## 4 Analysis of Experimental Results
### Quantitative Analysis of ISS Feature Points  
When using the 4 PCS algorithm based on ISS feature points for coarse alignment, the number of extracted ISS feature points affects the speed and accuracy of coarse alignment. In order to verify the feasibility and effectiveness of this algorithm, the optimal number of ISS feature points needs to be determined, and the following quantitative analysis is conducted on the effect of different numbers of ISS feature points on coarse alignment. The initial number of source point clouds is 4,137,760, and the number of target point clouds is 4,781,288. Considering that the coarse alignment involves aligning the source point clouds with the target point clouds by the positional transformation, the quantitative analysis is based on the target point clouds by increasing about 500 feature points each time to carry out nine groups of experiments. The experiments are set up with the key parameters of the ISS feature points: the radius of the spherical neighborhood of the covariance matrix and the number of feature points extracted from the source and target point clouds. The results of the coarse alignment experiments are shown in Table 2, in which the running time and the RMSE are the evaluation indexes of this quantitative analysis. The number of ISS feature points of the target point cloud extracted by setting the radius of the spherical neighborhood of the covariance matrix essentially meets the experimental requirements. It can be seen from the data in Table 2 that with the increase in the number of extracted ISS feature points, the running time of the algorithm shows a trend of increasing. On the contrary, the value of the RMSE shows a trend of decreasing, which means that the accuracy of the algorithm is continuously improved with the increase in the number of feature points. The running effect of the 4 PCS coarse alignment algorithm with different numbers of feature points is shown in Figure 8. From Figure 8a, it can be seen that when the number of ISS feature points of the target point cloud is extracted to be 996, the source and target point clouds are still in the initial spatial position, which also explains the invalidity of the RMSE values in Table 2. From Figure 8b-g, it can be seen that when the number of extracted feature points is between 1500-4000, the spatial position of the source point cloud has a tendency to be close to the target point cloud. From Figure 8g-i, it can be concluded that when the number of feature points reaches 4000-5000, the spatial position of the two point clouds essentially reaches the overlap state. This is combined with Table 2 to analyze the effect of coarse alignment for the number of points around 4000-5000. When the number of feature points is 3998, its running time is 4.59 s, and its accuracy measure RMSE value is 2.438 mm, which is 1.166 mm higher than 1.272 mm for 4998 feature points and 1.058 mm higher than 1.38 mm for 4496 feature points, and the difference is more than 1 mm. Since the higher the RMSE value, the lower the accuracy, the coarse alignment accuracy at this number of points is lower. When the number of feature points is 4496, although its accuracy measure RMSE is 0.108 mm higher than that of 4998 feature points, its running time is 6.57 s, which is 2.991 s less than that of 9.461 s for 4998 feature points. Considering the influence of the difference between the two on the running time and the accuracy on the alignment, the number of ISS feature points of the target point cloud is finally chosen to be 4496 as the amount of data used for the 4 PCS coarse alignment.