联合动态池化自适应配准与邻域特征DTW的分阶段隧道配准方法

doi:10.11947/j.AGCS.2026.20250550

测绘学报 ›› 2026, Vol. 55 ›› Issue (9): 1555-1566.doi: 10.11947/j.AGCS.2026.20250550

联合动态池化自适应配准与邻域特征DTW的分阶段隧道配准方法

孙海丽¹^,²(), 严心武¹^,², 杨平¹^,², 海晟¹^,², 钟若飞¹^,², 王明权³

^1.首都师范大学资源环境与旅游学院，北京　100048
^2.首都师范大学三维信息获取与应用教育部重点实验室，北京　100048
^3.重庆市测绘科学技术研究院，重庆　401336

收稿日期:2025-12-30 修回日期:2026-09-01 出版日期:2026-10-28 发布日期:2026-10-28
作者简介:孙海丽（1986—），女，博士，副教授，研究方向为测绘和轨道交通检测与监测。　E-mail：sunhaili@cnu.edu.cn
基金资助:
河南理工大学测绘科学与技术“双一流”学科项目(CH2026B008)

A staged tunnel registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted DTW

Haili Sun¹^,²(), Xinwu Yan¹^,², Ping Yang¹^,², Sheng Hai¹^,², Ruofei Zhong¹^,², Mingquan Wang³

^1.College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
^2.Key Laboratory of3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
^3.Chongqing Institute of Surveying and Mapping Science and Technology, Chongqing 401336, China

Received:2025-12-30 Revised:2026-09-01 Online:2026-10-28 Published:2026-10-28
About author:Sun Haili (1986—), female, PhD, associate professor, majors in surveying and rail transit monitoring. E-mail: sunhaili@cnu.edu.cn
Supported by:
The “Double First-Class” Discipline Project of Surveying and Mapping Science and Technology, Henan Polytechnic University(CH2026B008)

摘要/Abstract

摘要：

针对多期隧道移动激光扫描数据因里程累积误差及采集姿态差异导致的跨期对齐问题，提出一种联合动态池化自适应配准与邻域特征辅助动态时间规整（DTW）的分阶段配准方法。首先，利用点云反射强度生成隧道二维影像，通过SuperPoint提取局部特征，在LightGlue的旋转位置编码与注意力匹配框架基础上，引入面向隧道影像特征分布的动态池化策略，实现多期数据的大范围里程初始对齐；随后，将隧道断面点云转换为极坐标径向序列，利用动态时间规整建立跨期断面对应关系，并结合自适应邻域内在形状签名（ISS）关键点提取与最小二乘优化实现断面精配准。在重庆地铁盾构法和矿山法隧道两期移动激光扫描数据上开展试验。结果表明，盾构法隧道的影像配准和点云配准误差分别为1.3和1.2 cm，矿山法隧道分别为3.7和3.6 cm；精配准后点云倒角距离整体收敛至0.1~0.3 cm，平均约为0.2 cm，较配准前降低约90%。结果表明，本文方法能够适应不同结构形式隧道的跨期数据对齐，为隧道结构变形监测、病害演化分析及多期数据融合提供可靠的空间基准。

关键词: 隧道, 移动激光扫描, 多期数据配准, LightGlue, 动态时间规整, ISS

Abstract:

To address cross-epoch misalignment of multi-epoch tunnel mobile laser scanning data caused by accumulated mileage errors and variations in acquisition poses, a staged registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted dynamic time warping (DTW) is proposed. First, two-dimensional tunnel images are generated from point-cloud intensity information, and local features are extracted using SuperPoint. Building upon the rotary positional encoding and attention-based matching framework of LightGlue, a dynamic pooling strategy tailored to the feature distribution of tunnel images is introduced to achieve coarse mileage alignment over large offsets between multi-epoch datasets. Subsequently, tunnel cross-sectional point clouds are transformed into radial sequences in polar coordinates, and dynamic time warping is employed to establish cross-epoch correspondences between tunnel cross-sections. Adaptive-neighborhood intrinsic shape signature (ISS) keypoint extraction and least-squares optimization are then applied to achieve refined cross-sectional registration. Experiments were conducted on multi-epoch mobile laser scanning data acquired from shield and mined tunnels of Chongqing Metro. The image-registration and point-cloud-registration errors were 1.3 and 1.2 cm, respectively, for the shield tunnel, and 3.7 and 3.6 cm, respectively, for the mined tunnel. After refined registration, the Chamfer distances converged to 0.1~0.3 cm, with an average of approximately 0.2 cm, representing a reduction of approximately 90% compared with those before registration. The results demonstrate that the proposed method can effectively accommodate cross-epoch alignment of tunnels with different structural characteristics and provide a reliable spatial reference for tunnel deformation monitoring, defect evolution analysis, and multi-epoch data fusion.

Key words: tunnel, mobile laser scanning, multi-epoch registration, LightGlue, dynamic time warping, ISS

中图分类号:

P232

孙海丽, 严心武, 杨平, 海晟, 钟若飞, 王明权. 联合动态池化自适应配准与邻域特征DTW的分阶段隧道配准方法[J]. 测绘学报, 2026, 55(9): 1555-1566.

Haili Sun, Xinwu Yan, Ping Yang, Sheng Hai, Ruofei Zhong, Mingquan Wang. A staged tunnel registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted DTW[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(9): 1555-1566.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250550

http://xb.chinasmp.com/CN/Y2026/V55/I9/1555

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隧道类型	里程范围/m	第一期采集时间	第二期采集时间	文件数	数据总量
盾构法	47 779.0~48 229.0	2022-09	2023-05	26	11.6×10⁹
矿山法	0.0~200.0	2022-08	2023-08	16	6.74×10⁹

联合动态池化自适应配准与邻域特征DTW的分阶段隧道配准方法

A staged tunnel registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted DTW

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参考文献 22

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隧道数据	影像匹配方法	第一阶段影像配准误差	第二阶段点云配准误差	点云配准优化误差	点云配准前/后倒角距离
盾构法	LightGlue	1.0	0.9	0.1	2.4/0.2
盾构法	动态池化自适应配准	1.3	1.2	0.1	1.8/0.3
矿山法	LightGlue	3.6	3.0	0.6	2.3/0.1
矿山法	动态池化自适应配准	3.7	3.6	0.1	0.3/0.2

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