为解决车辆外廓尺寸测量中存在的测量准确度低、重复性差及三维轮廓重构质 量差的问题，提出一种基于激光点云的动态测量方法． 首先基于车辆外廓尺寸动态测量原 理，对系统测量方案进行了设计． 然后为去除激光雷达在采集过程中产生的噪声和冗余数 据，基于 kd-tree 建立点云空间拓扑关系并采用邻域平均法实现点云数据的去噪，借助最 小二乘法判断局部曲率特征对点云数据进行精简，并通过边界点识别算法对边界特征进 行保护． 最后通过实车试验对所提方法进行验证，并设计出反光镜滤除方案及曲线行驶矫 正模型，实现对试验结果的进一步优化． 试验结果表明: 4 种车型的示值误差均小于 1% ， 重复性最大为 0． 48% ，具有较高的准确度和较好的稳定性，满足国家标准要求; 根据车辆 三维轮廓重构模型，可对超限位置快速定位．

A dynamic measurement method based on laser point cloud was proposed in order to solve the problem of low measurement accuracy，poor repeatability and poor reconstruction quality of three-dimensional ( 3D) contour in the measurement of vehicle contour dimension． Firstly，the method was based on the measuring principle of vehicle contour dimension，and the system measurement scheme was designed． Then，in order to remove the noise and redundant data generated by lidar during data collection，the topology of the point cloud was established based on kd-tree and the neighborhood averaging method was used to denoise the point cloud data，and the point cloud data were simplified by using the least square method to judge the local curvature features，and the boundary features were protected by the boundary point recognition algorithm． Finally，the proposed method was verified by the real vehicle test，and the reflector filter and the curve correction model were designed to further optimize the test result． The test results show that the indicating errors of four types of vehicles are all less than 1% ，with a maximum repeatability of 0. 48% ，which has higher accuracy and better stability to meet the national standard requirements． According to the 3D contour reconstruction model of vehicle，it can locate out-of-gauge position quickly．