轻量级MEMS-LIDAR测距去噪算法研究

Research on ranging denoising algorithm on lightweight MEMS-LIDAR

为解决激光雷达定位回波峰值时容易受到噪声干扰而导致测距结果存在误差偏大的问题。采用以两次卡尔曼滤波算法为基础,提出一种能够有效抑制噪声的算法。首先对时域回波进行卡尔曼滤波,然后对连续周期内的峰-峰位置差再次进行周期域的卡尔曼滤波,最后将峰-峰位置差映射为真实的空间距离。实验结果表明,上述算法处理后的距离方差降为去噪前方差的6%以下,平均绝对误差和均方根误差降为去噪前的20%~50%,说明所设计的滤波算法能有效降低噪声影响,使得测距结果更加稳定。

In order to solve the problem of large errors in the ranging results caused by the interference of noise when the lidar locates the echo peak.Based on the two-time Kalman filter algorithm,this paper proposes an algorithm that can effectively suppress noise.First,perform Kalman filtering on the time-domain echo,then perform the period-domain Kalman filtering again on the peak-to-peak position difference in consecutive periods,and finally map the peak-to-peak position difference to the true spatial distance.Experimental results show that the distance variance after processing by the above algorithm is reduced to less than 6%of the denoising front error,and the average absolute error and root mean square error are reduced to about 20%to 50%before denoising,indicating the filtering algorithm designed in this paper.It can effectively reduce the influence of noise and make the ranging result more stable.