Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations
This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning metho...
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MDPI AG
2024-10-01
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| author | Ihnsik Weon Soongeul Lee Juhan Yoo |
| author_facet | Ihnsik Weon Soongeul Lee Juhan Yoo |
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| description | This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors. |
| format | Article |
| id | doaj-art-5fdbc3e17f2e4c488fa2935c482becd5 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
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| spelling | doaj-art-5fdbc3e17f2e4c488fa2935c482becd52025-08-20T02:14:22ZengMDPI AGApplied Sciences2076-34172024-10-011421968510.3390/app14219685Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge OperationsIhnsik Weon0Soongeul Lee1Juhan Yoo2Airport Industrial Technology Research Institute, Incheon International Airport Corp, Incheon Jung-gu Airport Rd. 424-47, Incheon 22382, Republic of KoreaDepartment of Mechanical Engineering, Kyunghee University, Yongin 17104, Republic of KoreaDepartment of Computer Engineering, Semyung University, Jecheon 02468, Republic of KoreaThis paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors.https://www.mdpi.com/2076-3417/14/21/9685semantic segmentation3D LiDARpoint cloudsreal timePointNetobject detection |
| spellingShingle | Ihnsik Weon Soongeul Lee Juhan Yoo Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations Applied Sciences semantic segmentation 3D LiDAR point clouds real time PointNet object detection |
| title | Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations |
| title_full | Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations |
| title_fullStr | Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations |
| title_full_unstemmed | Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations |
| title_short | Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations |
| title_sort | real time semantic segmentation of 3d lidar point clouds for aircraft engine detection in autonomous jetbridge operations |
| topic | semantic segmentation 3D LiDAR point clouds real time PointNet object detection |
| url | https://www.mdpi.com/2076-3417/14/21/9685 |
| work_keys_str_mv | AT ihnsikweon realtimesemanticsegmentationof3dlidarpointcloudsforaircraftenginedetectioninautonomousjetbridgeoperations AT soongeullee realtimesemanticsegmentationof3dlidarpointcloudsforaircraftenginedetectioninautonomousjetbridgeoperations AT juhanyoo realtimesemanticsegmentationof3dlidarpointcloudsforaircraftenginedetectioninautonomousjetbridgeoperations |