Vertical Clearance Assessment for Highway Bridges Based on Multisensor Fusion Simultaneous Localization and Mapping
Publication: Journal of Bridge Engineering
Volume 29, Issue 2
Abstract
The three-dimensional (3D) reconstruction method based on computer vision technology greatly facilitates the automated performance inspection and assessment of transportation infrastructure, in which the acquisition of vertical clearance is crucial for both traffic planning and structural integrity inspection of bridges. However, manual clearance assessment conducted by traditional measurement methods is dangerous and time-consuming. In order to address these problems, a vertical clearance assessment method for highway bridges based on multisensor fusion simultaneous localization and mapping (SLAM) was proposed. A convenient and low-cost handheld device was constructed, and a simple wireless remote data control method was proposed to improve the convenience of the device operation. The SLAM algorithm was used for the point cloud reconstruction of the scene, which could view the data acquisition process simultaneously. Based on the point cloud reconstruction data, a vertical clearance assessment method was proposed to evaluate the highway bridge's vertical clearance. The proposed method was tested through a complex section of the cross-line highway bridge. The field test results showed that the constructed equipment and the SLAM algorithm could quickly complete the bridge data acquisition and 3D point cloud reconstruction, and the process took about 316 s. The proposed vertical clearance assessment algorithm could remove the noise, such as that from vehicles, and the average vertical clearance difference between the ground truth and the extracted results was 2.5%.
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Data Availability Statement
Some data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request, such as raw point cloud data, image data, and IMU data.
Acknowledgments
The work described in this paper was supported by the Beijing Nova Program (No. 20220484103), the Beijing Natural Science Foundation (No. 8222027), and the Fundamental Research Funds for Central Universities (2022YJS071). The authors would like to thank the reviewers for their valuable comments and suggestions.
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Published In
Journal of Bridge Engineering
Volume 29 • Issue 2 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 18, 2023
Accepted: Oct 15, 2023
Published online: Dec 13, 2023
Published in print: Feb 1, 2024
Discussion open until: May 13, 2024
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