I (3 February 2014); doi: 10.1117/12.2045107
Show Author Affiliations
Alok Desai, Brigham Young Univ. (United States)
Dah-Jye Lee, Brigham Young Univ. (United States)

Published in SPIE Proceedings Vol. 9025:
Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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