基于线结构光的钢轨断面磨耗改进检测算法

Improved detection algorithm for rail profile wear based on line structured light

经典的最近点迭代(IterativeClosest Point,ICP)配准算法常被应用于钢轨断面磨耗检测系统。当轨道检修车工作运行时,基于线结构光的钢轨断面磨耗检测系统采集的钢轨廓形数据通常会受到各种离群点噪声的干扰,导致计算得到的钢轨廓形几何形态变化较大。在实际应用中,轨道检修车钢轨断面磨耗动态检测精度要求侧磨为±0.5mm,垂磨为±1.0mm,故磨耗检测算法的设计不能因为廓形含离群点噪声而降低检测精度。因此,文章提出了一种改进的两阶段钢轨断面廓形磨耗检测算法。该算法在第一阶段首先利用钢轨廓形的特征点对进行快速初始配准,使得2个点云集具有较好的初始位姿,第二阶段采用改进的鲁棒ICP算法完成精确配准,最后计算得到钢轨断面磨耗几何参数。通过在实验室搭建钢轨断面磨耗检测系统试验平台,以试验模拟实测数据常见的轨腰段和轨底段离群点干扰,并以手工接触式磨耗仪的测量数据作为参考基准,对比经典ICP算法和文章所提出的改进算法,分析磨耗检测的精度误差和有效性,并在此基础上,对算法的检测速度进行对比分析,对改进算法的重复性测量精度进行验证,最后在地铁线路上开展轨道检修车实测验证。结果表明,文章所提的改进算法有效提高了在离群点干扰场景下钢轨断面磨耗检测的精度和速度,具有工程实用价值。

The classical Iterative Closest Point(ICP)registration algorithm is commonly applied to rail profile wear detection systems.However,during the operation of track inspection vehicles,the profile data collected by the rail profile wear detection system based on linear structured light is often disrupted by noise from various outliers,leading to significant geometric variations in the rail profile calculation results.Given the dynamic detection accuracy requirements for rail profile wear using track inspection vehicles of±0.5 mm for lateral wear and±1.0 mm for vertical wear in practical applications,it is crucial that the design of wear detection algorithms do not compromise the detection accuracy due to outlier-induced noise in the profile data.To address this,this paper presented an improved two-stage rail profile wear detection algorithm.In the first stage,this algorithm enabled rapid initial registration based on feature point pairs extracted from the rail profile,providing an improved initial pose for the two point clouds.The second stage utilized an enhanced robust ICP algorithm for precise registration,followed by calculating geometric parameters related to rail profile wear.A laboratory experimental platform was set up to evaluate the developed rail profile wear detection system.This setup simulated typical disruptions from outliers in the measured data of rail web and rail base segments,in comparison with the measurements obtained using a manual contact-type wear tester for benchmarking.Moreover,the accuracy errors and effectiveness of wear detection were analyzed by comparing the classical ICP algorithm and articles with the proposed improved algorithm.Furthermore,a comparative evaluation was conducted to examine the detection speed of the algorithms,and the accuracy in repeatability measurements using the improved algorithm was verified.Finally,validation was conducted on a metro line using a track inspection vehicle,and the results highlighted the enhanced accuracy and speed of rail profile wear detection based on the improved algorithm in scenarios with disruptions from outliers,demonstrating the practical engineering value of the proposed algorithm.