基于车载激光点云数据的车辆行驶路面坑槽检测方法  

A method for detecting potholes on road surfaces during vehicle driving based on vehicle mounted laser point cloud data

作　　者：李想 王青正 LI Xiang;WANG Qingzheng(College of Information Engineering,Kaifeng University,Kaifeng Henan 475000,China;College of Information Engineering,North China University of Water Conservancy and Electric Power,Zhengzhou 450046,China)

机构地区：[1]开封大学信息工程学院,河南开封475000 [2]华北水利水电大学信息工程学院,郑州450046

出　　处：《激光杂志》2025年第3期227-232,共6页Laser Journal

基　　金：河南省科技攻关计划项目(No.222102210125);河南省高等学校重点科研项目(No.23B520042)。

摘　　要：车辆行驶路面坑槽检测由于车载激光点云数据通常包含大量的点,含噪声点,而直接计算每个点的邻近点,则路面点云提取不准确,导致检测准确性低。因此,研究一种基于车载激光点云数据的车辆行驶路面坑槽检测方法。首先,采用KD树算法和最小二乘法优化的CFS算法对车载激光点云数据展开滤波处理,避免直接计算每个点的邻近点,降低噪声,提高拟合精度,从而准确提取路面点云,通过映射处理将路面点云映射到XOY平面中,生成路面图像。采用伽马校正处理图像低频部分,利用双边滤波处理图像高频部分,融合上述处理结果获得增强后的图像;通过连通域特征确定坑槽区域,并计算坑槽深度以此实现路面坑槽检测。实验结果表明,所提方法有效提取了路面点云,路面图像处理效果较好,准确检测出了5个路面坑槽,并且检测出坑槽的形状与实际形状基本一致,位置偏差小,其具备了良好的图像处理效果和准确的检测结果。Since the vehicle laser point cloud data usually contain a large number of points,including noise points,and directly calculate the adjacent points of each point,the road surface point cloud extraction is not accurate,resulting in low detection accuracy.Therefore,a pothole detection method based on vehicle-mounted laser point cloud data was studied.Firstly,the KD tree algorithm and the CFS algorithm optimized by least square method are used to filter the laser point cloud data of the vehicle,avoid directly calculating the adjacent points of each point,reduce noise and improve fitting accuracy,so as to accurately extract the road surface point cloud,and map the road surface point cloud to the XOY plane through mapping processing to generate road surface images.The low frequency part of the image is processed by gamma correction,the high frequency part of the image is processed by bilateral filtering,and the enhanced image is obtained by fusion of the above processing results.The pothole area is determined by the characteristics of the connected domain,and the pothole depth is calculated to realize the pothole detection.The experimental results show that the proposed method can effectively extract the pavement point cloud,the pavement image processing effect is good,and 5 pavement potholes are accurately detected,and the shape of the detected potholes is basically consistent with the actual shape,and the position deviation is small,which has a good image processing effect and accurate detection result.

关 键 词：车载激光点云 CFS算法 伽马校正 双边滤波 坑槽检测 

分 类 号：TN209[电子电信—物理电子学]