地铁隧道沉降惯性视觉实时监测法

doi:10.11947/j.AGCS.2025.20250174

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2182-2193.doi: 10.11947/j.AGCS.2025.20250174

地铁隧道沉降惯性视觉实时监测法

华远盛¹^,²^,³^,⁴(), 陈博宇¹, 刘欣琳¹, 杨韵¹, 朱松¹^,²^,³(), 朱家松¹^,²^,³^,⁵, 李清泉²^,³

^1.深圳大学土木与交通工程学院，广东　深圳　518060
^2.广东省城市空间信息工程重点实验室，广东　深圳　518060
^3.自然资源部大湾区地理环境监测重点实验室，广东　深圳　518060
^4.极端环境岩土和隧道工程智能建养全国重点实验室，广东　深圳　518060
^5.极端环境绿色长寿道路工程全国重点实验室，广东　深圳　518060

收稿日期:2025-04-19 修回日期:2025-10-11 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 朱松 E-mail:yuansheng.hua@szu.edu.cn;zhusong2021@email.szu.edu.cn
作者简介:华远盛（1991—），男，博士，助理教授，研究方向为工程测量、影像解译。　E-mail：yuansheng.hua@szu.edu.cn
基金资助:
深圳市科技创新委项目(20231120191328001);广东省区域联合基金青年项目(2023A1515110722);国家自然科学基金青年项目(42401402)

Hybrid visual-inertial measurement method of subway tunnel settlement

Yuansheng HUA¹^,²^,³^,⁴(), Boyu CHEN¹, Xinlin LIU¹, Yun YANG¹, Song ZHU¹^,²^,³(), Jiasong ZHU¹^,²^,³^,⁵, Qingquan LI²^,³

^1.College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
^2.Guangdong Key Laboratory of Urban Informatics, Shenzhen 518060, China
^3.MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen 518060, China
^4.State Key Laboratory of Intelligent Geotechnics and Tunnelling, Shenzhen 518060, China
^5.National Key Laboratory of Green and Longevity Road Engineering in Extreme Environments, Shenzhen 518060, China

Received:2025-04-19 Revised:2025-10-11 Online:2026-01-15 Published:2026-01-15
Contact: Song ZHU E-mail:yuansheng.hua@szu.edu.cn;zhusong2021@email.szu.edu.cn
About author:HUA Yuansheng (1991—), male, PhD, assistant professor, majors in engineering surveying and image interpretation. E-mail: yuansheng.hua@szu.edu.cn
Supported by:
The Shenzhen Science and Technology Innovation Commission Project(20231120191328001);The Guangdong Province Regional Joint Fund-Young Scientists Fund(2023A1515110722);The National Natural Science Foundation of China-Young Scientists Fund(42401402)

摘要/Abstract

摘要：

地铁隧道是城市轨道交通工程的重要组成部分，在运营期间受地质压力、周边施工和长期荷载的影响，可能发生不均匀沉降，阻碍正常使用，甚至导致严重事故。因此，开展面向存在风险隐患的地铁隧道实时沉降监测至关重要。针对传统方法监测频率不足、受环境振动影响大等问题，本文提出了一种基于惯性视觉测量的地铁隧道实时沉降监测方法，通过惯性视觉测量仪采集隧道内测点布设影像，自动提取影像中测点像素坐标，同时结合内置惯性测量单元采集的数据计算测量仪姿态，建立隧道沉降解算模型。本文通过模拟试验验证该方法在测量平台振动的条件下可进行高频率、高精度的位移测量，精度优于1 mm，误差水平相较视觉测量方法可降低89.2%。在地铁隧道沉降监测实际应用中，监测结果表明惯性视觉测量方法在精度和稳定性方面显著优于传统视觉测量方法，并与全站仪监测数据高度一致，具有良好的应用前景。

关键词: 惯性视觉测量, 地铁隧道沉降, 实时监测, 动态精密工程测量

Abstract:

Subway tunnels, as critical components of urban rail transit systems, are prone to uneven settlement during operation due to geological stress, adjacent construction activities, and long-term loading, which may compromise structural integrity and potentially lead to severe accidents. To address the limitations of conventional methods such as insufficient monitoring frequency and vulnerability to environmental vibrations, this paper proposes a real-time settlement monitoring method for subway tunnels based on inertial-visual measurement. The methodology integrates a hybrid visual-inertial measurement device to simultaneously capture tunnel marker images and collect instrument pose data through built-in inertial measurement units (IMU). By establishing a tunnel settlement calculation model that combines pixel coordinates of feature points with real-time pose parameters, this system achieves high-frequency displacement measurement. Simulation experiments demonstrate the method's capability for high-precision displacement monitoring (accuracy better than 1 mm) under platform vibration conditions, showing an 89.2% error reduction compared with pure vision-based approaches. Practical applications in metro tunnel monitoring reveal that the inertial-visual measurement method significantly outperforms traditional optical techniques in both accuracy and stability, with monitoring results exhibiting strong consistency with total station data. This approach demonstrates promising potential for real-time safety monitoring in long-span tunnel engineering applications.

Key words: hybrid visual-inertial measurement, subway tunnel settlement, real-time monitoring, dynamic precise engineering surveying

中图分类号:

P227

华远盛, 陈博宇, 刘欣琳, 杨韵, 朱松, 朱家松, 李清泉. 地铁隧道沉降惯性视觉实时监测法[J]. 测绘学报, 2025, 54(12): 2182-2193.

Yuansheng HUA, Boyu CHEN, Xinlin LIU, Yun YANG, Song ZHU, Jiasong ZHU, Qingquan LI. Hybrid visual-inertial measurement method of subway tunnel settlement[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2182-2193.

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方法	0.4°		0.8°		1.2°		1.6°		2.0°
方法	单步	周期	单步	周期	单步	周期	单步	周期	单步	周期
视觉测量	11.42	7.96	51.79	35.03	91.63	63.73	131.75	89.44	171.46	110.35
视觉测量+基准修正	5.07	3.61	5.08	3.49	5.09	3.60	5.11	3.51	5.18	3.36
惯性视觉测量	0.45	0.61	0.47	0.74	0.33	0.54	0.90	0.97	0.65	0.93

断面	相互较差/mm		相关系数/（%）
断面	视觉测量	惯性视觉测量仪	视觉测量	惯性视觉测量仪
1	0.83	0.26	1.14	75.56
2	1.47	0.19	6.52	61.33
3	2.13	0.36	14.77	46.88

地铁隧道沉降惯性视觉实时监测法

Hybrid visual-inertial measurement method of subway tunnel settlement

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