基于轨型重建的平直度提取算法研究

钢轨焊前检测是保障铁路车辆安全行驶的重要环节,其中钢轨平直度是衡量钢轨质量优劣的重要指标。 针对传统钢轨 平直度检测步骤繁琐、单次测量长度有限和测量效率低等问题,依据三角法测量原理,利用激光轮廓仪交叠获取钢轨轮廓数据, 使用改进的 ICP 算法快速配准点云数据,完成钢轨三维重建。 然后利用自适应中值滤波对平直度参数曲线进行优化,采用模拟 平尺法求解钢轨平直度。 实验结果表明,基于轨型重建的平直度提取算法速度快,精度高,稳定性好,与人工检测的最大测量误 差为 0. 021 mm,与高精度电子平尺最大误差为 0. 011 mm,最大标准差为 0. 006 mm,符合钢轨焊前的平直度检测要求。

Research on flatness extraction algorithm based on rail type reconstruction

Pre-welding rail inspection is a significant step to ensure the safe operation of railway vehicles. Rail straightness is an important index to measure rail quality. Aiming at the problems of cumbersome traditional rail flatness detection steps, limited single measurement length and low measurement efficiency, according to the triangulation measurement principle, the overlapping of laser profiler is used to obtain the rail contour data, and the improved ICP algorithm is used to quickly register the point cloud data to complete the three-dimensional reconstruction of rail. Then the adaptive median filter is used to optimize the flatness parameter curve, and the analog ruler method is used to solve the rail flatness. The experimental results show that the flatness extraction algorithm based on rail shape reconstruction has the advantages of high speed, high precision and good stability. The maximum measurement error with manual detection is 0. 021 mm, the maximum error with high-precision electronic leveling ruler is 0. 011 mm and the maximum standard deviation is 0. 006 mm, which meet the requirements of flatness detection before rail welding.