Point Cloud Target Registration Algorithm Based on Local Feature Description

Abstract

Point cloud data has received extensive attention in the field of target recognition and target tracking due to its rich target information, and point cloud registration is one of the basic tasks in point cloud applications. Aiming at the problem that the feature descriptor in the traditional registration algorithm is complicated to calculate and cannot sufficiently describe the features of the point cloud, this paper proposes a local feature descriptor that integrates the local normal vector angle features, projection distance features, and distribution density features of the point cloud. The feature similarity is introduced to determine the corresponding point pairs between the template point cloud and the target point cloud, and the point cloud registration task is completed based on local feature matching. Experiments show that the feature descriptor in this paper is simple to calculate and can sufficiently describe the local features of the point cloud, a good correspondence between points can be established based on the feature descriptor, and the algorithm has good robustness. The registration algorithm proposed in this paper can provide a reference for the fields of target recognition and target pose estimation, etc.