dc.contributor.authorTang, Dengqing
dc.contributor.authorHu, Tianjiang
dc.contributor.authorShen, Lincheng
dc.contributor.authorZhang, Daibing
dc.contributor.authorKong, Weiwei
dc.contributor.authorLow, Kin Huat
dc.identifier.citationTang, 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-.en_US
dc.description.abstractThis 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.en_US
dc.format.extent14 p.en_US
dc.relation.ispartofseriesInternational Journal of Advanced Robotic Systemsen_US
dc.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.en_US
dc.titleGround Stereo Vision-based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approachen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.versionPublished versionen_US

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