基于激光雷达的智慧交通信息采集算法

Intelligent Traffic Information Acquisition Algorithm Based on LiDAR

针对智慧交通信息采集的实时性和准确性要求,提出了一种基于点云处理的交通路口运动物体的信息采集算法。首先通过VLP-16激光雷达采集路口的点云数据,利用kd-tree优化的帧差法对其进行背景点云的提取和滤除,以得到路口运动物体的点云,再采用dbscan聚类对运动物体的点云进行分割,得到每个单独物体的点云聚类,最后通过PCA算法对每个物体的点云的位置和姿态进行估计。实验证明了kd-tree优化后的帧差法用时只需要0.758 s,速度提升了78.5%,更符合智能交通的实时性要求,同时,PCA对物体位置的估计偏差在1 m以内,偏移角度在2°以内,采用PCA方法对路口运动物体姿态估计的精度较高,符合智能交通数据的准确性要求。

A point cloud processing-based information collection algorithm for moving objects at traffic intersections is proposed for the real-time and accuracy requirements of intelligent traffic information collection.Firstly,the point cloud data of the intersection is collected by VLP-16 LiDAR,and the background point cloud is extracted and filtered using the kd-tree optimized frame difference method to obtain the point clouds of moving objects at the intersection,and then the point clouds of moving objects are segmented using dbscan clustering to obtain the point cloud clusters of each individual object,and finally the position and pose of each object's point cloud are estimated by the PCA algorithm estimation.The experiment proves that the kd-tree optimized frame difference method takes only 0.758 seconds,which improves the speed by 78.5% and is more relevant to the real-time requirements of intelligent traffic.Meanwhile,the deviation of PCA's estimation of object position is within 1 meter and the offset angle is within 2 degrees,and the accuracy of estimating the pose of moving objects at intersections by PCA method is high,which meets the accuracy requirements of intelligent traffic data.