# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 1 Introduction  
With the rapid development of 3D laser scanning technology, it has been widely used in many fields such as robotics [1], reverse engineering [2], geological survey [3] and cultural protection [4]. Due to the influence of the angle of the scanning device, the shape of the scanned object and environmental factors, it is impossible to complete the data collection of the physical scanning at one time. In order to obtain the complete 3D information of the object surface, it is necessary to collect data from multiple angles and blocks of the object, and then splice or register the 3D point clouds collected from different angles, so as to obtain the point clouds containing complete information of the object under the same coordinate system. The point clouds registration [5] is a key step to obtain complete information of the object surface, and it also plays an important role in 3D reconstruction, 3D localization and pose estimation. Take the palletizing and sorting robot as an example, which have been widely used in food, medicine, chemical and other automatic production enterprises, point clouds registration technology is an essential step. It can also provide high-precision services for intelligent mobile robots. At present, the most widely used and classic registration algorithm is the iterative closest point (ICP) algorithm [6]. The algorithm is simple, but it requires a good initial position, and two point clouds must have overlapping parts, otherwise it is easy to fall into the local optimal solution, which leads to poor final registration effect. In recent years, researchers have put forward many improvement schemes based on the originalICP algorithm. For example, reference [7] proposes a cluster iterative closest point method named CICP for sparse-dense point clouds registration, which is a new method that surpassed the concept of density. It can handle registration of point clouds of different densities acquired by the same sensor at different resolutions or from different sensors. Reference [8] proposes a global optimal algorithm named Go-ICP, which integrates the local ICP into the BnB scheme. It can not only ensure the global optimality but also improve the speed of the algorithm. Reference [9] proposes LieTrICP algorithm. It is a robust registration method of two-point sets based on Lie group parameterization, which combines the advantages of Trimmed Iterative Closest Point (TrICP) and Lie group representation, making the algorithm more robust and accurate. Some researchers first use the coarse registration to obtain a good initial registration position, and then use ICP algorithm for fine registration. Reference [10] proposes a feature descriptor based on the rotation volume ratio to describe feature points. Based on the feature point descriptor, coarse registration is used to obtain a good initial transformation matrix, and then the improved ICP algorithm is used to obtain precise transformation matrix. Reference [11] proposes the Fast Point Feature Histogram (FPFH) descriptor, and then the Best-Bin-First (BBF) is used to reduce the data dimension, which greatly accelerates the iteration speed of ICP. Reference [12] first uses sample consensus initial alignment algorithm (SAC-IA) for coarse registration, and then iterative closest point algorithm based on point-to-face is used for fine registration. Other researchers have proposed point clouds registration algorithms different from the ICP model, such as the three-dimensional normal distributions transform (3D-NDT) algorithm based on the probability density model [13], which does not need to calculate the nearest neighbor matching points, and thus improves the calculation efficiency. The 4 PCS (4-points congruent sets) algorithm [14] reduces spatial matching operations by constructing and matching congruent four-point pairs, which accelerates the registration process. Reference [15] uses the deep learning model for point clouds registration.