Point Cloud Registration Methods for Long-Span Bridge Spatial Deformation Monitoring Using Terrestrial Laser Scanning

Abstract

In recent years, efforts have been devoted to utilizing terrestrial laser scanning for bridge spatial performance inspection, but they are still restricted to small or medium-span bridges, like some historical heritages. Due to the large-scale dimensional features of long-span bridges, applications of 3D point cloud techniques still remain challenging, such as the extra-long scan range and extreme-small incidence angle when scanning a bridge with a span over 1000 m. Moreover, rare attempts can be found for the performance evaluation of point cloud registration methods for long-span bridges as well, which is a critical basis for further spatial deformation recognition on the point cloud data. Hence, in this study, a cross-evaluation of three iterative closest point (ICP) registration methods is conducted for long-span suspension bridges, namely, traditional ICP, kd-tree-based ICP, and feature point-based ICP algorithms. We conducted field laser scanning on the Ma’anshan Yangtze Bridge, an 1880 m long suspension bridge located in China. The results show that the feature point-based ICP algorithm outperforms the other two in terms of convergence rate and execution time for a single iteration due to the smaller number of registration points compared to the other two algorithms. Moreover, it also gives more precise values in terms of bridge tower spatial deformation identification. Meanwhile, due to efficient data organization, the kd-tree-based ICP algorithm takes less time for a single iteration than the traditional ICP algorithm. Finally, two suggestions for algorithm improvement in terms of efficiency optimization and accuracy improvement of long-span bridge deformation analysis are proposed based on the assessment results.

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data

“…Zhao et al [ 15 ] proposed an efficient local descriptor for pairwise registration and validated its accuracy using a dataset of a large-scale high-pier concrete bridge. Based on a long-span suspension bridge dataset, Zhang et al [ 9 ] compared and evaluated three iterative closest point (ICP) registration methods from various aspects including convergence rate, execution time, and accuracy. They also provided suggestions for efficiency optimization and accuracy improvement to enhance long-span bridge deformation analysis.…”

“…Although some researchers have studied registration methods of bridge PCD, they mainly focused on pairwise registration [ 8 , 9 ]. However, to align multiple scans of a bridge to a unified coordinate system, the utilization of multi-view registration techniques is essential.…”

Real‐time displacement measurement for long‐span bridges using a compact vision‐based system with speed‐optimized template matching

“…In contrast to contact sensors, non-contact displacement sensors such as microwave interferometric radar (H. Zhang et al, 2023;, laser sensor (Kim & Jung, 2022;H. Zhang et al, 2023), and total station (Pehlivan, 2022) enable remote measurement of bridge displacement without physical access to the structure.…”

“…In contrast to contact sensors, non‐contact displacement sensors such as microwave interferometric radar (H. Zhang et al., 2023; Zhao et al., 2020), laser sensor (Kim & Jung, 2022; H. Zhang et al., 2023), and total station (Pehlivan, 2022) enable remote measurement of bridge displacement without physical access to the structure. While these contactless sensors offer significant advantages in terms of safety and convenience, they are often accompanied by high costs, which can limit their widespread use.…”

A New Method for the Compaction Quality Inspection of High Rockfill Dams Based on 3D Laser Scanning Technology

“…3D laser technology is becoming an important tool for precision measurement and testing with its advantages of rapidly scanning the surface of the measured object and directly obtaining high-resolution point cloud data [14][15][16][17][18]. Sedek and Serwa [19] developed a semiautomatic approach for forming and processing laser sensing data of trusses using ANN.…”