基于大数据挖掘的激光点云边界法向矢量提取方法

Normal vector extraction of laser point cloud boundary based on big data mining

激光点云边界法向矢量提取过程中点云边界数据难以精准配准,导致提取精度和效率较差。为了更加准确的对激光扫描信息实施分析,提出了基于大数据挖掘的激光点云边界法向矢量提取方法。该方法首先通过摄像机对激光点云实施标定,获取激光点云图像,通过主成分分析、曲面拟合以及滤波算法对获取图像实施去噪处理,最后对图像实施灰度化以及高斯滤波处理,同时结合大津阈值法提取图像中心点法线方向上的灰度中心,以此实现对激光点云边界法向矢量的提取。经试验验证,所提方法提取的法向矢量夹角特征与理想状态下的夹角特征基本一致,且提取效率高、迭代误差小。

In the process of laser point cloud boundary normal vector extraction,the point cloud boundary data is difficult to accurately register,resulting in poor extraction accuracy and efficiency.In order to analyze the laser scanning information more accurately,a method of extracting the normal vector of laser point cloud boundary based on big data mining is proposed.This method first calibrates the laser point cloud through the camera to obtain the laser point cloud image,and then de-noises the acquired image through the principal component analysis,surface fitting and filtering algorithm.Finally,the image is processed by graying and Gaussian filtering.At the same time,the gray center in the normal direction of the image center point is extracted by combining the Otsu threshold method,so as to achieve the extraction of the normal vector of the laser point cloud boundary.The experimental results show that the included angle feature of normal vector extracted by the proposed method is basically consistent with the included angle feature in the ideal state,and the extraction efficiency is high and the iteration error is small.