# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 1 Introduction  
Recently, the development of 3D laser scanning technology has been rapid. Laser scanning devices can obtain point cloud data by scanning objects [1]. Compared with 2D image data, point cloud data contain depth information and have a wider range of application scenarios. However, due to the limited field of view of laser scanning devices, point cloud registration technology is usually required to match and fuse fragmented point cloud data in applications. For example, in 3D reconstruction, point cloud data captured from different perspectives need to be registered to obtain complete point cloud data [2]; in robot grasping, point cloud registration is needed to estimate the precise pose of the workpiece [3]; in autonomous driving, point cloud registration is used to construct real-time maps and achieve high-precision positioning of the vehicle [4]; in rendering conservation, point cloud fragments are stitched together to improve the efficiency of heritage restoration while avoiding secondary damage to heritage through manual restoration [5]; in the construction of a digital city, point clouds of features obtained from multiple sites are stitched together to create a highly realistic and accurate 3D model of the urban landscape [6]. As a fundamental work, the study of point cloud registration technology has important value and broad application prospects. The iterative closest point (ICP) algorithm [7] is the most widely used and classic algorithm for point cloud registration. It selects the points with the minimum Euclidean distance as corresponding points and obtains the optimal rigid transformation between two point cloud datasets by minimizing the distance between corresponding points in the two sets [8]. However, the ICP algorithm is highly dependent on the initial registration position and is prone to local optimal solutions [9]. At the same time, ICP has low efficiency. To improve ICP, [PERSON] et al. proposed the Go-ICP algorithm, which partitions the subspaces in SE(3) and removes unproductive subspaces using a branch-and-bound method to find global optimal transformation [10]. Although this method solves the problem of local minima and has certain improvements in registration accuracy and iteration speed, it is still sensitive to initialization. [PERSON] et al. introduced Anderson acceleration into the ICP algorithm to accelerate the convergence of objective function and improve robustness of registration [11; 12], but it still does not solve the problem of dependence on the initial position. [PERSON] et al. proposed a fast and robust iterative closest point (FR-ICP) algorithm [13] based on the previous work that introduced [PERSON] acceleration into the ICP algorithm. They used the Welsch function as the metric error and minimized its quadratic surrogate function to obtain the optimal solution. This approach not only accelerated the iteration speed but also further improved the robustness of the algorithm. However, it still cannot solve the problem of dependence on the initial position. To address the problem of dependence on initial position, some researchers have proposed methods that combine initial registration with the ICP algorithm to achieve fast registration. [PERSON] et al. proposed a point cloud registration algorithm based on ISS-SHOT features [14], which uses the ISS algorithm [15] to extract point cloud feature points and perform SHOT feature description [16]. The initial registration is completed by matching point pairs. Although this method has strong descriptiveness, it lacks robustness and easily produces erroneous point matches. [PERSON] et al. proposed a point cloud registration method based on SIFT feature points combined with the ICP algorithm [17]. They used the scale invariant feature transform (SIFT) algorithm to extract feature points from the point cloud and calculated fast point feature histogram (FPFH) features of feature points [18]. In this method, the initial registration is completed by matching points.