# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 1 Introduction  
With its fast, accurate and convenient measurement advantages, mobile laser scanning technology is widely used in areas such as unmanned driving, 3D map construction, deformation monitoring, smart city construction, tunnel maintenance and measurement tasks [1; 2; 3; 4], and rail transit with high density and short windows at train stops. The most common method to obtain 3D point cloud data in mobile laser scanning is to combine laser scanners with global navigation satellite system (GNSS) and inertial measurement units (IMUs) to collect data through time synchronization and calibration of unified spatial coordinates and fuse them to generate 3D point cloud data in an absolute coordinate system. [5; 6]. Since there is no GNSS signal in the tunnel scene, the inertial navigation system positioning and attitude errors rapidly accumulate over time and quickly increase to the tolerance limit of the measurement [7]. To this end, some research institutions have developed a tunnel mobile detection system based on relative scanning, which integrates a laser scanner, a rail inspection car and an odometer. The three-dimensional data of the tunnel are obtained by the two-dimensional section obtained by the helical scanning of the laser scanner according to the mileage [8]. The acquired tunnel data does not have true three-dimensional coordinates and true alignment since the system does not have attitudeand position measurement sensors such as inertial navigation. To obtain 3D data of scenes without satellite signals, such as tunnels, some research teams use auxiliary equipment to correct coordinates and obtain point clouds. The Amberg GRP series track measurement system [9] is equipped with a reflective prism on a trolley body. During data acquisition, the total station measures the absolute three-dimensional coordinates of the prism center at intervals to correct the trajectory. The maximum deviation of the point cloud coordinates is 30.3 mm, and the average deviation is 10.4 mm. Despite the high accuracy, the measurement efficiency of this stop-and-go mode is low. In 2017, [PERSON] et al. studied a method of fixing a reflector on the tunnel wall or CPIII control pile, correcting the mileage through the target center, and then combining the design data to restore the three-dimensional point cloud in the absolute coordinate system. Based on the three-dimensional transformation matrix, [PERSON] and [PERSON] et al. studied the unified positioning of the cross-section related to coordinate transformation based on the relative measurement mode and tunnel design data [10; 11]. The points in the coordinate system are converted into the design route coordinate system, and the point cloud data obtained by the relative measurement technology are processed through the three-dimensional coordinate conversion and various transformation parameters to realize the unified positioning and attitude correction of the scanning section and obtain the absolute point cloud data. However, the problem with this method is that the design data may not match the actual construction, or obtaining the design data is difficult due to the track's age. In our previous study [12], we proposed methods such as fitting the design data based on the measured data and correcting the trajectory based on the segmented control points of the tunnel to generate 3D point clouds. However, these methods also have low absolute accuracy, cannot solve inertial navigation drift, and are only suitable for specific tunnels and other problems. In indoor positioning, simultaneous localization and mapping (SLAM) are more commonly used in 3D point cloud reconstruction. Since GNSS signals are not required, the pose estimation can be performed in closed spaces by means of the matching algorithm before and after the point cloud to achieve fast and accurate positioning. SLAM has also been widely used in the complete measurement of urban rail transit projects and the positioning and modeling of underground space [13; 14].