Visual model feature tracking for UAV control
Mondragon, Ivan F. , Campoy, Pascual , Correa, Juan F. , & Mejias, Luis (2007) Visual model feature tracking for UAV control. In IEEE International Symposium on Intelligent Signal Processing, 3-5 October, Alcala de Henares, Spain.
This paper explores the possibilities to use robust object tracking algorithms based on visual model features as generator of visual references for UAV control. The best performance SIFT algorithm is used to detect salient points in each image, then a projective transformation for evaluating the 3D transformation is obtained using a version of the RANSAC algorithm in order to fit a series of Keypoints pairs matched to the transformation rejecting the corrupted data. The system has been tested using diverse image sequences showing its capability to track objects significantly changed in scale, position, rotation, generating at the same time velocity references to the UAV flight controller. The robustness our approach has also been validated using images taken from real flights showing noise and lighting distortions. The results presented are promising in order to be used as reference generator for the control system.
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|Item Type:||Conference Paper|
|Keywords:||Autonomous Helicopter, Vision, Based Navigation|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)|
|Divisions:||Current > Research Centres > Australian Research Centre for Aerospace Automation|
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
|Copyright Owner:||Copyright 2007 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||17 Oct 2007|
|Last Modified:||29 Feb 2012 23:34|
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