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

Pestana Puerta, Jesús and Sanchez Lopez, Jose Luis and Saripalli, Srikanth and Campoy Cervera, Pascual (2014). Computer Vision Based General Object Following for GPS-denied Multirotor Unmanned Vehicles. In: "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.

Description

Title: Computer Vision Based General Object Following for GPS-denied Multirotor Unmanned Vehicles
Author/s:
  • Pestana Puerta, Jesús
  • Sanchez Lopez, Jose Luis
  • Saripalli, Srikanth
  • Campoy Cervera, Pascual
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2014 American Control Conference (ACC)
Event Dates: June 4-6, 2014
Event Location: Portland, Oregon, USA
Title of Book: 2014 American Control Conference (ACC)
Date: June 2014
ISBN: 9781479932740
Subjects:
Freetext Keywords: quadrator control, visual servoing, object following, UAV vision based control
Faculty: E.T.S.I. Industriales (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2010-20751-C02-01UnspecifiedUnspecifiedUnspecified
UnspecifiedPIRSES-GA-2010UnspecifiedUnspecifiedUnspecified

More information

Item ID: 45703
DC Identifier: http://oa.upm.es/45703/
OAI Identifier: oai:oa.upm.es:45703
DOI: 10.1109/ACC.2014.6858831
Official URL: http://acc2014.a2c2.org/
Deposited by: Memoria Investigacion
Deposited on: 03 May 2017 15:55
Last Modified: 04 May 2017 07:35
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