The Research On The Segmentation Algorithm Based On The Difference Of Normal Of Laser Point Cloud

As a new device capable of collecting rich information in real time,LIDAR has been widely used in various fields.Therefore,laser point cloud data processing has become a hot spot both at home and abroad,and the laser point cloud segmentation as the laser point cloud data processing indispensable part of its importance is self-evident.The segmentation of laser point cloud mainly involves removing noise points,calculating normals quickly and accurately,selecting suitable features and segmentation methods,etc.Each step is important to point cloud segmentation.And,as one of the most important attributes of laser point cloud,normal not only plays an important role in point cloud segmentation and feature extraction,but also in surface reconstruction of laser point cloud.First,the principle and composition of velodyne-16 laser radar system are described.By processing the information collected by the laser radar,the point cloud files in the PCD format are obtained and the scattered point clouds are indexed and filtered.Then the existing algorithms of feature extraction of point cloud normals are introduced.According to calculate the exact normal problem in the noise of point cloud data,the existing calculation method for normal improvement is considered in this work,and then based on the improved algorithm and boundary points with different advantages of another algorithm combined,describes the methods of judging the boundary points,by judging whether a point for boundary points,choose the calculation method of normal feature corresponding calculation,so as to improve the speed and accuracy of calculation.Then,the multiscale feature calculation method of point cloud is introduced in this work.First,the laser point cloud is filtered,and a separate group point is removed.Then the normal vector of point cloud is estimated and redirected on large and small scale by this method.The normal difference between the two is calculated,and the DON characteristics under three sets of scales are calculated.Then,the DON feature is used in the region growth segmentation of the point cloud to replace the curvature value in the traditional region growth segmentation to improving the region growth algorithm.The DON features and regional growth segmentation results under different scales are compared and analyzed.In this work,we show the improved algorithm and the traditional algorithm's normals calculation on three data in detail,and compare the computation time with the experimental results.The experiment proves that the algorithm can combine the advantages of the two algorithms.