# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 4 Experimental Results and Analysis  
### Experimental Data  
The experimental data in this paper adopts the Bunny, Armadillo, Dragon and Drill models in the point clouds database of Stanford University. The original scale of Bunny1 and Bunny2 point clouds are 35,974, and the original scale of Armadillo1 and Armadillo2 point clouds are 204,800. The original scale of Dragon1 and Dragon2 point clouds are 29,103, and the original scale of Drill1 and Drill2 point clouds are 204,800. The experimental hardware CPU is Intel(R) Core(TM) i5-9400 @ 2.90 GHz processor, the operating system software is Windows 10 Enterprise Edition, and the development environment is PCL 1.8.0 and visual studio 2013.  
In order to reduce the execution time of the algorithm, the improved voxel filter is first used for down-sampling to simplify the point clouds, and control the scale of points after down-sampling to several thousand. Too many feature points will increase the execution time of the algorithm, and too few feature points will affect the registration accuracy.  
Table 1 shows the number of points and ISS feature points of the Bunny model under different grid sizes. The grid size of the Bunny point clouds data is selected by experiment to be 0.005 m, and the grid size of the Armadillo point clouds data is 0.003 m. The grid size of the Dragon point clouds data is selected by experiment to be 0.03 m, and the grid size of the Drill point clouds data is 0.001 m.  
The neighborhood radius searched by the algorithm and grid size are the same, which are 0.005 m, 0.003 m, 0.03 m and 0.001 m, respectively. The final feature points are filtered out by non-maximum suppression strategy, and the selected non-maximum suppression radius is twice the radius of the searched neighborhood, which is 0.01 m, 0.006 m, 0.06 m and 0.002 m, respectively. The final scale of improved voxel filter down-sampling points and extracted feature points are shown in Table 2. Figures 6 and 7 are the feature point plots extracted from Bunny1, Bunny2, ArmAdillo1 and ArmAdillo2 models, respectively, after down-sampling with improved voxel filter. Figures 8 and 9 are the feature point plots extracted from Dragon1, Dragon2, Drill1 and Drill2 models, respectively, after down-sampling with improved voxel filter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Original Point**} & \multicolumn{2}{c}{**Improved Voxel Filter**} \\  & **Clouds** & **Down-Sampling Points** & **ISS Feature Points** \\ \hline Bunny1 & 35,947 & 3006 & 158 \\ Bunny2 & 35,947 & 3112 & 162 \\ Armadillo1 & 204,800 & 2822 & 147 \\ Armadillo2 & 204,800 & 2758 & 138 \\ Dragon1 & 29,103 & 5611 & 283 \\ Dragon2 & 29,103 & 5704 & 272 \\ Drill1 & 204,800 & 1551 & 108 \\ Drill2 & 204,800 & 1503 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of point clouds for different models.  
Figure 8: Feature points plot extracted from Dragon1 and Dragon2 models. (**a**) Dragon1 model. (**b**) Dragon2 model.  
Figure 7: Feature points plot extracted from ArmAdillo1 and ArmAdillo2 models. (**a**) ArmAdillo1 model. (**b**) ArmAdillo2 model.