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Visual-based Landing Guidance System of UAV with Deep Learning Technique for Environments of Visual-detection Impairment

Minjae Lee, Sung Gyun Shin, Seungsoo Jang, Woosung Cho, Sungkyum Kim, Sangsoo Han, Chanho Choi, Jooyeon Kim, Youngmin Kim, and Song Hyun Kim*
International Journal of Control, Automation, and Systems, vol. 20, no. 5, pp.1735-1744, 2022

Abstract : Most vision-based landing algorithms cannot be applied in a severe vision-detection environment. However, the application of a deep learning technique to vision-based landing algorithm can solve the problem of a severe vision-detection environment, especially in vision-impaired environments. Based on this fact, a novel-landing concept with deep learning technique is proposed in this study. Three main techniques applied for guided landing are 1) deep learning for accurate landing mark detection; 2) location-memorized system for coping temporal failure of the landing mark detection; 3) Unmanned Aerial Vehicle control algorithm for vibration minimization in vision sensor. The proposed system successfully and accurately guided the multicopter onto the landing area without failure in vision-impaired environments. The results show that the proposed landing algorithm can overcome environmental restrictions in operating multicopters.

Keyword : Deep learning, landing mark detection, multirotor, UAV, vision-based landing guidance, visionimpairment environments.

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