Research on Aircraft Docking Guidance Localization Based on LiDAR Point Cloud Completion

WEI Ning; LI Minglei; CHEN Guangyong; YE Fangzhou

doi:10.12000/JR25002

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WEI Ning, LI Minglei, CHEN Guangyong, et al. Research on aircraft docking guidance localization based on LiDAR point cloud completion[J]. Journal of Radars, in press. doi: 10.12000/JR25002

Citation:

WEI Ning, LI Minglei, CHEN Guangyong, et al. Research on aircraft docking guidance localization based on LiDAR point cloud completion[J]. Journal of Radars, in press. doi: 10.12000/JR25002

WEI Ning, LI Minglei, CHEN Guangyong, et al. Research on aircraft docking guidance localization based on LiDAR point cloud completion[J]. Journal of Radars, in press. doi: 10.12000/JR25002

Citation:

WEI Ning, LI Minglei, CHEN Guangyong, et al. Research on aircraft docking guidance localization based on LiDAR point cloud completion[J]. Journal of Radars, in press. doi: 10.12000/JR25002

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Research on Aircraft Docking Guidance Localization Based on LiDAR Point Cloud Completion

DOI: 10.12000/JR25002 CSTR: 32380.14.JR25002

1.
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
China National Astronautical Radio Electronics Research Institute, Shanghai 200241, China

Funds: The National Natural Science Foundation of China (42271343)

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Corresponding author: LI Minglei minglei_li@nuaa.edu.cn
Received Date: 2025-01-03
Rev Recd Date: 2025-04-01

Available Online: 2025-04-07

Abstract

Abstract

The airport docking guidance system is essential for enhancing airport safety and operational efficiency. This study introduces a deep learning-based point cloud completion network designed for accurate aircraft localization using LiDAR technology. Initially, the aircraft parking process is simulated in a realistic virtual environment to generate complete point cloud data. Subsequently, partial point clouds caused by occlusions or sensor limitations are processed through the proposed network to reconstruct their complete geometric structures. Then the restored point cloud is aligned with a predefined aircraft model, enabling precise calculation of the aircraft's center coordinates in the simulated coordinate system through spatial transformation. Experimental results demonstrate that the network effectively recovers structural details from incomplete point clouds, enabling accurate computation of aircraft centroid coordinates. This approach achieves high-precision position detection for aircraft during docking, showing significant potential for practical airport applications. The codes are available at: https://www.scidb.cn/anonymous/UXZFZkFm.
- Docking guidance system,
- Three-dimensional laser scanning,
- Point cloud completion,
- Point cloud registration,
- Deep learning

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References

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