A fast and adaptive method for estimating UAV attitude from the visual horizon

Moore, Richard J. D., Thurrowgood, Saul, Bland, Daniel, Soccol, Dean and Srinivasan, Mandyam V. (2011). A fast and adaptive method for estimating UAV attitude from the visual horizon. In: Nancy M. Amato, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems proceedings. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, United States, (4935-4940). 25-30 September 2011.


Author Moore, Richard J. D.
Thurrowgood, Saul
Bland, Daniel
Soccol, Dean
Srinivasan, Mandyam V.
Title of paper A fast and adaptive method for estimating UAV attitude from the visual horizon
Conference name 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Conference location San Francisco, CA, United States
Conference dates 25-30 September 2011
Proceedings title 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems proceedings   Check publisher's open access policy
Journal name IEEE International Conference on Intelligent Robots and Systems   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/IROS.2011.6048314
ISBN 9781612844541
ISSN 2153-0858
Editor Nancy M. Amato
Start page 4935
End page 4940
Total pages 6
Collection year 2012
Language eng
Abstract/Summary This study describes a novel method for automatically obtaining the attitude of an aircraft from the visual horizon. A wide-angle view of the environment, including the visual horizon, is captured and the input images are classified into fuzzy sky and ground regions using the spectral and intensity properties of the pixels. The classifier is updated continuously using an online reinforcement strategy and is therefore able to adapt to the changing appearance of the sky and ground, without requiring prior training offline. A novel approach to obtaining the attitude of the aircraft from the classified images is described, which is reliable, accurate, and computationally efficient to implement. This method is therefore suited to real-time operation and we present results from flight tests that demonstrate the ability of this vision-based approach to outperform an inexpensive inertial system.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Mon, 17 Oct 2011, 11:34:40 EST by Mr Richard Moore on behalf of Queensland Brain Institute