Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study
Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a w...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7550 |
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| author | Yi-Chun Lin Jidong Liu Yi-Ting Cheng Seyyed Meghdad Hasheminasab Timothy Wells Darcy Bullock Ayman Habib |
| author_facet | Yi-Chun Lin Jidong Liu Yi-Ting Cheng Seyyed Meghdad Hasheminasab Timothy Wells Darcy Bullock Ayman Habib |
| author_sort | Yi-Chun Lin |
| collection | DOAJ |
| description | Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours. |
| first_indexed | 2024-03-10T05:04:52Z |
| format | Article |
| id | doaj.art-3873447d8240469aaddec528a0c7281d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:04:52Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3873447d8240469aaddec528a0c7281d2023-11-23T01:25:23ZengMDPI AGSensors1424-82202021-11-012122755010.3390/s21227550Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case StudyYi-Chun Lin0Jidong Liu1Yi-Ting Cheng2Seyyed Meghdad Hasheminasab3Timothy Wells4Darcy Bullock5Ayman Habib6Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USAIndiana Department of Transportation Research and Development, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USACollecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.https://www.mdpi.com/1424-8220/21/22/7550bridge evaluationinfrastructure inspectionas-built databridge deck thicknessmobile LiDARregistration |
| spellingShingle | Yi-Chun Lin Jidong Liu Yi-Ting Cheng Seyyed Meghdad Hasheminasab Timothy Wells Darcy Bullock Ayman Habib Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study Sensors bridge evaluation infrastructure inspection as-built data bridge deck thickness mobile LiDAR registration |
| title | Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study |
| title_full | Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study |
| title_fullStr | Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study |
| title_full_unstemmed | Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study |
| title_short | Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study |
| title_sort | processing strategy and comparative performance of different mobile lidar system grades for bridge monitoring a case study |
| topic | bridge evaluation infrastructure inspection as-built data bridge deck thickness mobile LiDAR registration |
| url | https://www.mdpi.com/1424-8220/21/22/7550 |
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