Wang Junya, Zhang Gaofei, You Zheng
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China.
Department of Precision Instrument, Tsinghua University, 10084 Beijing, China.
Microsyst Nanoeng. 2022 Jun 15;8:64. doi: 10.1038/s41378-022-00397-9. eCollection 2022.
MEMS light detection and ranging (LiDAR) is becoming an indispensable sensor in vehicle environment sensing systems due to its low cost and high performance. The beam scanning trajectory, sampling scheme and gridding are the key technologies of MEMS LiDAR imaging. In Lissajous scanning mode, this paper improves the sampling scheme, through which a denser Cartesian grid of point cloud data at the same scanning frequency can be obtained. By summarizing the rules of the Cartesian grid, a general sampling scheme independent of the beam scanning trajectory patterns is proposed. Simulation and experiment results show that compared with the existing sampling scheme, the resolution and the number of points per frame are both increased by 2 times with the same hardware configuration and scanning frequencies for a MEMS scanning mirror (MEMS-SM). This is beneficial for improving the point cloud imaging performance of MEMS LiDAR.
微机电系统光探测与测距（LiDAR）因其低成本和高性能，正成为车辆环境传感系统中不可或缺的传感器。光束扫描轨迹、采样方案和网格化是微机电系统LiDAR成像的关键技术。在李萨如扫描模式下，本文改进了采样方案，通过该方案可以在相同扫描频率下获得更密集的笛卡尔点云数据网格。通过总结笛卡尔网格的规则，提出了一种与光束扫描轨迹模式无关的通用采样方案。仿真和实验结果表明，在相同硬件配置和微机电扫描镜（MEMS-SM）扫描频率下，与现有采样方案相比，分辨率和每帧点数均提高了2倍。这有利于提高微机电系统LiDAR的点云成像性能。
