Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Fu, Changhong and Carrio Fernández, Adrián and Olivares Méndez, Miguel Ángel and Campoy Cervera, Pascual (2014). Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles. In: "2014 International Conference on Unmanned Aircraft Systems (ICUAS)", 27 - 30 May 2014, Orlando, FL, USA. ISBN 978-1-4799-2376-2. pp. 649-655.

Description

Title: Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles
Author/s:
  • Fu, Changhong
  • Carrio Fernández, Adrián
  • Olivares Méndez, Miguel Ángel
  • Campoy Cervera, Pascual
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2014 International Conference on Unmanned Aircraft Systems (ICUAS)
Event Dates: 27 - 30 May 2014
Event Location: Orlando, FL, USA
Title of Book: 2014 International Conference on Unmanned Aircraft Systems (ICUAS)
Date: 2014
ISBN: 978-1-4799-2376-2
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
UPM's Research Group: Computer Vision CVG
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2010-20751-C02-01CICYTUnspecifiedUnspecified
FP7UnspecifiedUnspecifiedUnspecifiedUECIMUAVS - USA and Europe Cooperation in Mini UAVs. IRSES PROYECT

More information

Item ID: 37649
DC Identifier: http://oa.upm.es/37649/
OAI Identifier: oai:oa.upm.es:37649
Official URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6835384
Deposited by: Memoria Investigacion
Deposited on: 14 Sep 2015 16:33
Last Modified: 15 Sep 2015 14:52
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