Research on machine vision-based unmanned aerial vehicle landing technology

Chao Xie

doi:10.1117/12.3027188

1 April 2024 Research on machine vision-based unmanned aerial vehicle landing technology

Chao Xie

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 130770T (2024) https://doi.org/10.1117/12.3027188
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

Unmanned aerial vehicles (UAVs) are widely utilized in various fields. However, during mission execution, the occurrence of mechanical failures or subsystem malfunctions, including fuel shortages, may result in the UAV landing in an unspecified area. Additionally, in emergency situations, the UAV may be forced to land in densely populated areas or treacherous terrains. Careful consideration of suitable landing points before touchdown is crucial, making research on UAV landing technology a significant contemporary topic. Current traditional landing techniques, relying on satellite navigation and inertial navigation, face challenges in adapting to complex environments and terrain interference with satellite signals. The application of machine vision-based technology to UAVs presents a promising solution, enabling autonomous landings in signal-deprived scenarios. Therefore, this paper investigates machine vision-based UAV landing technology by processing images captured by the UAV of the terrain. A series of image processing steps are applied to reconstruct the terrain in three dimensions, generating a point cloud map of the terrain. Through the analysis of this map, a range of methods is employed to determine the optimal landing points. The study successfully achieves the three-dimensional reconstruction of the terrain and identifies the optimal landing points, conducting experiments in complex terrains to successfully locate the best landing points and accomplish autonomous UAV landings. This research leverages numerous algorithms to optimize the terrain map, resulting in a more comprehensive point cloud. By combining two landing strategies, the study achieves more precise landing points.

Citation Download Citation

Chao Xie "Research on machine vision-based unmanned aerial vehicle landing technology", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 130770T (1 April 2024); https://doi.org/10.1117/12.3027188

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KEYWORDS

Unmanned aerial vehicles

Point clouds

Cameras

Image processing

Matrices

Feature extraction

3D modeling

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