# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 3 Results  
The data used in this paper comes from self-collected data from LiDAR equipment and open-source datasets. The equipment used for data collection is the Livox-Mid-360 radar. The scenarios are indoor collections, including indoor point cloud data, storage box point cloud data, and chair point cloud data. The open-source datasets used are the Stanford University point cloud data Dragon and Bunny datasets. As shown in Figure 3, statistical filtering has been applied. The results indicate that this preprocessing technique effectively smooths edge details within the point cloud data. Furthermore, the filtering process not only mitigates the presence of noise and outlier data points, it also maintains the integrity of detailed features. This enhances the accuracy of subsequent point cloud data registration. After applying statistical outlier removal filtering, local feature points were selected from the resulting point cloud data. This feature point selection process was performed on four distinct point cloud datasets: (a) an indoor point cloud captured from varying viewpoints; (b) the Dragon point cloud dataset, with two instances representing the same object but scanned from different positions and orientations; (c) a storage box point cloud dataset, consisting of one partially occluded scan and a more complete scan; and (d) point cloud data of a chair which is captured from different angles and positions resulting in partial occlusions. Figure 4 illustrates the feature points extracted from the source and target point clouds, depicted in red and green, respectively. Figure 4: Feature point selection results of different point cloud data. (**a**) Feature points of indoor data, (**b**) feature points of the dragon data, (**c**) feature points of the storage box data, and (**d**) feature points of the chair data. Figure 3: Comparison of the effects of statistical filtering on different point cloud data. (**a**) Chair data filtering, (**b**) storage box data filtering. Following feature point extraction from the point cloud data, the dynamic neighborhood, \(L\), is computed for each point via Equation (17). A cumulative histogram of projected distances to neighboring points within \(L\) then provides a discriminative feature descriptor, facilitating the establishment of feature point correspondences. Optimal correspondences are determined by minimizing the chi-squared distance, which subsequently allows for the computation of the rotation matrix \(R\) and translation vector \(t\). This completes the registration of the point cloud data. The data used for point cloud data registration are house point cloud data (a), Dragon point cloud data (b), storage box point cloud data (c), and chair point cloud data (d). The registration results are shown in Figure 5.  
In Figure 5, red point cloud data are target point cloud data and green point cloud data are target point cloud data. After the registration algorithm, the two sets of point cloud data overlap and the data become more complete. To validate the rationality and effectiveness of the proposed method, we compared its registration performance against K4 PCSs, a k-dimensional (KD)-tree-based improved ICP method, and 3D-NDT under the same experimental conditions. Registration quality was evaluated using registration time and root mean square error (RMSE). The comparison experiments utilized three point cloud datasets: the Stanford Bunny and Dragon datasets, and a chair dataset acquired using a Livox-Mid360 LiDAR. The Bunny dataset comprised 35,947 points in both the source and target point clouds. The Dragon dataset contained 29,103 points in both the source and target point clouds. The acquired chair dataset consisted of 76,099 points in the source point cloud and 65,433 points in the target point cloud. Registration results for each method are shown in Figure 6.  
Figure 5: Registration results of different point cloud data.