A laser point cloud registration method for local geometric key points

Abstract

The laser point cloud has high density and large amount of data, which will cause the point cloud coarse registration to have a high time cost and unstable registration accuracy. Point cloud fine registration takes the transformation parameters obtained from the coarse registration as the initial value, and usually uses the standard Iterative Closest Point(ICP) algorithm to find the corresponding points and iteratively optimize the transformation parameters. For improving the accuracy and robustness of the laser point cloud registration, this paper proposes to use the 3D Difference-of-Gaussian(DoG) operator to extract the key points with curvature invariance, and then input the key point cloud into 4-Points Congruent Sets(4PCS) algorithm performs coarse registration, and finally uses the standard ICP algorithm to perform fine registration. After using the method in this paper to do registration experiments on three datasets, the effectiveness of the method is verified.