Research On Digital Image Assists Laser Point Cloud Feature Extraction

As the terrestrial, vehicles borne, air borne laser scanning data acquisition system technologies matured, more and more domestic and foreign researchers began to study on 3D modeling based on point cloud. Point cloud feature extraction as an important link in 3D laser scanning modeling, has gradually become one of the research hotspots.It is difficult to understand and realize the existed algorithms of point cloud feature extraction. To deal with these disadvantages, it puts forward a new algorithm to extract features from point cloud indirectly in this paper which named digital image assists point cloud feature extraction, and a detailed process of this algorithm and results of the experiment have been given. The core idea of this algorithm is mapping the points in the point cloud to the pixels in the 2D image,then get features of target object from 2D image, According to the registration relationship between the pixels in 2D image and points in point cloud, the points in point cloud compose the features can be found, using curve and surface fitting to make features more accurate, these curves or surfaces are the features extracted from point cloud. The key problem that needs to be resolved is the establishment of registration relationship between 2D image and point cloud. To avoid confusion, the 2D image acquired by CCD camera is called CCD image here. Since the intensity image of the point cloud can reflects most features of the original point cloud, the registration between point cloud and CCD image can be divided into two steps. First, register the CCD image and intensity image, in view of grayscale variation between CCD image and intensity image is too great to register them by use of traditional image register algorithms which based on gray information, the image register algorithm based on mutual information usually applied in medical image registration is introduced. Improved adaptive threshold Harris corner detection algorithm is used to extract feature points from images. Second, register the CCD image and point cloud, the collinear condition equation is introduced to solve this problem. Since registration relationship between CCD image and point cloud has been established, features extracted from CCD image can be mapped into the point cloud, with curve and surface fitting, the curves or surfaces fitted in point cloud are the features of point cloud. The experiment results show that this algorithm can extract features from point cloud more accurately and with less operations, can be applied in some solutions.