# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
###### Abstract  
In order to solve the problem of the traditional iterative closest point algorithm (ICPA), which requires a high initial position of point cloud and improves the speed and accuracy of point cloud registration, a new registration method is proposed in this paper. Firstly, the rough registration method is optimized. As for the extraction of the feature points, a new method of feature point extraction is adopted, which can better keep the features of the original point cloud. At the same time, the traditional point cloud filtering method is improved, and a voxel idea is introduced to filter the point cloud. The edge length data of the voxels is determined by the density, and the experimentally verified noise removal rates for the 3D cloud data are 95.3%, 98.6%, and 93.5%, respectively. Secondly, a precise registration method that combines the curvature feature and fast point feature histogram (FPFH) is proposed in the precise registration stage, and the algorithm is analyzed experimentally. Finally, the two point cloud data sets Stanford bunny and free-form surface are analyzed and verified, and it is concluded that this method can reduce the error by about 40.16% and 36.27%, respectively, and improve the iteration times by about 42.9% and 37.14%, respectively. feature extraction; point cloud filtering; edge feature points; iterative closest point (icp); rough registration; precise registration +
Footnote †: journal: Applied Sciences  
[PERSON] et al. [10] put forward the generalized iterative closest point (G-ICP) algorithm, which uses additional information channels to integrate into the used point covariance weights, and improves the registration in geometric feature areas without computational loss. [PERSON] et al. [11] proposed the normal iterative closest point (N-ICP) algorithm, which determines the data association between two point clouds through the 3D structure around the points. The algorithm relies on the least square formula of the alignment problem, which minimizes the error metric according to the surface features (curvature and normal). Feature-based matching method is also a kind of commonly used point cloud registration algorithm in which the key is to find obvious feature points and accurate correspondence. [PERSON] et al. [12] proposed a pose estimation algorithm using point cloud data for pose-picking tasks, which uses the curve set feature (CSF) to describe the points on the surface and can evaluate the pose at the same time. while matching, the origin point pair feature (PPF) algorithm in 2D space is combined with the matching idea of nearest neighbor search, and the rotating matching feature (RMF) is proposed to effectively match the descriptor curve set feature (CSF), providing robustness to noise. [PERSON] et al. [13] proposed a six-degree-of-freedom pose estimation method to solve the flatness measurement problem of laser profilometer, which uses the boundary of point cloud data to obtain the description features of key points, removes the wrong point pairs by neighborhood evaluation, and then clusters the point clouds by geometric consistency, greatly improving the performance of the algorithm in boundary extraction and registration. [PERSON] et al. [14] proposed a robust point cloud registration method based on deep learning. Principal component analysis was used to adjust the initial position between point clouds, and an inverse combination algorithm was used to iterate the neural network to complete the registration. Experiments showed that the registration error of this method was small and it was not easy to fall into the optimal solution, which enhanced the robustness of point cloud registration. [PERSON] et al. [15] introduced the implementation of iterative node placement in radial basis function nonrigid registration to adaptively reduce the registration error. Based on the widely used signature of histogram of orientation (SHOT) descriptors, [PERSON] et al. [16] put forward a binary simplification method for adaptive 3D feature descriptors.