Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System

Changhong, Fu; Carrio Fernández, Adrián y Campoy Cervera, Pascual (2015). Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System. En: "2015 International Conference on Unmanned Aircraft Systems (ICUAS)", June 9-12, 2015, Denver, Colorado, USA. ISBN 978-1-4799-6009-5. pp. 957-962.

Descripción

Título: Efficient Visual Odometry and Mapping for Unmanned Aerial Vehicle Using ARM-based Stereo Vision Pre-Processing System
Autor/es:
  • Changhong, Fu
  • Carrio Fernández, Adrián
  • Campoy Cervera, Pascual
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2015 International Conference on Unmanned Aircraft Systems (ICUAS)
Fechas del Evento: June 9-12, 2015
Lugar del Evento: Denver, Colorado, USA
Título del Libro: 2015 International Conference on Unmanned Aircraft Systems (ICUAS)
Fecha: 2015
ISBN: 978-1-4799-6009-5
Materias:
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Grupo Investigación UPM: Computer Vision Group (CVG)
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Visual odometry and mapping methods can provide accurate navigation and comprehensive environment (obstacle) information for autonomous flights of Unmanned Aerial Vehicle (UAV) in GPS-denied cluttered environments. This work presents a new light small-scale low-cost ARM-based stereo vision pre-processing system, which not only is used as onboard sensor to continuously estimate 6-DOF UAV pose, but also as onboard assistant computer to pre-process visual information, thereby saving more computational capability for the onboard host computer of the UAV to conduct other tasks. The visual odometry is done by one plugin specifically developed for this new system with a fixed baseline (12cm). In addition, the preprocessed infromation from this new system are sent via a Gigabit Ethernet cable to the onboard host computer of UAV for real-time environment reconstruction and obstacle detection with a octree-based 3D occupancy grid mapping approach, i.e. OctoMap. The visual algorithm is evaluated with the stereo video datasets from EuRoC Challenge III in terms of efficiency, accuracy and robustness. Finally, the new system is mounted and tested on a real quadrotor UAV to carry out the visual odometry and mapping task.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
FP7Sin especificarIRSESSin especificarUECIMUAVS: USA and Europe Cooperation in Mini UAVs
Gobierno de España2010-20751- C02-01Sin especificarSin especificarMeSOANTEN projec

Más información

ID de Registro: 42824
Identificador DC: http://oa.upm.es/42824/
Identificador OAI: oai:oa.upm.es:42824
Depositado por: Memoria Investigacion
Depositado el: 13 Jul 2016 15:40
Ultima Modificación: 14 Jul 2016 14:59
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