结合配准补偿和空间约束的隧道三维建图方法

doi:10.11947/j.AGCS.2025.20240018

测绘学报 ›› 2025, Vol. 54 ›› Issue (2): 297-307.doi: 10.11947/j.AGCS.2025.20240018

• 摄影测量学与遥感 • 上一篇

结合配准补偿和空间约束的隧道三维建图方法

张星¹^,²^,³^,⁴(), 黄展鹏¹^,²^,³^,⁴, 李清泉²^,³^,⁴^,⁵, 周宝定²^,⁵(), 李麒沛¹^,²^,³^,⁴

^1.深圳大学建筑与城市规划学院，广东　深圳　518060
^2.深圳大学广东省城市空间信息工程重点实验室，广东　深圳　518060
^3.深圳大学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.深圳大学深圳市空间信息智能感知与服务重点实验室，广东　深圳　518060
^5.深圳大学土木与交通工程学院，广东　深圳　518060

收稿日期:2024-01-12 发布日期:2025-03-11
通讯作者: 周宝定 E-mail:xzhang@szu.edu.cn;bdzhou@szu.edu.cn
作者简介:张星（1982—），男，博士，副教授，研究方向为多传感器融合定位与三维建图。　E-mail：xzhang@szu.edu.cn
基金资助:
国家重点研发计划(2022YFB3904602);国家自然科学基金(42071434);深圳市科技计划资助项目(JCYJ20240813142833044);广东省教育厅创新团队项目(2024KCXTD013);教育部高等学校科学研究发展中心项目(2024HT013)

3D tunnel mapping method combining registration compensation and spatial constraint

Xing ZHANG¹^,²^,³^,⁴(), Zhanpeng HUANG¹^,²^,³^,⁴, Qingquan LI²^,³^,⁴^,⁵, Baoding ZHOU²^,⁵(), Qipei LI¹^,²^,³^,⁴

^1.School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
^2.Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China
^3.Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, MNR, Shenzhen University, Shenzhen 518060, China
^4.Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
^5.College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China

Received:2024-01-12 Published:2025-03-11
Contact: Baoding ZHOU E-mail:xzhang@szu.edu.cn;bdzhou@szu.edu.cn
About author:ZHANG Xing (1982—), male, PhD, associate professor, majors in multi-sensor fusion positioning and 3D mapping.　E-mail: xzhang@szu.edu.cn
Supported by:
The National Key Research and Development Program of China(2022YFB3904602);The National Natural Science Foundation of China(42071434);Shenzhen Science and Technology Program(JCYJ20240813142833044);The Innovation Team of the Department of Education of Guangdong Province(2024KCXTD013);The Center for Scientific Research and Development in Higher Education Institutes, MOE(2024HT013)

摘要/Abstract

摘要：

三维地图对于隧道的施工安全预警和长期安全维护至关重要。然而，在纹理缺乏、结构粗糙且存在动态干扰的隧道环境中，生成准确的三维点云地图是一项具有挑战性的任务。本文提出了一种隧道三维建图方法，用于构建超长且高噪声隧道场景的点云地图。首先，提出了一种配准残差补偿模型，以减少由粗糙表面结构引起的配准误差。该模型利用K均值聚类算法识别非平整表面结构，并通过局部区域残差进行有效补偿。然后，提出了一种基于视场最大化的空间约束模型，以排除绝对测量偏差造成的点云误差。为了验证本方法的性能，本文在钻爆法和盾构法隧道的二次衬砌和管沟敷设阶段进行了试验。结果表明，该方法较主流建图算法FAST-LIO2、Faster-LIO和LiLi-OM具有更准确的轨迹估计和地图构建。此外，本文进行了一些消融试验，以进一步阐明不同模型在隧道三维建图中的作用。

关键词: 建图, 配准补偿, 空间约束, 激光雷达, 四足机器人

Abstract:

Three-dimensional (3D) maps are crucial for early-warning construction safety and long-term safety maintenance of tunnels. However, generating an accurate 3D point cloud map in tunnels characterized by sparse textures, rough structures, and dynamic interference poses a challenging task. This paper proposes a 3D tunnel mapping method to generate point cloud maps of extremely long and noisy scenes. First, a registration residual compensation model is proposed to eliminate the registration errors caused by rough surface structures. The K-means clustering method is used to identify non-planar surface structures, and compensation is carried out based on local region residual. Then, a spatial constraint strategy based on view field maximization is proposed to eliminate point cloud errors caused by absolute measurement deviations. To verify the performance of the proposed method, we conducted experiments during the secondary lining and pipeline laying stages in both drilling and blasting and shield tunnels. The results indicate that the proposed method outperforms the methods of FAST-LIO2, Faster-LIO, and LiLi-OM in terms of both trajectory estimation and map accuracy. Additionally, ablation experiments were conducted to elucidate the contributions of different models in 3D mapping of tunnels.

Key words: mapping, registration compensation, spatial constraint, LiDAR, legged robot

中图分类号:

P231

张星, 黄展鹏, 李清泉, 周宝定, 李麒沛. 结合配准补偿和空间约束的隧道三维建图方法[J]. 测绘学报, 2025, 54(2): 297-307.

Xing ZHANG, Zhanpeng HUANG, Qingquan LI, Baoding ZHOU, Qipei LI. 3D tunnel mapping method combining registration compensation and spatial constraint[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(2): 297-307.

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数据集	施工阶段	长度/m	激光雷达点数量				扫描帧数量
数据集	施工阶段	长度/m	FAST-LIO2	Faster-LIO	LiLi-OM	本文方法	扫描帧数量
Tunnel-P	管沟敷设	123.2	1 121.9×10⁴	1 122.3×10⁴	152.0×10⁴	1 114.3×10⁴	1451
Tunnel-C	二次衬砌	129.0	905.1×10⁴	905.4×10⁴	124.7×10⁴	904.9×10⁴	1224
Tunnel-T	管沟敷设	101.8	1 535.9×10⁴	1 535.6×10⁴	182.0×10⁴	1 448.3×10⁴	1767

方法	Tunnel-C	Tunnel-T			Tunnel-P
方法	CE/m	CE/m	AME/m	RME/（%）	AME/m	RME/（%）
FAST-LIO2	0.90	0.20	0.07	1.90	0.06	1.09
Faster-LIO	0.59	2.04	0.27	7.38	0.12	2.06
LiLi-OM	1.81	—		—	1.64	28.50
本文方法	0.01	0.03	0.02	0.49	0.02	0.26

方法	Tunnel-C	Tunnel-T			Tunnel-P		计算时间/s
方法	CE/m	CE/m	AME/m	RME/（%）	AME/m	RME/（%）	计算时间/s
残差补偿+无空间约束	0.56	0.19	0.06	1.57	0.07	1.17	0.012
无残差补偿+空间约束	0.84	0.30	0.21	5.94	0.44	7.62	0.013
无残差补偿+无空间约束	1.04	0.51	0.39	10.76	0.57	10.03	0.012
残差补偿+空间约束(本文方法)	0.01	0.03	0.02	0.49	0.02	0.26	0.012

结合配准补偿和空间约束的隧道三维建图方法

3D tunnel mapping method combining registration compensation and spatial constraint

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