Deep learning for vision-based micro aerial vehicle autonomous landing

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
42 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)171-185
Number of pages15
JournalInternational Journal of Micro Air Vehicles
Issue number2
Early online date16 May 2018
Publication statusPublished - 30 Jun 2018


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

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