Computer Vision Based General Object Following for GPS-denied Multirotor Unmanned Vehicles

Pestana Puerta, Jesús; Sanchez Lopez, Jose Luis; Saripalli, Srikanth y Campoy Cervera, Pascual (2014). Computer Vision Based General Object Following for GPS-denied Multirotor Unmanned Vehicles. En: "2014 American Control Conference (ACC)", June 4-6, 2014, Portland, Oregon, USA. ISBN 9781479932740. pp. 1886-1891. https://doi.org/10.1109/ACC.2014.6858831.

Descripción

Título: Computer Vision Based General Object Following for GPS-denied Multirotor Unmanned Vehicles
Autor/es:
  • Pestana Puerta, Jesús
  • Sanchez Lopez, Jose Luis
  • Saripalli, Srikanth
  • Campoy Cervera, Pascual
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2014 American Control Conference (ACC)
Fechas del Evento: June 4-6, 2014
Lugar del Evento: Portland, Oregon, USA
Título del Libro: 2014 American Control Conference (ACC)
Fecha: Junio 2014
ISBN: 9781479932740
Materias:
Palabras Clave Informales: quadrator control, visual servoing, object following, UAV vision based control
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The motivation of this research is to show that visual based object tracking and following is reliable using a cheap GPS-denied multirotor platform such as the AR Drone 2.0. Our architecture allows the user to specify an object in the image that the robot has to follow from an approximate constant distance. At the current stage of our development, in the event of image tracking loss the system starts to hover and waits for the image tracking recovery or second detection, which requires the usage of odometry measurements for self stabilization. During the following task, our software utilizes the forward-facing camera images and part of the IMU data to calculate the references for the four on-board low-level control loops. To obtain a stronger wind disturbance rejection and an improved navigation performance, a yaw heading reference based on the IMU data is internally kept and updated by our control algorithm. We validate the architecture using an AR Drone 2.0 and the OpenTLD tracker in outdoor suburban areas. The experimental tests have shown robustness against wind perturbations, target occlusion and illumination changes, and the system's capability to track a great variety of objects present on suburban areas, for instance: walking or running people, windows, AC machines, static and moving cars and plants.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaDPI2010-20751-C02-01Sin especificarSin especificarSin especificar
Sin especificarPIRSES-GA-2010Sin especificarSin especificarSin especificar

Más información

ID de Registro: 45703
Identificador DC: http://oa.upm.es/45703/
Identificador OAI: oai:oa.upm.es:45703
Identificador DOI: 10.1109/ACC.2014.6858831
URL Oficial: http://acc2014.a2c2.org/
Depositado por: Memoria Investigacion
Depositado el: 03 May 2017 15:55
Ultima Modificación: 04 May 2017 07:35
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