# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 4 Experimental Results and Analysis
### Experimental Procedure
For the KD tree based ICP registration, the selection of the maximum distance threshold \(\varepsilon_{ICP}\) between corresponding points also has a certain affect to the registration accuracy. According to the experimental comparison and analysis of the four models' registration, set the parameters \(\varepsilon_{RANSAC}\) = 0.001 and \(\varepsilon_{ICP}\) = 0.4 of the Bunny model, \(\varepsilon_{RANSAC}\) = 0.01 and \(\varepsilon_{ICP}\) = 0.03 of the Armadillo model, \(\varepsilon_{RANSAC}\) = 0.3 and \(\varepsilon_{ICP}\) = 0.3 of the Dragon model and \(\varepsilon_{RANSAC}\) = 0.003 and \(\varepsilon_{ICP}\) = 0.009 of the Drill model. Table 4 shows the registration time for four models of each algorithm. Figure 11 clearly shows the trend of registration time for the four models of each algorithm. It can be seen that the registration time of proposed algorithm is the shortest. Table 5 shows the registration error for four models of each algorithm/s. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Model**} & **Voxel Filter+ 3 DSC +** & **Improved Voxel Filter +** & **Voxel Filter+ USC +** & \multirow{2}{*}{**Proposed Algorithm**} \\  & **RANSAC + ICP** & **3 DSC + RANSAC + ICP** & **RANSAC + ICP** & \\ \hline Bunny & 49.321 & 25.61 & 61.607 & 20.803 \\ Arm Adillo & 25.414 & 15.134 & 33.234 & 10.296 \\ Dragon & 45.74 & 10.302 & 4.649 & 5.78 \\ Drill & 5.654 & 4.69 & 5.131 & 4.386 \\ \hline \end{tabular}
\end{table} 
Table 4: Registration time for four models of each algorithm/s. Figure 10: Time and Error of ICP, ICP+ voxel filter and ICP+ Improved voxel filter. For the Bunny point clouds data, the proposed algorithm is optimal in both registration error and algorithm execution time. In terms of algorithm execution time, the proposed algorithm is reduced by 50% compared with the Voxel filter + 3 DSC + RANSAC + ICP algorithm, and the speed is greatly improved. Since the shape characteristics of the point clouds with the improved voxel filter are more similar to the original point clouds, the 3 DSC + RANSAC + ICP algorithm combining the improved voxel filter also has a faster registration speed, and a shorter algorithm execution time. For the Armadillo point clouds data, the Voxel filter + 3 DSC + RANSAC + ICP algorithm is better than the proposed algorithm on the registration error. However, the registration error of the proposed algorithm is only 0.00058 \(\times\) 10\({}^{-5}\) lower than the Voxel filter + 3 DSC + RANSAC + ICP algorithm, with a very small difference. Compared with the Voxel filter + 3 DSC + RANSAC + ICP algorithm, registration time has been reduced by 50%. When the registration accuracy error is not very large, the execution time of the algorithm can be given priority. For the Dragon point clouds data, in terms of registration accuracy and registration time, the registration effect of the proposed algorithm is the best. Compared with the Voxel filter + 3 DSC + RANSAC + ICP algorithm, the registration time of the proposed algorithm is greatly reduced. The Voxel filter + 3 DSC + RANSAC + ICP algorithm with the improved voxel filter also increases the registration time by more than four times, and the registration accuracy is also higher, indicating that the improved voxel filter can improve the registration speed. Figure 12 shows the original image of the Dragon point clouds registration, and the result under the USC descriptor algorithm without the improved voxel filter. It can be seen that when the USC descriptor algorithm lacking the improved voxel filter is used, the  
Figure 11: Total registration time for four models of each algorithm.