# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
## 3 Experiment
### Results
#### 3.3.3 Point Cloud Registration Algorithm Based on Feature Point Similarity Formula  
After extracting feature points from different scenes, it was necessary to pair these feature points. In this study, the eigenvalue loading ratio was used to pair feature point clusters. Generally, a feature point from the source point cloud may correspond to multiple matches in the target point cloud within the initial matching cluster. Therefore, constraints must be applied to eliminate duplicate and incorrect matching pairs before proceeding with coarse registration. Figure 7 shows the optimized map of the matched feature points obtained after the optimal matching of the two-piece point cloud in the chamber scenario, along with the mapping of the feature point descriptors. Figure 6(a),b illustrate the matching harness optimization maps, where the red connecting lines represent all initial feature point pairs, while the green connecting lines indicate the optimized feature point pairs. The visualization results prove that the algorithm proposed in this study greatly enhanced the accuracy of the pairings and simplified the subsequent coarse registration process. Figure 6(c),d are the feature point descriptor maps. Since the number of matching pairs was too large, the three principal eigenvalues corresponding to the first 80 matching pairs were selected for mapping from both the source and target point clouds, the eigenvalues were normalized, and the values were output as percentages. The comparison between Figure 6(c),d reveals that the loading ratios corresponding to the matched pairs were identical, which also validates the feasibility and accuracy of the wiring harness optimization from a data-level perspective. After the optimization of optimized matched pairs, the sampling consensus initial Registration (SAC-IA) algorithm was utilized, followed by the ICP fine registration accelerated by a KD-tree to derive the final transformation matrix, thus completing the entire registration process. To verify the effectiveness of the algorithm proposed in this study, the feature points extracted in the previous section were aligned under identical experimental conditions. The algorithm proposed in this study was compared analytically with the ICP, FPFH+ICP, and ColorICP algorithms. The number of ICP iterations was uniformly set to 100, with all other parameters unchanged. To reflect the final registration outcomes, the results for all scenes were uniformly colored, with the red point cloud indicating the source point cloud's location and the purple point cloud indicating the target point cloud's location. The main reason for the addition of the ColorICP algorithm was that the experi \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point Cloud** & \begin{tabular}{c} **Original Point** \\ **Cloud** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Feature Points-A** \\ \end{tabular} & \
\begin{tabular}{c} **Time/s** \\ **Feature Points-B** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Time/s** \\ \end{tabular} \\ \hline Bunny & 35,947 & 2679 & 3.398 & 2067 & 6.372 \\ Dragon & 43,764 & 4023 & 4.369 & 3749 & 7.648 \\ Bear doll & 23,282 & 2132 & 2.223 & 1511 & 4.521 \\ Tree stool & 49,061 & 3856 & 5.211 & 4259 & 11.176 \\ Chamber & 131,232 & 6787 & 12.071 & 7149 & 33.843 \\ Office & 131,006 & 6213 & 13.298 & 8910 & 36.419 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the extraction result data of the two algorithms in different scenarios. Figure 7: Optimization maps of matched harnesses and mapping of feature point descriptors. mental subjects in this article were mainly real color point clouds, which contained RGB values. In contrast, other algorithms did not take into account the color information of point clouds, which allowed us to more comprehensively compare the performance of different algorithms in processing real point clouds, thereby improving the contrast and persuasiveness of the experimental results.