实验技术与管理2025，Vol.42Issue(3)：44-53,10.DOI:10.16791/j.cnki.sjg.2025.03.006
基于自旋式激光雷达点云的车厢冻煤残留测量实验技术
Measurement experimental technology for residual frozen coal in train compartments based on point cloud of spin type LiDAR
摘要
Abstract
[Objective]During winter in high-altitude regions,coal transported via railways can freeze onto the walls of train carriages,making it difficult to unload completely.Detecting the residual frozen coal and formulating effective removal plans is essential in such cases.At present,video detection is mainly used for this purpose but often suffers from issues such as inaccurate estimation of residual frozen coal volume and high sensitivity to light conditions.To address these challenges,this article introduces a measurement method for detecting residual frozen coal in train compartments using spin type LiDAR.[Methods]The method first determines the head and tail of a single open-top carriage using periodic changes in the point cloud data.The system then extracts all point cloud data for the specific carriage.Preprocessing of point cloud data is then performed,which includes extracting point cloud data within the carriage based on the relative position of the radar and the vehicle compartment,correcting for contour tilt and removing motion distortions in the point cloud,performing coordinate system transformation on point cloud data and using a motion displacement fusion algorithm to stitch single-frame point clouds,applying statistical filtering and voxel grid methods to simplify the extracted point cloud data,smoothing the point cloud using the moving least squares method to eliminate ghosting artifacts.To slice point cloud data,the carriages are segmented into equal spaces;these segmented point clouds are then projected and filtered.For point cloud contour extraction,the ray 360-degree algorithm and the alpha algorithm are applied.By employing the Shoelace theorem,the cross-sectional area of the point cloud contour is calculated.Multiplying this area by the segmentation spacing determines the volume size of the point cloud in the compartment.[Results]Experiments demonstrated the relationship between voxel grid size and sampling quality.With the increase of voxel grid size,the number of point clouds decreases rapidly;however,the average distance between point clouds increases linearly,and the variance of point cloud distance rises exponentially.Experiments were also conducted to analyze the relationship between slice spacing and estimation accuracy in our proposed fusion volume estimation method.Results showed that,for a fixed detection volume,the calculation accuracy decreased with increased slice spacing.Conversely,at a set slice width,larger detection volumes resulted in a linear increase in the relative error range while reducing the absolute error range.We compared and analyzed the detection accuracy of our proposed fusion algorithm with other algorithms through experiments.The relative error of our proposed fusion algorithm was significantly smaller than that of the other two algorithms,and it decreased as the volume increased.The 360-degree scanning algorithm fluctuated greatly,around 10%,while the alpha algorithm demonstrated more stable results with relative error rates maintained at around 8%.The proposed fusion algorithm demonstrated superior accuracy and stability compared with the other two algorithms.[Conclusions]The winter frozen coal residue measurement method proposed in this research achieved high detection accuracy.This method can be widely applied not only for detecting frozen coal residues in railway transportation but also for evaluating residual goods in open transportation vehicles used on highways or waterways.Furthermore,the detection hardware can be equipped on mobile carriers for identifying concave surfaces,such as ground depressions,slopes,or even cave-like structures.关键词
雷达成像/冻煤体积测量/点云处理/火车车厢Key words
radar imaging/measurement of frozen coal volume/point cloud processing/train carriage分类
交通工程引用本文复制引用
莫祥伦,吴祥庚,董书琪,刘洋..基于自旋式激光雷达点云的车厢冻煤残留测量实验技术[J].实验技术与管理,2025,42(3):44-53,10.基金项目
国家自然科学基金(52374145) （52374145）
