基于MEMS激光雷达的车辆目标识别算法

Vehicle Target Recognition Algorithm Based on MEMS LiDAR

针对传统线阵激光雷达对地面目标识别准确率低的问题,设计一种基于MEMS激光雷达推扫成像点云识别算法。引入直通滤波和栅格分割算法缩减原始点云数据,有效提高算法的运算速度。结合MEMS激光雷达点云有序化处理方法,提出基于数学形态学的点云聚类算法,将去除地面后的点云分割为相互独立的点云簇。在此基础上使用自适应阈值的分布直方图去噪算法,去除点云簇周围的离群噪点。设计多特征复合判据,直接处理聚类去噪后的三维激光点云,实现对目标的准确识别。分析典型实验条件下的数据处理结果,识别准确率达到了94.9%,表明该方法具有良好的泛化能力和准确性。

In order to solve the problem of low recognition accuracy of traditional linear array Lidar,recognition algorithm based on MEMS LiDAR pushbroom scanning is designed.To reduce the amount of computation,directly filtering and grid segmentation algorithms are introduced to reduce the amount of original point clouds and effectively improve the real-time performance of detection.Combined with the organized processing method of MEMS LiDAR point cloud,a point cloud clustering algorithm based on mathematical morphology is proposed,which divides the point clouds after removing the ground points into independent point cloud clusters.The denoising algorithm based on distribution histogram with adaptive threshold is used to remove the outlier noise points around the targets.On this basis,a multifeature composite criterion is designed to directly process the three-dimensional LiDAR point clouds after clustering denoising to realize the accurate recognition of the targets.The data processing results under different experimental conditions are analyzed,and the recognition accuracy reaches 94.9%,which shows that the method has good generalization ability and accuracy.