激光点云孤岛数据高精度滤波算法研究
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1.武汉纺织大学机械工程与自动化学院,武汉 430073; 2.工业雷管智能装配湖北省工程研究中心,武汉 430073

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TP391.9

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Research on high precision filtering algorithm for isolated data of LiDAR point cloud
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1. School of Mechanical Engineering and Automation,Wuhan Textile University,Wuhan 430073,China; 2. Industrial Detonator Intelligent Assembly Hubei Engineering Research Center,Wuhan 430073,China

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    摘要:

    针对车载激光雷达线性测量数据中由于遮挡造成的远离主体点云且成簇状分布的孤岛数据滤除困难问题,提出一种与数字信号处理技术相结合的点云滤波方法。通过对点云数据帧中相邻数据点构造高差序列,采用离散傅里叶变换求解高差序列的幅频响应。经过频域信号的低通滤波与反傅里叶变换,还原出孤岛数据被极大修正的比较信号。通过比较信号对点云数据帧中数据点进行阈值判断,从而滤除孤岛区域点信息。通过实验验证算法性能,结果表明,该滤波方法实现了孤岛数据的全部滤除,同时,一类误差与三类误差均低于1%,滤波对主体点云特征影响较小。满足中大型测量场中对点云滤波的使用要求,具有良好的工程应用价值。

    Abstract:

    Aiming at the difficulty of filtering the clustered and isolated data far away from the main point cloud caused by occlusion in the linear measurement data of LiDAR, a point cloud filtering method combined with digital signal processing technology was proposed. The height difference sequence is constructed for the adjacent points in the data section, the discrete Fourier transform was used to solve the amplitude frequency response of the height difference sequence. After low-pass filtering and inverse Fourier transform of frequency domain signal, the comparison signal with greatly modified isolated data was restored. By comparison signal ,we cloud find the location of isolated points in the point data section , so as to filter the point information of isolated area. The performance of the algorithm is verified by experiments. The results show that the filtering method can filter all the isolated data. At the same time, the class I error and class III error are less than 1%, and the filtering has little impact on the characteristics of the main point cloud.It meets the application requirements of Point Cloud Filtering in medium and large measurement fields, and has good engineering application value.

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郭晓锐,章智涵,张弛,李红军.激光点云孤岛数据高精度滤波算法研究[J].电子测量技术,2021,44(24):116-121

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  • 在线发布日期: 2024-07-02
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