# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 3 LiDAR Target Detection Experiment
### Target Extraction Verification Experiment
#### 3.1.2 Experimental Procedure and Target Extraction  
The LiDAR collects 3D point cloud data of the target, acquiring 3D information such as coordinates, distance, and reflectance values. Each frame of collected data contains 150,000 points. The 3D coordinates and depth values of the point cloud data are extracted, with the points distributed according to their coordinates. The color values are mapped to the reflectance of the scanned points based on their intensity, with brighter colors corresponding to higher reflectance, generating a 3D image, as shown in Figure 6.  
After collecting the data from the LiDAR, the resulting 3D point cloud data are converted into a scatter plot using cylindrical unfolding projection. In the 2D plane, The distance information is mapped to pixel color depth using a linear scale, resulting in a scatter plot as shown in Figure 7a, where the target is located in the central part of the image. The specific steps for 3D point cloud target extraction are as follows: \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Laser band & 1550 nm \\ Detection range & 1.5 m\(-\)250 m(@10\%) \\ Precision value & \(\pm\)2 cm \\ H/V view angle & \(120^{\circ}/22.5^{\circ}\) \\ Frame rate & 10 fps \\ Angle resolution & 0.129\({}^{\circ}\)\(\times\) 0.11\({}^{\circ}\) \\ Total line number & 1800/second \\ Size & 231 \(\times\) 132 \(\times\) 46 mm \\ \hline \end{tabular}
\end{table} 
Table 1: The performance parameters of LiDAR. Figure 6: Raw point cloud data collected by LiDAR. Figure 7: (**a**) Grayscale image (Scattergram); (**b**) grayscale image; (**c**) the filtered grayscale image; (**d**) edge map; (**e**) surface diagram; (**f**) connected block diagram; and (**g**) target section. First, after obtaining the scatter plot of the point cloud, it is converted into a grayscale image (with pixel dimensions of 2405 \(\times\) 1239), transforming the original 3D point cloud data into a 2D-pixel matrix, thus reducing its data complexity. The resulting grayscale image is shown in Figure 7b. 2. After converting the original projection map to a grayscale image, frequency domain filtering is performed. In the frequency domain, a mask is applied to filter out the most frequent background components, which reduces the data size and increases the processing speed for subsequent target extraction. The filtering result is shown in Figure 7c. 3. After filtering, the Sobel operator is used to compute the gradient changes in depth values between pixels. A gradient threshold is set to extract the edges of the image, generating both the edge map and surface map of the point cloud, as shown in Figure 7d,e. 4. After extracting the edge map, the edges of the same object in the image are closely connected, allowing for the calculation of connected regions. These connected regions are then binarized to obtain the connected region map, as shown in Figure 7f. 5. Once the connected regions are identified, an appropriate pixel area threshold is set based on the required size of the target. This allows for the filtering out of other connected regions, leaving only the target portion. This process enables the target extraction in the 2D image, as shown in Figure 7g. 6. After extracting the target portion from the 2D image (Figure 8a), the conversion matrix K between the scatter plot and the grayscale image is used to identify the corresponding target part in the scatter plot. The Target Retrospective Search Algorithm (TRSA) is then applied to accurately obtain the target part, as shown in Figure 8b. 7. The inverse cylindrical unfolding projection is applied to the scatter plot to recover the 3D coordinates of the target point cloud, as shown in Figure 8c. It can be seen that due to the 2D edge extraction process, the extracted target's edges may have some noise points. These noise points are filtered out using the K-means method, completing the target extraction, as shown in Figure 8d.