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Robotics 2017, 6(2), 6; doi:10.3390/robotics6020006

A New Combined Vision Technique for Micro Aerial Vehicle Pose Estimation

1,2,* , 1,3,4,* , 1
,
1,3,4
,
1,3
and
2,*
1
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
2
Electrical & Computer Engineering, George Mason University, Fairfax, VA 22030, USA
3
Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
4
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Kah Bin Lim and Chui Chee Kong
Received: 5 December 2016 / Revised: 20 February 2017 / Accepted: 23 March 2017 / Published: 28 March 2017
(This article belongs to the Special Issue Robotics and 3D Vision)
View Full-Text   |   Download PDF [4718 KB, uploaded 28 March 2017]   |  

Abstract

In this work, a new combined vision technique (CVT) is proposed, comprehensively developed, and experimentally tested for stable, precise unmanned micro aerial vehicle (MAV) pose estimation. The CVT combines two measurement methods (multi- and mono-view) based on different constraint conditions. These constraints are considered simultaneously by the particle filter framework to improve the accuracy of visual positioning. The framework, which is driven by an onboard inertial module, takes the positioning results from the visual system as measurements and updates the vehicle state. Moreover, experimental testing and data analysis have been carried out to verify the proposed algorithm, including multi-camera configuration, design and assembly of MAV systems, and the marker detection and matching between different views. Our results indicated that the combined vision technique is very attractive for high-performance MAV pose estimation. View Full-Text
Keywords: micro aerial vehicle (MAV); triangulation; perspective-three-point (P3P); pose estimation; particle filter micro aerial vehicle (MAV); triangulation; perspective-three-point (P3P); pose estimation; particle filter
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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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Yuan, H.; Xiao, C.; Xiu, S.; Wen, Y.; Zhou, C.; Li, Q. A New Combined Vision Technique for Micro Aerial Vehicle Pose Estimation. Robotics 2017, 6, 6.

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