# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 1 Introduction  
In recent years, the rapid development of light detection and ranging (LiDAR) technology has provided strong support for the acquisition and processing of point cloud data, and has promoted technical advancements and expanded applications in related fields [1]. Currently, LiDAR is widely used in autonomous driving, intelligent robotics, drones, surveying and mapping/geographic information systems, construction engineering, environmental monitoring, medical treatment, and other fields [2, 3]. LiDAR offers advantages such as high resolution, long detection range, high real-time performance, and strong anti-interference capabilities. Due to factors such as the shape of the object, the scanning angle of the LiDAR system, and the accuracy of the equipment, point cloud data from different poses and angles must be registered and fused in order to obtain more complete data. The main goal of point cloud registration is to combine point clouds acquired from different viewpoints into a unified and complete three-dimensional model [4]. Many researchers have employed different methods and techniques to optimize this process. For example, the iterative closest point (ICP) method, introduced by [PERSON] [5] and [PERSON] [6], minimizes the distance between two point clouds by calculating rotation and translation transformations. This method is simple and efficient, but it requires a good initial pose to avoid registration failure. [PERSON] [7] proposed an improved iterative closest point (ICP) method, combining random sample consensus (RANSAC), intrinsic shape signatures (ISSs), and 3D shape context (3 DSC) features. Although this method has improved registrationaccuracy and efficiency, it is still highly dependent on the initial pose and sensitive to parameters. [PERSON] et al. [8] proposed the four-point congruent sets (4 PCSs) method, which utilizes co-planar four-point sets for fast registration. However, the 4 PCSs method has high complexity and poor performance when the overlap rates of the two sets of point cloud data are low. Therefore, [PERSON] et al. [9] proposed an improved method, key-point-based four-points congruent sets (K4 PCSs), which refines the point cloud using key points to address issues such as low registration area density, large registration scanning angles, and low computational and memory efficiency. [PERSON] et al. [10] proposed a three-dimensions-normal distributions transform (3D-NDT) method, introducing the normal distributions transform (NDT) to three-dimensional space for the first time, and validated its performance through registration of real-world mining data. This method is faster and more reliable than the traditional ICP method, but it is still sensitive to the initial pose and more susceptible to noise. To mitigate the significant impact of the initial pose on point cloud registration results, many studies employ a coarse-to-fine strategy. This involves performing a coarse registration to obtain a sufficiently accurate initial pose, followed by a fine registration for further refinement [11,12]. While this approach can lead to higher registration accuracy, it often comes at the cost of increased computational time. To address the limitations of existing point cloud registration methods, which often suffer from insufficient accuracy and low efficiency, this paper proposes a novel registration method based on single LiDAR dynamic neighborhood features. In this approach, the centroid of the point cloud serves as the feature point, and the distance projection between this feature point and other points within a dynamic neighborhood is extracted as feature information. By calculating the chi-square distance between these feature vectors [13], the registration of two point cloud datasets is achieved. The size of the dynamic neighborhood is adaptively determined based on curvature, density, and curvature-based wavelet transform detail coefficients [14].