基于主成分分析与曲面拟合的激光点云滤波去噪

为了消除激光点云采集时点云中的噪声点,避免噪声尤其是一些孤立离群点对点云数据质量的影响,将散乱的、含有噪声点云变成规则的、高精度的点云,采用了基于主成分分析与曲面拟合进行点云去噪的方法,首先提出了点云区域的主成分分析计算方法,在主成分分析的法向量进行粗去噪,而后去噪后的点云进行曲面拟合,最后根据点到曲面的距离进行了点云的滤波,得到滤波后的点云。结果表明,该方法去噪效果精度高,尤其针对散乱点云,去噪效果明显,最佳滤波性能误差仅为0.018mm。该研究为散乱激光点云的去噪滤波提供了参考。

Laser point cloud denoising based on principal component analysis and surface fitting

In order to eliminate the noise points in the process of collecting laser point cloud and avoid the impact of noise the data quality of the point cloud, especially some isolated outliers. The scattered and noisy point cloud is transformed into regular and high-precision point cloud, and the method of point cloud denoising based on principal component analysis and surface fitting is adopted. In this paper, a point cloud denoising method based on principal component analysis and surface fitting was proposed. Firstly, the principal component analysis method of point cloud region was proposed. Then, the principal component analysis normal vector was used for rough denoising, and the rough denoising point cloud was used for surface fitting. Finally, the point cloud was filtered according to the synthetic distance between the point and the surface. The experimental result shows that the denoising effect is accurate. From the above experimental results and analysis, it can be seen that this method has high filtering accuracy through fitting operation. The algorithm is simple in structure and can retain the details effectively, the error of the best filter performance is only 0.018mm. This study provides a reference for the denoising and filtering of scattered laser point clouds.