Lissajous microscanners are very popular in compact laser-scanning applications, such as solid-state light detection and ranging (LIDAR), owing to their high-quality factor and low power consumption. In the Lissajous scanner driven by a two-axis micro-electro-mechanical system scanning mirror (MEMS-SM), the design theory is insufficient to meet the temporal and spatial resolution at the same time. In this paper, the greatest common divisor of the two-axis driving frequency is used as the temporal resolution, the concept of the fill factor (FF) is used to describe the spatial resolution of the scanner, and a general algorithm for calculating the FF is presented. Combined with the characteristics of the Lissajous trajectory, three design rules of the general Lissajous scanner are proposed, and the design theory of the Lissajous scanner enabling MEMS LIDAR is perfected. Experimental results show that the proposed design rules can effectively meet the LIDAR design requirements.
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.
Azimuth and distance measurement are two key technologies of MEMS LIDAR. In order to improve the accuracy of (micro-electronical mechanical system scanning mirror) MEMS-SM angle measurement, this paper proposes an angle estimation algorithm based on unscented Kalman filter (UKF), which can reduce the sensor noise by using the motion model of MEMS-SM. First, the angle measurement is given by the built-in angle sensor or transfer function model of MEMS-SM. Secondly, the dynamic model is established according to the Lissajous scanning mode of MEMS-SM. Then the UKF algorithm can be presented, including the measurement equation and the state equation, where the nonlinear equation is the inverse trigonometric function. Finally, Laser Doppler Velocimeter was adopted as a standard instrument to verify the accuracy of the proposed algorithm. The results showed that the UKF angle estimation algorithm based on MEMS-SM dynamic model improved the accuracy of the built-in sensor’s angle measurement by 5–10 times. And this method is suitable for LIDAR of different scanners’ types and different scanning modes, which can meet the demand of imaging MEMS LIDAR for the accuracy and stability of angle measurement.
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