苟辛琳,李则辰,梁永楼,刘涛,梅偌玮,王鼎康.轻量级MEMS LIDAR测距去噪算法研究*[J].电子测量与仪器学报,2021,35(11):177-184
轻量级MEMS LIDAR测距去噪算法研究*
Research on ranging denoising algorithm on lightweight MEMS LIDAR
  
DOI:
中文关键词:  MEMS LIDAR  OSLRF 01  激光雷达  卡尔曼滤波
英文关键词:MEMS LiDAR  OSLRF 01  LiDAR  Kalman filtering
基金项目:四川省教育厅科研项目(18ZA0111)资助
作者单位
苟辛琳 成都信息工程大学电子工程学院成都610225 
李则辰 重庆大学自动化学院重庆400044 
梁永楼 北京华云星地通科技有限公司北京100089 
刘涛 成都信息工程大学电子工程学院成都610225;中国气象局大气探测重点开放实验室成都610225; 
梅偌玮 成都信息工程大学电子工程学院成都610225 
王鼎康 佛罗里达大学盖恩斯维尔32603 
AuthorInstitution
Gou Xinlin College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225,China; 
Li Zechen Chongqing University, Chongqing 400044,China; 
Liang Yonglou Beijing Huayun Shinetek Science and Technology Company,Beijing 100089,China 
Liu Tao College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225,China;CMA. Key Laboratory of Atmospheric SoundingKLAS, Chengdu 610225,China; 
Mei Ruowei College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225,China; 
Wang Dingkang Department of Electrical and Computer Engineer, University of Florida, Gainesville 32603,USA 
摘要点击次数: 463
全文下载次数: 0
中文摘要:
      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。
英文摘要:
      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.
查看全文  查看/发表评论  下载PDF阅读器