小波变换技术的三维激光扫描点云数据分类和去噪系统

Classification and denoising system for three-dimensional laser scanning point cloud data using wavelet transform technology

为提升三维激光扫描点云数据的有效性,降低点云数据中的噪声点干扰,设计小波变换技术的三维激光扫描点云数据分类和去噪系统。系统采用三维激光扫描仪获取三维点云数据,并进行精度校验后,利用可编程门阵列和乒乓RAM等硬件将其传送至数据处理。数据处理阶段通过K-Means优化的自组织映射神经网络对接收到的三维点云数据进行分类,通过双树复小波变换技术处理点云数据中的噪声点。处理后的点云数据通过虚拟接口传送至可视化及输出中,实现点云数据的可视化。测试结果显示:该系统可有效完成点云数据的分类处理,获取点云数据类别,并有效处理点云数据中的噪声点,提升点云数据的清晰度,分类和降噪后的点云数据拟合相关系数和决定系数均在0.933和0.927以上,应用效果较好。

To improve the effectiveness of 3D laser scanning point cloud data and reduce noise interference in point cloud data,a 3D laser scanning point cloud data classification and denoising system using wavelet transform technology is designed.The system u-ses 3D laser scanner to obtain 3D point cloud data,and carries out accuracy verification,and transmits it to data processing by using programmable gate array and Ping-Pong RAM.In the data processing stage,the received 3D point cloud data is classified by self-or-ganizing mapping neural network optimized by K-Means,and the noise points in the point cloud data are processed by double-tree complex wavelet transform technology.The processed point cloud data is sent to the visualization and output through the virtual inter-face to realize the visualization of point cloud data.The test results show that the system can effectively complete the classification pro-cessing of point cloud data,obtain point cloud data categories,and effectively process noise points in point cloud data,improve the clarity of point cloud data.The correlation coefficient and determination coefficient of point cloud data fitting after classification and noise reduction are above 0.933 and 0.927,and the application effect is good.