Improved Algorithm for 3D Point Cloud Registration Based on the 3DSC

The 3D laser scanning technology has been widely adopted in the field of workshop automatic assembly. Among them, the point cloud registration is a crucial part of the 3D data processing. Among the 3D data processing steps, point cloud registration is an important one. However, existing 3D point cloud registration methods have issues such as mismatching of feature point pairs, long registration time, and poor registration accuracy. In this paper, an LM-NDT point cloud registration algorithm based on 3D shape context feature (3DSC) is proposed.Firstly, the key points of the 3D point cloud are extracted through the intrinsic shape signature (ISS) to enhance the registration efficiency. Then, the features of the key points are described by the 3D shape context feature. According to the feature points, the error pair is processed using the median distance method, and the initial transformation matrix is calculated through SVD decomposition. Finally, the alignment is accomplished by the LM-NDT algorithm. Compared with the NDT algorithm, the proposed algorithm is more accurate and robust when the initial pose transformation is more significant. The test results demonstrate that the accuracy of the algorithm in the shoe and sole can reach 0.019cm, which remarkably improves the registration efficiency of the traditional SAC-IA and LM-NDT algorithms and has certain engineering application value.