基于设计信息的桥梁实测点云补全方法

Measured Point Cloud Completion Method for Bridge Based on Design Information

摘要

针对三维激光扫描下桥梁实测点云因密度控制不足和遮挡造成的细节缺失、控制点难以提取的问题,提出了基于设计信息的桥梁实测点云补全方法,以实现桥梁施工监控工作中复杂结构高精度控制点坐标提取与空间姿态分析．首先,提出基于K维树算法与主成分分析的断面点云分割算法,通过调控切片厚度与方向实现点云断面的精确分割；其次,提出了基于CAD设计信息和粗精两阶段配准的断面点云补全算法,生成高精度断面设计点云,并配准设计与实测点云,实现了断面点云补全,并分析不同插值参数对补全精度的影响；最后,提取补全点云的控制点坐标并分析桥梁空间姿态．在针对天津滨海新区某跨海河东西走向待接续施工异型钢拱桥的检测中对上述方法进行了实验验证．结果表明：本研究在1 mm插值间距下均方根误差为0.315 mm,实现了高精度断面点云补全；实验桥梁两岸待施工断面的面内相对偏转角为6.80×10^-4 rad,中心相对高差为35.9 mm；两岸主拱空间姿态为东岸向南偏转,西岸向北偏转．本文所提方法可补全不完备的桥梁实测点云,为桥梁空间姿态检测提供参考和算法支撑,具有一定的工程应用前景．

Abstract

To tackle the issues of missing details and difficult control point extraction in three-dimensional laser-scanned point clouds of bridges, which result from inadequate point density and occlusion, a method based on the design information is proposed for completing the measured point clouds of bridges, so as to enable high-accuracy coordinate extraction of control points and spatial posture analysis for complex structures during the monitoring of bridge construction. First, a cross-section point cloud segmentation algorithm is developed using a K-dimensional tree algorithm and principal component analysis, allowing an accurate segmentation of cross-section point clouds by adjusting the slice thickness and orientation. Second, a cross-section cloud point completion algorithm based on the CAD design information and coarse-to-fine two-stage registration is put forward to generate high-accuracy cross-section design point clouds, which are further used to complete the scanned sections with the registration of design and measured point clouds. In addition, the effect of different interpolation parameters on the completion accuracy is also analyzed. Finally, the coordinates of control points are extracted from the completed point clouds, and the bridge’s spatial posture is also analyzed. The entire methodology was experimentally validated on an east-west special-shaped steel arch bridge, which was still under construction over the Haihe River in Tianjin Binhai New Area. Results demonstrate that the root mean square error was 0.315 mm at an interpolation spacing of 1 mm, realizing a high-accuracy cross-section cloud point completion. The measured in-plane relative deflection angle between the two bank sections to be constructed was 6.80×10^-4 rad, and the relative central height difference was 35.9 mm. The result of spatial posture analysis indicated southward deflection on the eastern bank and northward deflection on the western bank. The proposed method in this paper can complete incomplete measured point clouds of bridges and provide reference and algorithm support for spatial posture detection, demonstrating its potential for engineering applications.

关键词

桥梁工程 / 实测点云 / 点云补全 / 设计信息 / 空间姿态分析

Key words

bridge engineering / measured point cloud / point cloud completion / design information / spatial posture analysis

参考文献

[1] 王凌波, 王秋玲, 朱钊, 等. 桥梁健康监测技术研究现状及展望[J]. 中国公路学报, 2021, 34(12): 25-45.

[2] Wang Lingbo, Wang Qiuling, Zhu Zhao, et al. Current status and prospects of research on bridge health monitoring technology[J]. China Journal of Highway and Transport, 2021, 34(12): 25-45(in Chinese).

[3] 楚玺, 周志祥, 段鑫, 等. 桥梁结构连续线形监测方法[J]. 公路交通科技, 2023, 40(11): 157—163, 171.

[4] Chu Xi, Zhou Zhixiang, Duan Xin, et al. A monitoring method for continuous line—shape of bridge structure[J]. Journal of Highway and Transportation Research and Development, 2023, 40(11): 157—163, 171(in Chinese).

[5] 易有淼, 樊少彻. 港珠澳大桥青州航道桥钢箱梁施工关键技术[J]. 桥梁建设, 2021, 51(3): 138-144.

[6] Yi Youmiao, Fan Shaoche. Key construction technology of steel box girder of Qingzhou Channel Bridge of Hong Kong—Zhuhai—Macao Bridge[J]. Bridge Construction, 2021, 51(3): 138-144(in Chinese).

[7] 朱颖, 郭辉, 孙大奇, 等. 运营期公铁两用悬索桥温度效应研究[J]. 铁道学报, 2024, 46(6): 156-166.

[8] Zhu Ying, Guo Hui, Sun Daqi, et al. Investigation on temperature—induced effect on rail—cum—road suspension bridge during service life[J]. Journal of the China Railway Society, 2024, 46(6): 156-166(in Chinese).

[9] 吴迪军. 桥梁工程测量技术现状及发展方向[J]. 测绘通报, 2016(1): 1-5.

[10] Wu Dijun. Current status and development trend of bridge engineering surveying techniques[J]. Bulletin of Surveying and Mapping, 2016(1): 1-5(in Chinese).

[11] Rashidi M, Mohammadi M, Kivi S S, et al. A decade of modern bridge monitoring using terrestrial laser scanning: Review and future directions[J]. Remote Sensing, 2020, 12(22): 3796.

[12] 王贝, 叶险峰, 王军宁. 利用点云切片提取技术分析桥梁形变[J]. 测绘通报, 2022(4): 167-171.

[13] Wang Bei, Ye Xianfeng, Wang Junning. Bridge deformation analysis based on point cloud slice extraction technique[J]. Bulletin of Surveying and Mapping, 2022(4): 167-171(in Chinese).

[14] Kim D, Kwak Y, Sohn H. Accelerated cable—stayed bridge construction using terrestrial laser scanning[J]. Automation in Construction, 2020, 117: 103269.

[15] 齐宏拓, 刘界鹏, 程国忠, 等. 基于点云数据的大型复杂钢结构智能化施工方法[J]. 土木工程学报, 2024, 57(1): 65-75.

[16] Qi Hongtuo, Liu Jiepeng, Cheng Guozhong, et al. Intelligent construction of large and complex steel structure based on point cloud data[J]. China Civil Engineering Journal, 2024, 57(1): 65-75(in Chinese).

[17] 石林泽, 赵健, 任延龙, 等. 基于三维激光扫描的钢桁拱桥吊索塔架施工线形监测研究[J]. 世界桥梁, 2022, 50(1): 79-85.

[18] Shi Linze, Zhao Jian, Ren Yanlong, et al. Research on 3D laser scanning based line—shape monitoring for towers of cable—stayed cantilever assembly system used in steel truss arch bridge construction[J]. World Bridges, 2022, 50(1): 79-85(in Chinese).

[19] Li J, Peng Y P, Tang Z Y, et al. Three—dimensional reconstruction of railway bridges based on unmanned aerial vehicle—terrestrial laser scanner point cloud fusion[J]. Buildings, 2023, 13: 2841.

[20] Hu G T, Zhou Y, Xiang Z F, et al. Fast and accurate generation method of geometric digital twin model of RC bridge with box chambers based on terrestrial laser scanning[J]. Remote Sensing, 2023, 15(18): 4440.

[21] 王建军, 卢云鹏, 张荠匀, 等. 实现激光点云高效配准的 ICP 优化及性能验证[J]. 红外与激光工程, 2021, 50(10): 20200483.

[22] Wang Jianjun, Lu Yunpeng, Zhang Jiyun, et al. Optimization and performance verification of high efficiency ICP registration for laser point clouds[J]. Infrared and Laser Engineering, 2021, 50(10): 20200483(in Chinese).

[23] Liang H G, Shen H H, Liu P H, et al. Deep learning for sub—Nyquist sampling scanning white light interferometry[J]. Optics Letters, 2023, 48(22): 5976-5979.

[24] 朱劲松, 王多吴, 杨瑞鹏. 基于点云数据的钢箱节段数字化预拼装方法[J]. 长安大学学报(自然科学版), 2024, 44(6): 47-58.

[25] Zhu Jinsong, Wang Duowu, Yang Ruipeng. Digital pre—assembly method for steel box segments based on point cloud data[J]. Journal of Chang’an University(Natural Science Edition), 2024, 44(6): 47-58(in Chinese).

[26] 李岩, 张子毅, 王建柱. 基于特征点提取的RANSAC—ICP三维点云配准方法[J]. 山东大学学报(工学版), 2024, 54(5): 144-154.

[27] Li Yan, Zhang Ziyi, Wang Jianzhu. RANSAC—ICP 3D point cloud registration method based on feature point extraction[J]. Journal of Shandong University(Engineering Science), 2024, 54(5): 144-154(in Chinese).

[28] 王太学, 江智, 江德港, 等. 融合PCA的改进ICP激光点云配准算法[J]. 遥感信息, 2022, 37(2): 70-76.

[29] Wang Taixue, Jiang Zhi, Jiang Degang, et al. Improved ICP laser point cloud registration algorithm based on PCA[J]. Remote Sensing Information, 2022, 37(2): 70-76(in Chinese).

[30] Sorkine—Hornung O, Rabinovich M. Least—Squares Rigid Motion Using SVD[R]. Zurich, Switzerland: ETH Zurich, 2017.

[31] 余永维, 王康, 杜柳青, 等. 点云模型的匹配点对优化配准[J]. 光学精密工程, 2023, 31(4): 503-516.

[32] Yu Yongwei, Wang Kang, Du Liuqing, et al. Matching point pair optimization registration method for point cloud model[J]. Optics and Precision Engineering, 2023, 31(4): 503-516(in Chinese).

基金资助

国家自然科学基金资助项目(52378310)