## 1 Introduction  
With the rapid development of optical measurement technology and three-dimensional (3-D) imaging [1; 2; 3], point cloud data has received substantial attention as a special information format that contains complete 3-D spatial data. The application of the 3-D image information is widespread in the fields of 3-D reconstruction for medical applications [4], 3-D object recognition, reverse engineering of mechanical components [5], virtual reality, and many others such as image processing and machine vision [6; 7]. There have been many efforts to achieve point cloud registration. The classic algorithm for this purpose is the iterative closest point [8], proposed by [PERSON] and [PERSON]. This algorithm can be efficiently applied to registration problems for simple situations. However, if there is significant variance in the initial position of the two cloud points, it is easy to fall into a local optimum and thus increase the possibility of inaccurate registration. In order to provide improved initial parameters, it is necessary to perform the initial registration before accurate registration using algorithms such as the sampling consistency initial registration algorithm [9]. Due to the large capacity and complexity of point cloud data models, describing feature points is one of the most important and decisive steps in the processing for initial registration. Various methods have been developed to obtain feature information, such as local binary patterns (LBP) [10], local reference frame (LRF) [11], signatures of histogram of orientations(SHOT) [12], and point feature histograms (PFH) [13]. These feature operators can only provide a single description for feature information with high feature dimensions and high computational complexity. Other efforts have been made in terms of feature matching. Scale-invariant feature transform (SIFT) [14; 15; 16] utilizes difference of gaussian (DOG) images to calculate key points. It describes local features of images and obtains the corresponding 3-D feature points through mapping relationships. It has certain stability in terms of the change of view and affine transformation; however, the matching speed for this algorithm is the main limitation. The speeded-up robust features (SURF) algorithm can be used to extract the feature points of the image [17; 18; 19; 20] and implement image matching according to the correlation. However, this algorithm relies too much on the gradient direction of the pixels in the local area, which yields unsatisfactory feature matching results. The intrinsic shape signature (ISS) algorithm has been proposed for feature extraction to complete the initial registration process [21; 22]; however, wide range in searching feature point pairs and low computational efficiency are the limitations for this algorithm. The method for interpolating point cloud models using basis functions has been proposed for establishing local correlation to reduce computational complexity [23]. There are some limitations in traditional methods, such as the inability to comprehensively describe feature information and slow matching of feature point pairs. These issues limit the accuracy and speed of 3-D point cloud registration and significantly impacts its application in practical fields. Based on the traditional sampling consistency initial registration, and iterative closest point accurate registration, a new point cloud registration algorithm is proposed herein. The proposed algorithm combines fast point feature histograms (FPFH) feature description with greedy projection triangulation. The FPFH feature descriptor describes feature information accurately and comprehensively, and greedy projection triangulation reflects the topological connection between data points and its neighbors, establishes local optimal correlation, narrows the search scope, and eliminate unnecessary matching times.