基于激光点云数据的城市轨道交通限界侵限判别算法

Urban Rail Transit Clearance Violation Judgment Algorithm Based on Laser Point Cloud Data

目的:当前常用的城市轨道交通限界侵线判别方法只能进行超限情况的单向判断,无法对超限程度和未超限裕度进行定量化描述,不利于开展精细化的限界健康管控工作,因此需要对城市轨道交通限界侵限判别算法进行研究。方法:基于计算几何中采用向量积法确定点与多边形平面位置关系的判别思路,提出了一种基于激光点云数据的城轨限界侵限判别算法。依托OpenCV软件中的pointPolygonTest函数对该算法进行了实现。开发了北京地铁限界大数据综合平台。在北京地铁车辆跨线运行和新设备加装两类典型场景中对该算法进行了验证。结果及结论:基于激光点云数据的城轨限界侵限判别算法利用矢量积的方式计算向量与多边形交点的数量,通过交点个数的奇偶性来判断检测点与多边形的位置关系。该算法共包含侵限点判断、异常点识别处理和侵限幅值计算等3个过程,有效实现了不同型号车辆在不同线路中限界侵限的精准、高效、自动化判别。开发的北京地铁限界大数据综合平台实现了对侵限数据的可视化展示。该算法在北京地铁车辆跨线运行和新设备加装等场景中进行了实际应用,并取得了良好的应用效果。

Objective:Current methods for determining clearance violations in URT(urban rail transit)can only provide unidirectional judgments for violation cases,lacking the ability to quantitatively describe the degree of violation and the margin of non-violation,which hinders the implementation of refined clearance health management and control.Therefore,research on the judgment algorithm for URT clearance violation is essential.Method:Drawing on the judgment idea of using vector cross product method in computational geometry to determine the position relation between points and polygon planes,a URT clearance violation judgment algorithm based on laser point cloud data is proposed.The algorithm is implemented using the pointPolygonTest function available in the OpenCV software,and a comprehensive platform for Beijing Subway clearance big data is developed.The algorithm is validated in two typical scenarios of Beijing Subway:vehicle cross-line operation and new equipment installation.Result&Conclusion:The proposed algorithm uses cross-product calculations to determine the number of intersections between vectors and polygons,and judges the position relation between the detection point and the polygon by the parity of intersection points.Consisted of three processes:violation point determination,abnormal point identification and processing,and clearance violation amplitude calculation,the algorithm achieves accurate,efficient,and automated judgment of clearance violations for different vehicle models on various lines.The developed comprehensive platform for Beijing Subway clearance big data facilitates visual representation of the clearance violation data.The algorithm is successfully applied in practical scenarios of Beijing Subway vehicle cross-line operation and equipment installation,achieving favorable application outcomes.