Vehicle Target Recognition Algorithm Based on MEMS LiDAR

doi:10.12382/bgxb.2021.0822

Abstract

Abstract:

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.

Key words: MEMS LiDAR, pushbroom imaging, vehicle, target recognition

HUO Jian, CHEN Huimin, MA Yunfei, GUO Pengyu, YANG Xu, MENG Xiangsheng. Vehicle Target Recognition Algorithm Based on MEMS LiDAR[J]. Acta Armamentarii, 2023, 44(4): 940-948.

Figures/Tables 15

Fig.1 Schematic diagram of missile borne MEMS LiDAR

Fig.2 Equivalent experimental scene

Table 1 Parameters of one-dimensional MEMS LiDAR

参数	数值
波长/nm	905
最大探测距离/m	75
测距精度/cm	2
视场角/(°)	60
角分辨率/(°)	0.2
帧频/Hz	360

Fig.3 Comparison between MEMS LiDAR point cloud and linear array LiDAR point cloud

Fig.4 Comparison of point cloud images before and after directly filtering

Fig.5 Comparison before and after ground points segmentation

Fig.6 Comparison of matrix formats of unorganized point cloud and organized point cloud

Fig.7 Organized processing of point cloud

Fig.8 Comparison between original point cloud and black-white image

Fig.9 Position diagram of point cloud cluster after morphological operations

Fig.10 Independent point cloud extracted after clustering operations

Fig.11 Distribution histogram of point cloud targets

Table 2 Point cloud feature parameters

编号	特征名称	维数
1	点云数量	1
2	点云外框尺寸	3
3	点云归一化坐标中心	3
4	点云空间离散值	6
5	点云三维协方差矩阵	9
6	点云三维协方差矩阵特征值	3

Fig.12 Effect drawing of target recognition

Table 3 Number of vehicles collected in the experiment

车辆数量及误判数量	种类
车辆数量及误判数量	私家车	公交车	摩托车	厢式车
数量	542	11	43	30
误判数量	26	0	2	4

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