A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
The increasing demand for 3D geospatial data is driving the development of new products. Laser scanners are becoming more mobile, affordable, and user-friendly. With the increased number of systems and service providers on the market, the scope of mobile laser scanning (MLS) applications has expande...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/4/857 |
| _version_ | 1797476806898483200 |
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| author | Slaven Kalenjuk Werner Lienhart |
| author_facet | Slaven Kalenjuk Werner Lienhart |
| author_sort | Slaven Kalenjuk |
| collection | DOAJ |
| description | The increasing demand for 3D geospatial data is driving the development of new products. Laser scanners are becoming more mobile, affordable, and user-friendly. With the increased number of systems and service providers on the market, the scope of mobile laser scanning (MLS) applications has expanded dramatically in recent years. However, quality control measures are not keeping pace with the flood of data. Evaluating MLS surveys of long corridors with control points is expensive and, as a result, is frequently neglected. However, information on data quality is crucial, particularly for safety-critical tasks in infrastructure engineering. In this paper, we propose an efficient method for the quality control of MLS point clouds. Based on point cloud discrepancies, we estimate the transformation parameters profile-wise. The elegance of the approach lies in its ability to detect and correct small, high-frequency errors. To demonstrate its potential, we apply the method to real-world data collected with two high-end, car-mounted MLSs. The field study revealed tremendous systematic variations of two passes following tunnels, varied co-registration quality of two scanners, and local inhomogeneities due to poor positioning quality. In each case, the method succeeds in mitigating errors and thus in enhancing quality. |
| first_indexed | 2024-03-09T21:08:57Z |
| format | Article |
| id | doaj.art-2ad5ca7593f34bbeb05c448cd909d483 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:57Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2ad5ca7593f34bbeb05c448cd909d4832023-11-23T21:53:13ZengMDPI AGRemote Sensing2072-42922022-02-0114485710.3390/rs14040857A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning DataSlaven Kalenjuk0Werner Lienhart1Institute of Engineering Geodesy and Measurement Systems, Graz University of Technology, Steyrergasse 30, 8010 Graz, AustriaInstitute of Engineering Geodesy and Measurement Systems, Graz University of Technology, Steyrergasse 30, 8010 Graz, AustriaThe increasing demand for 3D geospatial data is driving the development of new products. Laser scanners are becoming more mobile, affordable, and user-friendly. With the increased number of systems and service providers on the market, the scope of mobile laser scanning (MLS) applications has expanded dramatically in recent years. However, quality control measures are not keeping pace with the flood of data. Evaluating MLS surveys of long corridors with control points is expensive and, as a result, is frequently neglected. However, information on data quality is crucial, particularly for safety-critical tasks in infrastructure engineering. In this paper, we propose an efficient method for the quality control of MLS point clouds. Based on point cloud discrepancies, we estimate the transformation parameters profile-wise. The elegance of the approach lies in its ability to detect and correct small, high-frequency errors. To demonstrate its potential, we apply the method to real-world data collected with two high-end, car-mounted MLSs. The field study revealed tremendous systematic variations of two passes following tunnels, varied co-registration quality of two scanners, and local inhomogeneities due to poor positioning quality. In each case, the method succeeds in mitigating errors and thus in enhancing quality.https://www.mdpi.com/2072-4292/14/4/857mobile laser scanningpoint cloudsquality controlgeoreferencingsystematic errorstransformation parameters |
| spellingShingle | Slaven Kalenjuk Werner Lienhart A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data Remote Sensing mobile laser scanning point clouds quality control georeferencing systematic errors transformation parameters |
| title | A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data |
| title_full | A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data |
| title_fullStr | A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data |
| title_full_unstemmed | A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data |
| title_short | A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data |
| title_sort | method for efficient quality control and enhancement of mobile laser scanning data |
| topic | mobile laser scanning point clouds quality control georeferencing systematic errors transformation parameters |
| url | https://www.mdpi.com/2072-4292/14/4/857 |
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