# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 2 Principle and Methods
### Principle of Rapid Target Point Cloud Extraction Method
#### 2.1.3 Target Point Cloud Extraction
To eliminate this error and precisely extract the target portion, the TRSA performs a reverse nearest-point search between \(S^{\prime}\) and \(S_{k}\). For each point in the new scatter plot \(S^{\prime}\), the corresponding closest point is found in the original scatter plot \(S_{k}\). This ensures that the corner points \(A_{0}\), \(B_{0}\), \(C_{0}\), \(D_{0}\) in \(S^{\prime}\) align with the corner points \(A\), \(B\), \(C\), \(D\) in \(S_{k}\). By matching these points, the corresponding target points in the original scatter plot \(S_{k}\) can be identified, as shown in Equation (18). Afterward, the inverse cylindrical unfolding projection is applied to this portion of points, yielding the corresponding target section of the 3D point cloud. The schematic of this process is illustrated in Figure 4.  
\[\text{I}_{T}\cdot\text{K}^{-1}=S^{\prime}\overset{\text{TRSA}}{\rightarrow}S_{k} \tag{18}\]  
In the above process, the 3D target point cloud is extracted from the 2D image. However, since the method used in this paper is based on 2D edge segmentation, a small number of points that do not belong to the target may be present along the target's edges. To address this issue, a simple clustering approach is applied to the 3D point cloud. A mean  
Figure 4: Schematic diagram of 3D target extraction. distance value is calculated for the main target points, and points exceeding this threshold are classified as noise. The K-means clustering method is used, as it is straightforward to implement and allows for rapid elimination of noise points. This enables accurate separation of the target from the surrounding background, ensuring precise extraction of the 3D target point cloud. In this section, we present a method for extracting the target 3D point cloud from 3D data through a series of algorithms. First, after reading the 3D point cloud data \(P_{k}\), the cylindrical unfolding projection method is applied to convert the point cloud into a scatter plot \(S_{k}\), transforming the 3D data into 2D for processing. This is then saved as a grayscale image \(I\). Using edge segmentation, the edge features of the objects in the image are extracted. By analyzing the grayscale value variations between the target and the background, as well as the area occupied by the target pixels, the target point cloud can be filtered out. This is the key step in the process. Then, through the conversion relationship between the 2D and 3D point clouds and the Target Retrospective Search Algorithm (TRSA), the final 3D target extraction result is obtained. Finally, the K-means method is applied to filter out some of the noise points along the edges, resulting in the extraction of the 3D target point cloud. In this target extraction method based on cylindrical unfolding projection, the convolution mask and grayscale image are used to analyze the features of the point cloud on the 2D image. The target extraction process is computationally lightweight, making it scalable to different resolution types, further reducing computational costs. Thus, when applied to LiDAR point cloud data, this method enables accurate, efficient, and lightweight target extraction.