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首页> 外文期刊>International journal of micro air vehicles >Deep learning for vision-based micro aerial vehicle autonomous landing
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Deep learning for vision-based micro aerial vehicle autonomous landing

机译：深度学习基于视觉的微型飞行器自主着陆

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摘要

Vision-based techniques are widely used in micro aerial vehicle autonomous landing systems. Existing vision-based autonomous landing schemes tend to detect specific landing landmarks by identifying their straightforward visual features such as shapes and colors. Though efficient to compute, these schemes only apply to landmarks with limited variability and require strict environmental conditions such as consistent lighting. To overcome these limitations, we propose an end-to-end landmark detection system based on a deep convolutional neural network, which not only easily scales up to a larger number of various landmarks but also exhibit robustness to different lighting conditions. Furthermore, we propose a separative implementation strategy which conducts convolutional neural network training and detection on different hardware platforms separately, i.e. a graphics processing unit work station and a micro aerial vehicle on-board system, subject to their specific implementation requirements. To evaluate the performance of our framework, we test it on synthesized scenarios and real-world videos captured by a quadrotor on-board camera. Experimental results validate that the proposed vision-based autonomous landing system is robust to landmark variability in different backgrounds and lighting situations.
机译：基于视觉的技术广泛用于微型飞行器自主着陆系统。现有的基于视觉的自主着陆方案倾向于通过识别特定的着陆地标直接的视觉特征（例如形状和颜色）来检测它们。尽管计算效率很高，但是这些方案仅适用于变化有限的地标，并且需要严格的环境条件，例如一致的照明。为了克服这些限制，我们提出了一种基于深度卷积神经网络的端到端地标检测系统，该系统不仅可以轻松扩展到更多的各种地标，而且还可以在不同的照明条件下表现出鲁棒性。此外，我们提出了一种单独的实现策略，根据其具体实现要求，在不同的硬件平台上分别进行卷积神经网络训练和检测，即图形处理单元工作站和微型飞机机载系统。为了评估我们框架的性能，我们在合成场景和四旋翼机载摄像头捕获的真实视频中对其进行了测试。实验结果验证了所提出的基于视觉的自主着陆系统对于不同背景和光照条件下的地标可变性具有鲁棒性。

著录项

  • 来源
    《International journal of micro air vehicles》 |2018年第2期|171-185|共15页
  • 作者

    Yu Leijian; Luo Cai; Yu Xingrui; Jiang Xiangyuan; Yang Erfu; Luo Chunbo; Ren Peng;

  • 作者单位

    China Univ Petr East China, Coll Informat & Control Engn, 66 Changjiang West Rd, Qingdao 266580, Peoples R China;

    China Univ Petr East China, Coll Mech & Elect Engn, Qingdao, Peoples R China;

    China Univ Petr East China, Coll Informat & Control Engn, 66 Changjiang West Rd, Qingdao 266580, Peoples R China;

    China Univ Petr East China, Coll Informat & Control Engn, 66 Changjiang West Rd, Qingdao 266580, Peoples R China;

    Strathclyde Space Inst, Dept Design Mfg & Engn Management, Space Mechatron Syst Technol Lab, Glasgow, Lanark, Scotland;

    Univ Exeter, Dept Comp Sci, Coll Engn Math & Phys Sci, Exeter, Devon, England;

    China Univ Petr East China, Coll Informat & Control Engn, 66 Changjiang West Rd, Qingdao 266580, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Micro aerial vehicle; vision-based autonomous landing; convolutional neural networks;

    机译：微型飞行器;基于视觉的自主着陆;卷积神经网络;

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