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15 March 2019 Deep learning for UAV autonomous landing based on self-built image dataset
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Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110412I (2019) https://doi.org/10.1117/12.2522751
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
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.
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Yinbo Xu, Yongwei Zhang, Huan Liu, and Xiangke Wang "Deep learning for UAV autonomous landing based on self-built image dataset", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412I (15 March 2019); https://doi.org/10.1117/12.2522751
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