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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (9): 1607-1614    DOI: 10.3785/j.issn.1008-973X.2021.09.001
机械工程、能源工程     
基于激光传感器的槽型轨轮廓匹配方法
伍川辉1(),廖家1,熊仕勇1,牛英杰1,周博2
1. 西南交通大学 机械工程学院，四川 成都 610031
2. 中车长春轨道客车股份有限公司，吉林 长春 130062
Contour matching method of groove track based on laser sensor
Chuan-hui WU1(),Jia LIAO1,Shi-yong XIONG1,Ying-jie NIU1,Bo ZHOU2
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. CRRC Changchun Railway Vehicles Limited Company, Changchun 130062, China
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摘要：

为了实时掌握有轨电车线路的轨道状态，以期更有效地指导线路运营维护，针对有轨电车特殊的轨道结构和安装方式带来的轨道轮廓匹配困难问题，提出适用于槽型轨的轮廓匹配算法. 通过分析轨道的磨损规律及结构特征，定义槽型轨的匹配基准区域，给出区域的自动分割方法. 为了解决匹配区域定位困难的问题，设计两段式最近点迭代（ICP）匹配算法，实现轮廓的精确匹配；通过卡尔曼滤波器对匹配得到的旋转平移参数进行连续预测，解决在特殊情况下的异常匹配问题；通过实际线路实验，验证算法的可靠性. 实验结果表明：所提匹配算法具有快速、高精度和高鲁棒性的特点，能有效克服槽型轨的特殊结构和嵌入式安装方式带来的匹配困难问题.
关键词： 槽型轨断面轮廓;  动态匹配;  匹配基准区域;  最近点迭代(ICP);  卡尔曼滤波器    
Abstract:

Aiming at the difficulty of matching the special track structure and installation mode of the tram, a contour matching algorithm for embedded trough track was presented, in order to grasp the track status of tram lines in real time, and guide the line operation and maintenance more effectively. By analyzing the wear law and structural characteristics of the track, the matching reference area of the slotted rail was defined, and a automatic segmentation method of the region was presented. In order to solve the problem of the matching region locating, a two-step iterative closest point (ICP) matching algorithm was designed, which realized the exact matching of contour. The Kalman filter was used to predict the rotation and translation parameters to solve the problem of abnormal matching in special cases. The reliability of the algorithm was verified by actual line experiments. Experimental results show that the algorithm is fast, accurate and robust, and it can effectively overcome the matching difficulties caused by the special structure and embedded installation of groove rail.
Key words: groove rail profile    dynamic matching    matched reference region    iterative closest point (ICP)    Kalman filter
收稿日期: 2020-07-23 出版日期: 2021-10-20
CLC:  U 213.2  
基金资助: 国家重点研发计划资助项目（2018YFB1201605）；四川省科技计划资助项目（2019JDRC0024）
作者简介: 伍川辉（1964—），男，副教授，从事检测技术及故障诊断技术研究. orcid.org/0000-0002-1447-7796. E-mail： chwu126@126.com
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伍川辉
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周博

引用本文:

伍川辉,廖家,熊仕勇,牛英杰,周博. 基于激光传感器的槽型轨轮廓匹配方法[J]. 浙江大学学报(工学版), 2021, 55(9): 1607-1614.

Chuan-hui WU,Jia LIAO,Shi-yong XIONG,Ying-jie NIU,Bo ZHOU. Contour matching method of groove track based on laser sensor. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1607-1614.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.09.001    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I9/1607

图 1  激光位移传感器内部结构示意图
图 2  轨道廓形检测方式示意图
图 3  轨道几何参数检测系统结构简图
图 4  原始廓形点云数据
图 5  传感器坐标系与轨道基准坐标系的相对位置
图 6  槽型轨结构及嵌入式安装方式
图 7  实测点云数据及其分段
图 8  粗匹配结果及基准区域分割
图 9  正常廓形与含杂物廓形的CE斜率曲线对比
图 10  磨损轮廓数据及其粗匹配结果
图 11  匹配基准区域分割结果
图 12  近似曲率参数示意图
图 13  最终匹配结果
图 14  轨道几何参数检测系统安装位置
图 15  轨距偏差值动态检测结果
$K$/m ${d_{\rm{m}}}$/mm ${d_{\rm{d}}}$/mm $\varepsilon $/mm
K23+225 0.7 0.603 7 ?0.096 4
K23+250 0.3 0.329 6 0.029 6
K23+275 ?0.1 ?0.156 4 ?0.056 4
K23+300 0.5 0.395 5 ?0.104 5
K23+325 ?0.2 ?0.160 3 0.039 7
K23+350 0.3 0.465 5 0.165 5
K23+375 ?0.1 ?0.161 4 ?0.061 4
K23+400 ?0.3 ?0.335 5 ?0.035 5
K23+425 0.1 0.161 3 0.061 3
K23+450 0.2 0.302 2 0.102 2
表 1  动态检测与人工检测实验结果对比
图 16  轮轨接触末端点的卡尔曼滤波效果
图 17  卡尔曼滤波在异常匹配时的修正效果
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