Low-error star pattern recognition based on point cloud matching algorithm*

Abstract

Aiming at the problem that the matching rate of traditional star map recognition methods is not high enough, this paper proposes a star map recognition method based on the ICP point cloud matching algorithm based on feature point extraction. Feature points are selected through the change of the local normal vector of the point cloud, and then used Common (ICP) algorithm to realize more accurate registration of point clouds. In order to further reduce the error rate of star map matching, an improved ICP algorithm based on feature points is used to accurately register the point cloud. Finally, the matching conditions of several star maps are obtained through MATLAB tool simulation and the matching rate error rate is calculated. Common ICP algorithm The minimum matching error of is 14.224, and the minimum matching error of the improved ICP algorithm based on feature points is 14.0715. Obviously, the star map matching error rate of the ICP algorithm based on feature points is lower, which also verifies the superiority of the improved ICP algorithm.