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基于点云配准与最近邻搜索的钢轨磨耗测量方法

曾杉 王文斌 尹太军 彭建川 刘艳彩 张杰

燕山大学学报2025,Vol.49Issue(1):55-65,11.
燕山大学学报2025,Vol.49Issue(1):55-65,11.DOI:10.3969/j.issn.1007-791X.2025.01.006

基于点云配准与最近邻搜索的钢轨磨耗测量方法

A measurement method for rail wear based on point cloud registration and nearest neighbor search

曾杉 1王文斌 2尹太军 3彭建川 2刘艳彩 3张杰4

作者信息

  • 1. 中国科学院地理科学与资源研究所,北京 100101
  • 2. 中国神华能源股份有限公司,北京 100011
  • 3. 中科吉芯(秦皇岛)信息技术有限公司,河北 秦皇岛 066000
  • 4. 国能包神铁路集团有限责任公司,内蒙古 包头 014000
  • 折叠

摘要

Abstract

A method based on point cloud registration and nearest neighbor search is proposed to address the significant errors in identifying wear measurement points on the rail head caused by noise from stamped markings on the rail web.Automatic localization of vertical and lateral wear points on the rail is successfully achieved.Initially,preprocessing techniques such as coordinate system rotation and point cloud filtering are employed to use the rail profile as a data unit and obtain effective rail registration data.Subsequently,a nonlinear fitting method is used to fit the center of the arc of the rail web,serving as the reference point for preliminary coarse registration of the point cloud in any state.For the actual measurement scenario where stamped numbers appear on the rail web,an ICP(Iterative Closest Point)weighted fine registration scheme for the rail head and rail web point clouds is adopted to achieve precise alignment of the measured profile with the standard profile.Finally,according to the rail wear measurement method,the specified position coordinate line of the standard rail profile is used as the reference line.In the registered point cloud data,the nearest coordinate to the reference line is identified through the nearest neighbor search method,thereby accurately locating the wear measurement points.Experimental results demonstrate that this method can efficiently and accurately extract rail wear measurement points.A three-dimensional graph comparing the wear measurement points with the standard profile is shown as the article′s conclusion.The standard deviation of the extracted feature points is less than 0.1 mm,and the maximum deviation is less than 0.3 mm.

关键词

钢轨磨耗/点云预处理/加权点云配准/最近邻搜索

Key words

rail wear/point cloud preprocessing/weighted point cloud registration/nearest neighbor search

分类

交通运输

引用本文复制引用

曾杉,王文斌,尹太军,彭建川,刘艳彩,张杰..基于点云配准与最近邻搜索的钢轨磨耗测量方法[J].燕山大学学报,2025,49(1):55-65,11.

基金项目

中国神华能源股份有限公司科技项目(SHGF-21-02) (SHGF-21-02)

河北省科技计划项目(216Z1704G) (216Z1704G)

中国科学院战略性先导科技专项(XDB0740200) (XDB0740200)

燕山大学学报

OA北大核心

1007-791X

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