# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 2 Rough Surface Matching Based on Octree and Edge Feature Point Algorithm
### Point Cloud Mismatch Pair Elimination  
After filtering and other operations, most of the noise points in the point cloud data have been well removed; however, in many cases, the ideal state cannot be achieved, and there is still the possibility of false matching pairs. Therefore, this paper combines the edge feature point algorithm with the random sample consensus (RANSAC) algorithm to eliminate the false matching pairs, so that the accuracy of the initial data will be guaranteed.  
The first requirement of the edge feature point method is to determine the edge points of data. In this paper, the kd-tree method is used to search the neighboring points in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filtering Method** & **Total Number of** & **Number of Filtered** & **Effective Filtering** & **Filter Rate** \\  & **Point Clouds** & **Point Clouds** & **Number** & **Filter Rate** \\ \hline Single statistical filtering & 45,475  & 41,858 & 3614 & 90.35\% \\ \hline Optimization algorithm used in this paper & & 41,660 & 3812 & 95.3\% \\ \hline Single statistical filtering & 44,256  & 40,346 & 3910 & 97.7\% \\ \hline Optimization algorithm used in this paper & & 40,314 & 3942 & 98.6\% \\ \hline Single statistical filtering & 25,316  & 21,612 & 3704 & 92.6\% \\ \hline Optimization algorithm used in this paper & & 21,577 & 3739 & 93.5\% \\ \hline \hline \end{tabular}  
* Teapot point cloud dataset;
* Bun000 point cloud dataset;
* Free-form surface point cloud dataset.  
\end{table}
Table 1: Number of point cloud filters.  
Figure 9: Visualization of matching relationships between feature points.  
neighborhood, traverse all the points, fit the neighboring points into a curved surface, and use the projection method to calculate the angle between the points and the projected points. When it is greater than the set threshold, the point is identified as an edge point, otherwise, the point is skipped, all edge points are connected, and the result is visualized by MATLAB, as shown in the Figure 10:  
After the edge feature points are eliminated, the RANSAC algorithm is used to eliminate the mismatched point pairs. The algorithm uses randomness to randomly sample feature points, randomly selecting M pairs of feature-matching points to calculate the transformation relationship between the original point cloud and the target point cloud, and then counts the number of feature-matching pairs and satisfying the transformation relationship. By repeating the above process, the transformation relationship of the largest feature-matching pair can be obtained as a correct assumption, and the feature point-matching pair satisfying the transformation relationship will be kept, while the feature point-matching pair not satisfying the transformation relationship will be rejected. The reason why the two methods are combined is that the RANSAC algorithm alone cannot completely eliminate the influence of edge feature points, and the original features of point clouds will not be affected after elimination, which improves the accuracy of point cloud matching.