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

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

Descripción

Título: Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles
Autor/es:
  • Fu, Changhong
  • Carrio Fernández, Adrián
  • Olivares-Mendez, Miguel A.
  • Campoy Cervera, Pascual
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2014 International Conference on Unmanned Aircraft Systems (ICUAS)
Fechas del Evento: May 27-30, 2014
Lugar del Evento: Orlando, FL, USA
Título del Libro: 2014 International Conference on Unmanned Aircraft Systems (ICUAS). Conference Proceedings
Fecha: 2014
ISBN: 978‐1‐4799‐2376‐2
Materias:
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Grupo Investigación UPM: Computer Vision Group
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaCICYT DPI2010-20751-C02-01Sin especificarSin especificarSin especificar
FP7UECIMUAVSIRSESSin especificarUSA and Europe Cooperation in Mini UAVs

Más información

ID de Registro: 37637
Identificador DC: http://oa.upm.es/37637/
Identificador OAI: oai:oa.upm.es:37637
URL Oficial: http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html%3FConf_ID%3D32188
Depositado por: Memoria Investigacion
Depositado el: 01 Mar 2016 17:28
Ultima Modificación: 02 Mar 2016 15:45
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