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Drones 2018, 2(2), 15; https://doi.org/10.3390/drones2020015

Vision-Based Autonomous Landing of a Quadrotor on the Perturbed Deck of an Unmanned Surface Vehicle

Autonomous Marine Systems Research Group, School of Engineering, University of Plymouth, Plymouth PL4 8AA, UK
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Received: 5 March 2018 / Revised: 11 April 2018 / Accepted: 11 April 2018 / Published: 14 April 2018
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Abstract

Autonomous landing on the deck of an unmanned surface vehicle (USV) is still a major challenge for unmanned aerial vehicles (UAVs). In this paper, a fiducial marker is located on the platform so as to facilitate the task since it is possible to retrieve its six-degrees of freedom relative-pose in an easy way. To compensate interruption in the marker’s observations, an extended Kalman filter (EKF) estimates the current USV’s position with reference to the last known position. Validation experiments have been performed in a simulated environment under various marine conditions. The results confirmed that the EKF provides estimates accurate enough to direct the UAV in proximity of the autonomous vessel such that the marker becomes visible again. Using only the odometry and the inertial measurements for the estimation, this method is found to be applicable even under adverse weather conditions in the absence of the global positioning system. View Full-Text
Keywords: unmanned aerial vehicle; position control; computer vision; image processing unmanned aerial vehicle; position control; computer vision; image processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Polvara, R.; Sharma, S.; Wan, J.; Manning, A.; Sutton, R. Vision-Based Autonomous Landing of a Quadrotor on the Perturbed Deck of an Unmanned Surface Vehicle. Drones 2018, 2, 15.

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