# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 1 Introduction  
With the rise of 3D imaging technology, laser scanners have been widely used in many applications, including robotics, intelligent manufacturing, cultural relics protecting, etc. Limited by the scanning angle of the equipment and the shape of the objects, the entire three-dimensional information of the object needs to be collected from multiple views, and it is necessary to align the correspondent point clouds into a complete model. Point cloud registration is a key progress to capture the full shapes of 3D objects. At present, the most classic registration method is the iterative closest point (ICP) algorithm [1], which requires a good initial position and a high overlap rate, and is prone to fall into a local optimal solution. In recent years, many variants on the original ICP algorithm have been proposed to improve registration accuracy and efficiency, such as velocity updating ICP (VICP) [2], generalized-ICP (GICP) [3], globally optimal ICP (Go-ICP) [4], point-to-line ICP (PLICP) [5], etc. [PERSON] [6] proposed a registration algorithm different from the ICP registration model called the three-dimensional normal distribute transform (3D-NDT) algorithm, which based on the probability density model. Compared with ICP, 3D-NDT does not need to calculate the nearest neighbor matching point and thus reduces the computational complexity. [PERSON] et al. [7] proposed a non-rigid registration method by performing k-means clustering on two point clouds and constructing connection relationship. [PERSON] [8] used the principal component analysis (PCA) to achieve the rough pair-wise registration. [PERSON] [9] proposed a point cloud registration method based on extracting overlapping regions to get high accuracy. [PERSON] et al. [10] proposed the super 4-points congruent sets (4 PCS) algorithm, which uses intelligent indexing to reducethe complexity of the original 4 PCS algorithm. The methods in papers [9; 10] have high registration accuracy for point clouds with large initial deviations and are robust to noise and outliers. [PERSON] et al. [11] proposed a registration algorithm combing PointNet++ and ICP, which has fast registration speed and good robustness, but its registration result for sparse point clouds is poor. [PERSON] et al. [12] uses the scale-invariant feature transform (SIFT) functions for initial alignment in combination with the k-nearest neighbor (KNN) algorithm for function comparison and the ICP algorithm weighted for performing accurate registration. Since calculating SIFT is time-consuming, it reduces the registration efficiency. [PERSON] et al. [13] proposed a local feature descriptor based on the rotating volume for point cloud registration. [PERSON] et al. [14] used the oriented bounding box (OBB) regional area-based descriptor to get an initial transformation matrix and ICP algorithm for fine registration. The above methods can be classified to two categories: one is based on optimization; the other is based on features. Optimization-based methods search corresponding point pairs in the source cloud and target cloud at first, then estimate the transformation matrix by the corresponding relation. These two stages will be iterated to find the best transformation. With continuous iteration, the corresponding relation will become more and more accurate. The limitation of this category is that many complicated strategies are needed to overcome noise, outliers, density changes, and partial overlap changes, which will increase the calculation cost. Feature-based methods do not search corresponding points directly; they firstly extract feature points from the target and source clouds and describe them by another method, then features are used to estimate the transformation matrix, which need not an iteration. In those methods, the selection of feature points and their describing methods will determine the registration results. Those methods often result in lower registration accuracy for the reason that the feature points lack representativeness or their amount is not enough.