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Proceedings Paper

Deep learning for UAV autonomous landing based on self-built image dataset
Author(s): Yinbo Xu; Yongwei Zhang; Huan Liu; Xiangke Wang
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Paper Abstract

An end-to-end deep learning (DL) control model is proposed to solve autonomous landing problem of the quadrotor in way of supervised learning. Traditional methods mainly focus on getting the relative position of the quadrotor through GPS signal which is not always reliable or position-based vision servo (PBVS) methods. In this paper, we have constructed a deep neural network based on convolutional neural network(CNN) whose input is raw image. A monocular camera is used as only sensor to capture down-looking image which contains landing area. To train our deep neural network, we have used our self-built image dataset. After training phase, the well-trained control model is tested and the results perform well. Light changes and background interferences have little influence on the model`s performance, which shows the robustness and adaptation of our deep learning model.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412I (15 March 2019); doi: 10.1117/12.2522751
Show Author Affiliations
Yinbo Xu, National Univ. of Defense Technology (China)
Yongwei Zhang, National Univ. of Defense Technology (China)
Huan Liu, Jiuquan Satellite Launch Ctr. (China)
Xiangke Wang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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