## 2 Materials and Methods
### SLAM Technology 3D Reconstruction  
Simultaneous localization and mapping (SLAM) can perform real-time localization and map construction [19]. In the unknown three-dimensional space, it obtains 3D spatial environmental information through its own sensors, determines the pose in 3D space according to its own movement path to sense the location changes of different objects in 3D space, and finally constructs a 3D mapping. Laser SLAM is a method based on laser ranging and positioning, which collects 3D scene information through LIDAR to present a series of points with 3D spatial information attributes. By registration and comparison of 3D point clouds of the two scenes acquired at different times, the distance and attitude change of the relative motion of the LIDAR are calculated, thus the construction of the 3D scene is completed. The laser SLAM system framework mainly comprises sensor data, front-end, back-end, mapping, and other modules. The system flow is shown in Figure 2.  
The front-end processing (laser odometer, etc.,) is to match the data feature points at the frequency of each frame of the sensor, and after initialization, to obtain the initial value of the pose of each frame by using the geometric method. Since the front-end processing only refers to the previous frame or frames, the accumulated error will increase. Therefore, the back-end uses the frequency of key frames to satisfy the feature matching and reduces the number of frames to obtain the initial value of the key frame pose. The threshold value can be set for global optimization in segments. The Loopback detection can find the previously passed place through data alignment, find the loopback that uses a similar transformation method to adjust the poses of key frames associated with the closed-loop frames, optimize all the key frames and points within the loopback, and finally, perform global optimization. The final optimized data is the scene map.  
The LOAM (Lidar Odometry and Mapping in Real-time) algorithm [20] is one of the representatives of real-time laser SLAM. It was first proposed by [PERSON] and [PERSON], aiming to solve the problem of localization, navigation, and mapping of mobile robots when operating in an unknown environment. The LOAM algorithm is a laser-matching SLAM method, which constructs a 3D scene mainly through scan registration,  
Figure 1: 3D model diagrams of different technologies ((**a**) shows the 3D model of UAV; (**b**) shows the 3D model of airborne lidar).  
Figure 2: SLAM system flow chart.  
LIDAR odometry, LIDAR mapping, and pose integration. The LOAM-Livox [21] algorithm is an open-source algorithm developed by the MaRS lab of the University of Hong Kong for the Livox LIDAR SLAM technology. The LOAM algorithm is a real-time, low-drift LIDAR ranging method using 3D LIDAR for state estimation and mapping. It divides complex SLAM problems into high-frequency motion estimation and low-frequency environment mapping. It uses the collaboration of ranging and mapping algorithms to seek to optimize a large number of variables simultaneously for real-time accurate motion estimation and mapping [22]. The LOAM-Livox algorithm still uses the same way of point cloud extraction as LOAM, extracting edge points and plane points by curvature, but filtering out those close to the viewpoint edge, or with too large or small reflection intensity, small plane angle, and occluded points when selecting candidate feature points. It then uses sparse features to calculate the positional transformation, and adds a loopback detection module, using a \"bag-of-words model\" for matching. Finally, the graph optimization method is used to establish constraints and optimize each pose node to obtain the scene model.