基于 MEMS 激光雷达的车辆目标识别算法

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.