## 4 Registration Algorithms Based on Feature
### Feature Extraction  
It is not appropriate to register all the points. On the one hand, it will greatly increase the computational workload. On the other hand, it is not particularly helpful for registration since there are numerous comparable point cloud parts. It leads to pointless processing and increases the number of feature matches and the probability of erroneous matches. From the perspective of information theory, it is less informative for the region with low uniqueness to register point clouds. Considering that features produced could be unhelpful for registration, uniqueness additionally takes into account the impacts of noise, occlusion, and outliers. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Classifications** & \begin{tabular}{l} **Typical** \\ **Algorithms** \\ \end{tabular}  & **Author** & **Year** & **Strengthens** & **Weaknesses** \\ \hline Random Sample & \ \begin{tabular}{l} RANSAC [72] \\ NAPSAC [73] \\ SC-RamsAC [79] \\ Super-4 PCS [78] \\ SC-PROSAC [76] \\ RANSAC [83] \\ \end{tabular}  &
\begin{tabular}{l} Fischler and Bolles \\ [PERSON] et al. \\ [PERSON] et al. \\ [PERSON] et al. \\ [PERSON] et al. Figure 8: Typical feature-based point cloud registration algorithm process. Figure 9: Chronological overview of registration algorithms based on features [103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134]. Therefore, the repetition of features and the uniqueness of extracted features are significant markers for gauging the effectiveness of feature extraction algorithms. To extract the crucial points, [PERSON] et al. [103] utilized the basic random sampling method. Despite being fairly straightforward and capable of controlling the quantity of points, this technique lacks a mechanism to guarantee that the sampled points are dispersed equally over the point cloud. The spatial difference between the query point and the neighborhood point is parameterized in the [PERSON] et al. [104]-proposed point feature histogram (PFH), which creates a multidimensional histogram to characterize the geometric attributes of the query point k vicinity. The simplicity and rotational invariance of this type of feature extraction approach, as well as its great resistance to sampling density and noise spots, making it a viable option. However, in response to its comparatively high computational amount, [PERSON] et al. [105] further proposed the FPFH, which reduces the computational complexity and improves the operation speed while retaining the PFH characteristics. To address the registration problem in current machine vision, [PERSON] et al. [135] presented a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation. To increase the likelihood that the key points retrieved would be unique in the point cloud, [PERSON] [106] advocated extracting key points with the covariance matrix within a certain radius and selecting points in the region where the minimum value of the covariance matrix eigenvalues varied significantly. [PERSON] et al. [107] extracted feature points by FAST feature extractor, then sorted them according to the Harris measure and selected the top-ranked points as feature points. [PERSON] et al. [108] introduced the histogram of normal orientations (HoNO) for detecting 3D key points on point clouds by calculating the histogram for each point in the normal orientations. In comparison to previous 3D key point detectors, the identified key points are highly reproducible, successfully avoiding planar regions, and situated in the informative regions of the 3D point cloud. [PERSON] et al. [109] proposed a 3 DMatch network with voxels as input. Prior to using a 3D convolutional neural network to learn local geometric patterns and obtain 512-dimensional feature descriptors, the point cloud is first quantified into a voxel representation.