# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 1 Introduction
It proposes a new unsupervised deep learning network-Binary Tree Network (BTreeNet), which learns the features of rotation and translation matrices, respectively. Point clouds registration methods can be divided into two categories: optimization-based methods, and feature-based methods. Optimization-based methods search for corresponding point pairs in the source point clouds and the target point clouds, and the transformation matrix is estimated according to the correspondence. Above two steps will be iterated to acquire the best transformation matrix. The disadvantage of this kind of methods is that complex strategies are required to suppress noise, outliers and density changes, which can increase computational burden. Feature-based methods do not search for corresponding point pairs, they extract the feature points from the source point clouds and the target point clouds. Then the feature descriptor is used to describe them, and the feature is used to estimate the transformation matrix. Therefore, the selection of feature points and feature descriptor must affect the registration effect. Due to the lack of representativeness or insufficient number of feature points, these methods tend to have low registration accuracy. By analyzing and comparing the current point clouds registration algorithms, in order to improve the registration speed and accuracy, this paper proposes a point clouds registration algorithm based on improved voxel filter combining ISS feature points detection [16] algorithm and the USC [17] descriptor. The ISS feature points detection algorithm is used to extract feature points from the point clouds with the improved voxel filter, and the extracted feature points are described by the USC descriptor. Based on the initial registration position obtained after the improved RANSAC coarse registration [18], the ICP algorithm based on KD tree [19] is used to complete the final point clouds fine registration. In summary, the main contributions of this paper include: (1) The traditional voxel filter is improved, and the point closest to the voxel center of gravity in the original point clouds data is used to replace the voxel center of gravity. This method not only maintains the tiny features of the original point clouds, but also improves the accuracy of the point clouds data, which is more conducive to the description of the surface corresponding to the sampling point. (2) USC feature descriptors are used to describe the extracted key points, it only needs to calculate a certain descriptor on each feature point instead of multiple fuzzy descriptors, which reduces memory usage and improves computational efficiency. (3) In the process of RANSAC coarse registration, adding a pre-exclusion step can immediately filter out the wrong hypothetical poses, thereby saving more time to generate more other possibly correct hypothetical poses and reducing the time of coarse registration. (4) The feasibility and effectiveness of the proposed algorithm are verified by comparing with other algorithms and the registration experiment of the VGA connector for monitor. The rest of this paper is organized as follows. Section 2 briefly introduces the proposed algorithm flow. Section 3 introduces the principles of the five main steps of the proposed algorithm in detail. Section 4 discusses the comparison experimental results of the proposed algorithm and other algorithms on several models, and the registration experiment of the VGA connector for monitor, as well as evaluates their accuracy and effectiveness. Finally, Section 5 is the conclusion of this paper.