# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
###### Abstract
Coarse registration matches and obtains optimized initial positions by extracting feature points, which is followed by point cloud registration by fine registration using the iterative nearest point algorithm [14]. The combination of feature extraction and matching improves the applicability of registration algorithms. In feature selection, since curvature features are initially the most sensitive, [PERSON] et al. [15] first proposed to extract feature points by using the principal directions corresponding to the maximum and minimum curvatures of the neighborhood of the point cloud as a descriptor for that key point. [PERSON] et al. [16] transitioned from the classical 2D scale-invariant feature transform (SIFT) descriptor and extended it to 3D for feature point extraction, enabling the detection of feature points at various scales. [PERSON] et al. [17] proposed a coarse point cloud registration method based on ISS feature point descriptors combined with 4 PCS and achieved good robustness with respect to noise and density variations. [PERSON] et al. [18] proposed an approach to extract feature points by using curvature and fast point feature histograms (FPFHs) for fusion. Specifically, they first processed the point cloud with curvature features and then replaced the original Euclidean distance judgment condition by comparing the FPFH features of the points. This approach increases the number of correctly matched pairs and greatly improves the point registration accuracy. Not only the internal structural features of the point cloud but also the color and reflectance of the point cloud can be employed for feature extraction. [PERSON] et al. [19] proposed an algorithm that optimizes the joint photometric and geometric objectives to align two-colored point clouds. This innovative algorithm extends the photometric objective used for aligning RGB-D images to point clouds and surpasses previous methods in terms of accuracy and robustness. However, the above feature descriptors still have their respective deficiencies. For instance, the curvature feature descriptor is only suitable for point clouds with major curvature changes. The SIFT feature descriptor is relatively expensive in computation and, simultaneously, more sensitive to noise and outliers. The performance of ISS combined with the 4 PCS feature descriptor largely depends on the selection of parameters, and inappropriate parameter settings may degrade the detection effect. The FPFH feature descriptor has high time complexity. Additionally, ColorICP often performs poorly in regions where the point cloud's color does not change greatly. Therefore, it is necessary to preprocess the point cloud by denoising before registration and use adaptive parameters to mitigate the impact of the parameter selection on the experimental results. While considering the local neighborhood structure of the point cloud to extract representative feature points effectively and strongly, the efficiency of the algorithm must also be ensured. To deal with the issues of low registration efficiency and high dependence on the initial position characteristic of traditional ICP and its variant registration algorithms, in this study, a point cloud registration algorithm is proposed based on the adaptive neighborhood eigenvalue loading ratio. Firstly, the point cloud resolution is calculated based on the uniqueness of the point cloud data, which are used to determine the average raster density and the radius value of the adaptive spherical neighborhood in adaptive raster filtering. Secondly, the invariance of the internal structural characteristics of the rigid body is reused. The eigenvalue negative loading ratio of the adaptive spherical neighborhood covariance matrix is calculated to screen the surface profile feature points. Thirdly, the eigenvalue similarity formula is employed to match the feature points, and the coarse registration is used together to optimize the initial position. Finally, the feasibility of the algorithm was verified by experiments.