车载LiDAR点云驱动的高速公路护栏参数化建模及变形段识别

doi:10.11947/j.AGCS.2025.20240359

测绘学报 ›› 2025, Vol. 54 ›› Issue (4): 688-701.doi: 10.11947/j.AGCS.2025.20240359

• 实景三维中国建设 • 上一篇

车载LiDAR点云驱动的高速公路护栏参数化建模及变形段识别

贾鑫¹^,²(), 朱庆¹^,³(), 葛旭明³, 马瑞峰⁴, 胡翰³

^1.兰州交通大学测绘与地理信息学院，甘肃　兰州　730070
^2.甘肃大禹九洲空间信息科技有限公司院士专家工作站，甘肃　兰州　730050
^3.西南交通大学地球科学与工程学院，四川　成都　611756
^4.成都工业学院电子工程学院，四川　成都　611730

收稿日期:2024-08-31 发布日期:2025-05-30
通讯作者: 朱庆 E-mail:jiaxin1246782373@163.com;zhuqing@swjtu.edu.cn
作者简介:贾鑫（1995—），男，博士生，研究方向为车载LiDAR点云三维建模。　E-mail：jiaxin1246782373@163.com
基金资助:
国家自然科学基金(42230102)

Parametric modeling and deformation identification of highway guardrail driven by MLS point clouds

Xin JIA¹^,²(), Qing ZHU¹^,³(), Xuming GE³, Ruifeng MA⁴, Han HU³

^1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
^2.Academician Expert Workstation of Gansu Dayu Jiuzhou Space Information Technology Co., Ltd., Lanzhou 730050, China
^3.Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
^4.College of Electronic Engineering, Chengdu Technology University, Chengdu 611730, China

Received:2024-08-31 Published:2025-05-30
Contact: Qing ZHU E-mail:jiaxin1246782373@163.com;zhuqing@swjtu.edu.cn
About author:JIA Xin (1995—), male, PhD candidate, majors in MLS point cloud 3D reconstruction.　E-mail: jiaxin1246782373@163.com
Supported by:
The National Natural Science Foundation of China(42230102)

摘要/Abstract

摘要：

护栏是公路设施的重要组成部分，若其形态发生变形将严重影响其防护功能。已有的护栏形变检测方法主要是从移动测量点云中提取护栏并建模，未对护栏进行更进一步的语义特征挖掘，且在逆向建模中没有真实反映出护栏的形变状况。为此，本文提出以车载激光点云驱动和建筑信息模型引导的高精度护栏参数化建模框架。①从移动激光扫描（MLS）数据中自动化提取和实例化护栏元素；②通过随机抽样一致（RANSAC）方法求解护栏类型的结构参数；③引入B样条曲线护栏参数化，通过Dynamo模块化建模创建真实的护栏模型，并通过曲率法向量约束的轨迹检测机制评估护栏形变里程。该方法提高了护栏构件级模型的精度，为不同护栏类型的养护检查提供安全、高效的解决方案。试验结果表明，该方法在高速公路护栏自动识别中达到98.7%的识别准确率。对选定路段的所有变形护栏进行检测，定位误差小于2.2 m，满足实际检测要求，减轻了因护栏变形带来的交通安全风险。

关键词: 移动激光扫描点云, 高速公路护栏, 建筑信息模型, 变形段识别, 参数化建模

Abstract:

Guardrails are critical component of highway infrastructure, and their deformation can significantly impair their protective function. Existing methods for detecting guardrail deformation primarily focus on extracting and modeling guardrails from mobile mapping point clouds. However, these methods often lack in-depth semantic feature analysis of the guardrails and fail to accurately reflect the deformation conditions in reverse modeling. To address these limitations, this study proposes a high-precision parametric modeling framework for guardrails driven by mobile laser scanning (MLS) point clouds and guided by building information modeling (BIM). The framework involves: ① Automated extraction and instantiation of guardrail elements from MLS data; ② Solving structural parameters of guardrail using the random sample consensus (RANSAC) algorithm; and ③ Introducing B-spline curve-based parametric modeling of guardrails, creating realistic guardrail models through modular modeling in Dynamo. Moreover, evaluating guardrail deformation mileage using a curvature and vector-constrained trajectory detection mechanism. This approach enhances the precision of component-level guardrail models, providing a safe and efficient solution for maintenance inspection of various guardrail types. Experimental results demonstrate a guardrail recognition accuracy of 98.7% on highway guardrail. All deformed guardrails on the selected test sections were detected, with localization errors less than 2.2 meters, meeting practical inspection requirements and mitigating traffic safety risks associated with guardrail deformation.

Key words: MLS point clouds, highway guardrails, BIM model, deformation detection, parametric modeling function

中图分类号:

P232

贾鑫, 朱庆, 葛旭明, 马瑞峰, 胡翰. 车载LiDAR点云驱动的高速公路护栏参数化建模及变形段识别[J]. 测绘学报, 2025, 54(4): 688-701.

Xin JIA, Qing ZHU, Xuming GE, Ruifeng MA, Han HU. Parametric modeling and deformation identification of highway guardrail driven by MLS point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(4): 688-701.

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方法	聚类数量
方法	直线段	弯道
DBSCAN	6	9
本文方法	3	3
真实值	3	3

试验段	真正例和假负例	真正例和假正例	真正例	精确率/（%）	召回率/（%）
直线	302 869	302 258	299 235	99.0	98.8
弯道	370 246	369 492	363 211	98.4	98.1

类型	已知参数	未知参数
dual-wave	H，L，r₁，r₂，r₃，x₂，x₃，…，x₁₄	Length，R，Position_dual
three-wave	H，L，r₁，r₂，r₃，r₄，r₅，x₂，x₃，…，x₂₀	Length，R，Position_three
pillar	w_post，h_post	Spacing_pillar，Position_pillar，offset，R
anti-dazzle board	Rectangle，Cylinder	Spacing_board，Position_board，offset，R
concrete barriers	trapezoid	Length，Position_barriers

护栏	变形里程检测/m	实际里程位置/m	实际里程范围/m	里程误差/m
1	K46+419.9	K11+422.0	K46+420—K46+430	+2.1
2	K47+457.8	K47+459.1	K47+455—K47+465	+1.3
3	K49+263.0	K49+263.0	K49+270—K49+270	0
4	K49+658.5	K49+659.2	K49+655—K49+665	+0.7
5	K50+341.6	K50+339.1	K50+335—K50+345	－1.5
6	K50+773.4	K50+771.7	K50+765—K50+775	－1.7
7	K51+583.5	K51+585.1	K51+580—K50+590	+1.6
8	K52+766.3	K52+764.8	K52+760—K52+770	－1.5
9	K52+844.7	K52+846.4	K52+840—K52+850	+1.7
10	K54+189.1	K54+190.3	K54+185—K54+195	+1.2
11	K55+380.2	K55+379.0	K55+375—K55+385	－0.8
12	K56+612.3	K56+614.1	K56+610—K56+620	+1.8
13	K57+296.7	K57+298.0	K57+290—K57+230	+1.3
14	K58+146.8	K58+144.6	K58+140—K58+150	－2.2
15	K58+475.0	K58+473.6	K58+470—K58+480	－1.4
16	K59+223.7	K59+224.9	K59+220—K59+230	+1.2

方法	试验路段长度/km	识别精度/（%）	里程定位误差/m	形变护栏数	有效检测数
文献[30]	12	92.8	10.0	11	10
本文方法	15.5	98.7	2.2	16	16

车载LiDAR点云驱动的高速公路护栏参数化建模及变形段识别

Parametric modeling and deformation identification of highway guardrail driven by MLS point clouds

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