# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 5 Conclusions  
This paper introduces a novel point cloud registration method based on dynamic neighborhood features. The method leverages the projection distance relationship between statistical feature points and other points within the dynamic neighborhood. This approach establishes accurate feature point correspondences and enables robust point cloud registration. By accurately matching feature points and overcoming the limitations of traditional registration techniques that often struggle to simultaneously optimize accuracy and efficiency, this method offers a compelling solution. The incorporation of multiple parameters enhances the robustness of the approach. Furthermore, by using feature points  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Registered Data** & **RMSE/mm** & **Standard Deviations** \\ \hline \multirow{3}{*}{K4 PCSs method} & [PERSON] & 2.86 & \multirow{3}{*}{0.1028} \\  & [PERSON] & 3.089 & \\  & Chair & 2.882 & \\ \hline \multirow{3}{*}{KD-ICP method} & [PERSON] & 0.599 & \multirow{3}{*}{1.520} \\  & [PERSON] & 0.978 & \\  & Chair & 4.207 & \\ \hline \multirow{3}{*}{3D-NDT method} & [PERSON] & 1.587 & \multirow{3}{*}{0.7225} \\  & [PERSON] & 1.931 & \\  & Chair & 3.264 & \\ \hline \multirow{3}{*}{Proposed method} & [PERSON] & 0.698 & \multirow{3}{*}{0.173} \\  & [PERSON] & 0.964 & \\ \cline{1-1}  & Chair & 1.115 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The root mean square error (RMSE) of the method after registration.  
instead of the entire point cloud for registration, the computational burden is reduced, leading to more efficient data processing. The dynamic neighborhood approach also allows the method to adapt to point cloud data with varying scales and densities, enhancing its flexibility and broadening its application potential in complex environments, especially in fields such as autonomous driving, robotic navigation, building reconstruction, and 3D map construction. Integrating this method into practical systems can enhance the capability of processing point cloud data in complex environments. This study was tested only on a limited set of data and did not extend to a wider range of complex environmental data, which presents a limitation. Future work needs to involve testing on more diverse datasets to validate the feasibility of the proposed method and to develop a simpler and more direct guideline for parameter selection.  
Conceptualization, X.L. and Z.W.; methodology, X.L.; software, X.L.; validation, X.L., Z.W. and R.W.; formal analysis, R.W.; investigation, R.W.; resources, Z.W.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, Z.W.; visualization, R.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: