Open AccessGround Stereo Vision-Based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach
Author(s) -
Dengqing Tang,
Tianjiang Hu,
Lincheng Shen,
Daibing Zhang,
Weiwei Kong,
Kin Huat Low
Publication year - 2016
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/62027
Subject(s) - computer science , computer vision , artificial intelligence , extended kalman filter , gnss applications , kalman filter , stereopsis , triangulation , tilt (camera) , global positioning system , engineering , geography , telecommunications , cartography , mechanical engineering
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
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