Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84706
Title: Ground Stereo Vision-based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach
Authors: Tang, Dengqing
Hu, Tianjiang
Shen, Lincheng
Zhang, Daibing
Kong, Weiwei
Low, Kin Huat
Keywords: UAV
Localization
Issue Date: 2016
Source: Tang, D., Hu, T., Shen, L., Zhang, D., Kong, W., & Low, K. H. (2016). Ground Stereo Vision-based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach. International Journal of Advanced Robotic Systems, 13(2), 67-.
Series/Report no.: International Journal of Advanced Robotic Systems
Abstract: This article aims at flying target detection and localization of a fixed-wing unmanned aerial vehicle (UAV) autonomous take-off and landing within Global Navigation Satellite System (GNSS)-denied environments. A Chan-Vese model–based approach is proposed and developed for ground stereo vision detection. Extended Kalman Filter (EKF) is fused into state estimation to reduce the localization inaccuracy caused by measurement errors of object detection and Pan-Tilt unit (PTU) attitudes. Furthermore, the region-of-interest (ROI) setting up is conducted to improve the real-time capability. The present work contributes to real-time, accurate and robust features, compared with our previous works. Both offline and online experimental results validate the effectiveness and better performances of the proposed method against the traditional triangulation-based localization algorithm.
URI: https://hdl.handle.net/10356/84706
http://hdl.handle.net/10220/41954
ISSN: 1729-8806
DOI: 10.5772/62027
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2016 Author(s). Licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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