Deflection detection method of steel structure based on SLAM technology

doi:10.13474/j.cnki.11-2246.2024.1030.

Abstract

Abstract: Steel structures will produce spatial deformation under long-term loading and uneven forces, and deflection is an important measure of it. Conventional deflection detection methods rely on manual measurements or fixed sensors, which have certain limitations in real-time, accuracy and coverage, making it difficult to accurately capture the subtle changes in structural deformation. In this paper, the plant project of an automobile is taken as the engineering background, and the 3D laser scanning system based on simultaneous localization and mapping (SLAM) technology is used to carry out jacking measurements on the steel structure mesh frame and extract the spherical centers of the node balls of the steel structure through the RANSAC algorithm for deflection detection. The study shows that the steel structure deflection detection method based on SLAM technology has high accuracy and real-time performance, and can effectively detect the deflection deformation of the steel structure mesh frame, which provides a new idea and solution for steel structure deflection detection, and has important theoretical and application value.

Key words: SLAM, deflection detection, RANSAC, steel structure

CLC Number:

P237

ZHU Weigang, ZHAO Tiankai, LIU Mingyang, JIN Shengbo, JIANG Shaohua. Deflection detection method of steel structure based on SLAM technology[J]. Bulletin of Surveying and Mapping, 2024, 0(10): 179-182.

References

[1] HUANG Y Q,FENG R Q,ZHONG C J,et al.Computer vision-based real-time deflection monitoring of complex and sizeable steel structures[J].Engineering Structures,2024,305:117752.
[2] 宋云记,王晓民,王智.基于三维激光扫描技术的钢结构扰度检测及应用[J].测绘通报,2020(12):160-163.
[3] 谷川.钢网架挠度检测评定方法研究[J].城市道桥与防洪,2017(5):273-275.
[4] 冒如权.基于激光测距技术的舱壁挠度监测系统开发及试验验证[J].船舶与海洋工程,2023,39(4):20-24.
[5] 伍辉.挠度测量技术在桥梁监测中的应用[J].工程技术研究,2023,8(19):66-68.
[6] 马慧宇.基于无线传感器网络的桥梁全天候健康监测系统设计[D].太原:中北大学,2022.
[7] 诸宏博,谢忠,傅林峰.三维激光扫描技术在钢结构检测技术中的应用研究[J].建筑结构,2023,53(S2):1739-1743.
[8] CHGHAF M,FLÓREZ S R,EL OUARDI A.A multimodal loop closure fusion for autonomous vehicles SLAM[J].Robotics and Autonomous Systems,2023,165:104446.
[9] 李晨曦,张军,靳欣宇,等.激光雷达SLAM技术及其在无人车中的应用研究进展[J].北京联合大学学报,2017,31(4):61-69.
[10] 余联想,郑明魁,欧文君,等.多传感器融合的移动机器人室外激光SLAM算法优化与系统实现[J].电子测量与仪器学报,2023,37(2):48-55.
[11] 郭旭.基于激光雷达的SLAM地图构建算法研究[D].沈阳:沈阳工业大学,2022.
[12] 刘铭哲,徐光辉,唐堂,等.激光雷达SLAM算法综述[J].计算机工程与应用,2024,60(1):1-14.
[13] 曾庆化,罗怡雪,孙克诚,等.视觉及其融合惯性的SLAM技术发展综述[J].南京航空航天大学学报,2022,54(6):1007-1020.
[14] FISCHLER M A,BOLLES R C.Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[M]//Readings in Computer Vision.Amsterdam: Elsevier,1987: 726-740.

[1]	XU Zhe, DONG Linxiao, WU Jiayue. LiDAR point cloud registration with improved ICP algorithm [J]. Bulletin of Surveying and Mapping, 2024, 0(4): 1-5.
[2]	FU Qiang, TENG Xianyun, JI Yuanfa, REN Fenghua, KONG Jianming. ORB-SLAM3 algorithm based on binocular depth screening [J]. Bulletin of Surveying and Mapping, 2024, 0(1): 77-82.
[3]	ZHI Zhiyang, LI Aiguang, DU Zhigang, RUAN Qinglin, ZHAO Yipeng. Noise-resistant extraction of tunnel excavation axis under point cloud density feature constraints [J]. Bulletin of Surveying and Mapping, 2024, 0(1): 109-114.
[4]	LI Yi, CAI Yi, DONG Siqi, ZHAO Zhiyang, CHEN Qi. Quantitative detection technology of pipe damage based on laser point cloud data [J]. Bulletin of Surveying and Mapping, 2024, 0(1): 120-125.
[5]	WU Siqi, LIU Fei, BAI Yu, MA Yuntao, WANG Fei, GUO Ziyu. Research on the robust positioning model of ORB-SLAM2 in outdoor glare scene [J]. Bulletin of Surveying and Mapping, 2023, 0(9): 59-63.
[6]	ZHANG Qingyu, CUI Lizhen, DU Xiuduo, MA Baoliang. Mapping and positioning of 3D LiDAR SLAM algorithm in mine environment [J]. Bulletin of Surveying and Mapping, 2023, 0(5): 72-77.
[7]	ZHU Jianwei, LI Chaokui, ZHOU Xinshao, ZHAO Dingying, FU Kaihong. Research and application of damage detection of highway billboard damage based on vehicle-mounted remote sensing image [J]. Bulletin of Surveying and Mapping, 2023, 0(4): 59-63,98.
[8]	LIN Lesheng, LIN Song. Modular extraction method for point cloud of shield tunnel [J]. Bulletin of Surveying and Mapping, 2023, 0(11): 66-68,106.
[9]	YANG Zhen, MENG Xiangwu, TANG Shunjun, WANG Hao, GONG Yinxi. Application of indoor mobile measurement system in new basic surveying and mapping [J]. Bulletin of Surveying and Mapping, 2023, 0(1): 149-153.
[10]	WANG Yanjun, LIN Yunhao, WANG Shuhan, LI Shaochun, WANG Mengjie. 3D road boundary extraction based on mobile laser scanning point clouds and OSM data [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 18-25.
[11]	XU Fubin, QU Xiangyi, WANG Jian, LIU Xiuhan. Reconstruction method of complex steel structure model based on LiDAR point cloud [J]. Bulletin of Surveying and Mapping, 2022, 0(5): 170-173.
[12]	YU Yao, SUN Xinzhu, GUO Junyang, CHEN Mengyuan. Visual SLAM algorithm based on improved CoHOG [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 42-50.
[13]	XU Xiaoyu, LIU Quqian. High-accuracy survey of roller coaster steel structure and data application of local coordinate system [J]. Bulletin of Surveying and Mapping, 2022, 0(12): 165-169.
[14]	CEN Wenguang, XU Xiong, WANG Guojian, XIAO Changjiang. Research and ground validation based on the visual positioning methods for rover in deep space exploration [J]. Bulletin of Surveying and Mapping, 2022, 0(10): 49-55.
[15]	LI Guojun, XU Yanhai, DUAN Jiewen, HAN Shilei. ORB-SLAM method based on local adaptive threshold extraction feature points [J]. Bulletin of Surveying and Mapping, 2021, 0(9): 32-36,48.