Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud
Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/22/3702 |
| _version_ | 1797548206370848768 |
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| author | Amila Karunathilake Ryohei Honma Yasuhito Niina |
| author_facet | Amila Karunathilake Ryohei Honma Yasuhito Niina |
| author_sort | Amila Karunathilake |
| collection | DOAJ |
| description | Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring. |
| first_indexed | 2024-03-10T14:56:06Z |
| format | Article |
| id | doaj.art-b1a05de3dafe46b6909b3c46bc20bd55 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:56:06Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b1a05de3dafe46b6909b3c46bc20bd552023-11-20T20:37:58ZengMDPI AGRemote Sensing2072-42922020-11-011222370210.3390/rs12223702Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point CloudAmila Karunathilake0Ryohei Honma1Yasuhito Niina2Advanced Technologies Research Laboratory, Asia Air Survey Co. Ltd, 1-2-2 Manpukuji, Asao-ku, Kawasaki-shi 215-0004, Kanagawa Prefecture, JapanAdvanced Technologies Research Laboratory, Asia Air Survey Co. Ltd, 1-2-2 Manpukuji, Asao-ku, Kawasaki-shi 215-0004, Kanagawa Prefecture, JapanAdvanced Technologies Research Laboratory, Asia Air Survey Co. Ltd, 1-2-2 Manpukuji, Asao-ku, Kawasaki-shi 215-0004, Kanagawa Prefecture, JapanMobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring.https://www.mdpi.com/2072-4292/12/22/3702mobile laser scanning (MLS)railway infrastructure monitoringmodel fitting3D point cloud |
| spellingShingle | Amila Karunathilake Ryohei Honma Yasuhito Niina Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud Remote Sensing mobile laser scanning (MLS) railway infrastructure monitoring model fitting 3D point cloud |
| title | Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud |
| title_full | Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud |
| title_fullStr | Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud |
| title_full_unstemmed | Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud |
| title_short | Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud |
| title_sort | self organized model fitting method for railway structures monitoring using lidar point cloud |
| topic | mobile laser scanning (MLS) railway infrastructure monitoring model fitting 3D point cloud |
| url | https://www.mdpi.com/2072-4292/12/22/3702 |
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