# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 5 Discussion  
The value of the tunnel laser measurement system and 3D point cloud generation method can be discussed from the following aspects. First, from the working mode, most of the existing railway scanning systems obtain 3D point cloud data based on GNSS and IMU integrated navigation and scanner fusion. Since there is no GNSS signal in the tunnel scene, the traditional tunnel relative scanning system mainly integrates the scanner and odometer to obtain the point cloud data under the relative coordinate system expanded by mileage. To solve the problem of obtaining the absolute coordinates and real geometric forms of point cloud data in a relative coordinate system, [PERSON] et al. and [PERSON] et al. both adopted the method of combining design data with point cloud data in a relative coordinate system to generate three-dimensional point cloud data in an absolute coordinate system based on mileage correspondence. However, this method has problems in that construction may not conform to the design. To solve this problem, [PERSON] et al. proposed a method of designing a data correction trajectory based on measurement data fitting to generate 3D point cloud data in an absolute coordinate system. Some other teams have adopted another method, directly establishing an absolute scanning system, integrating scanners, inertial navigation and odometers and other auxiliary means to make it suitable for tunnel scenes. A typical case is the Amberg GRP5000. The Amberg GRP5000 integrates a scanner, inertial navigation and odometer; a prism is placed above the scanner. Stopping the trolley at intervals during scanning and using the total station to measure the prism coordinates corrects the trajectory. The TMLS established in this study integrates a scanner, inertial navigation, odometer and inclinometer. Placing targets in the scanning section does not need to stop measuring control points halfway, which can improve efficiency, simplify the operation process and make the generated point cloud data more uniform and stable. Secondly, from the perspective of measurement accuracy, generally based on the combination of relative scanning and design data, the absolute accuracy of the point cloud is about 0.1 m. The maximum error of the Amberg GRP5000 point cloud is 30.3 mm. The absolute measurement accuracy of the point cloud in this study is 40 mm. In the tunnel scene, the measurement accuracy of this method was slightly lower than Amberg's, but the efficiency was improved. Finally, at present, we are also studying the point cloud SLAM algorithm. SLAM is more often used for 3D point cloud reconstructions in GNSS-free scenarios. However, due to the characteristics of the tunnel scene with few feature points, the SLAM algorithm is challenged, and corresponding research is needed to adapt it to the tunnel scene. The method we proposed in this study could effectively avoid the deformation of the tunnel and the sharp increase of the error and provide better data support for follow-up tunnel analyses of the rail transit tunnel, such as three-dimensional displays, completion measurements, disease system management and so on. This study has achieved certain results in the research of mobile scanning point cloud generation, but there are still problems that need to be further studied. In the system integration, since the PPS pulse signal cannot be used in a non-GNSS environment, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point ID**} & **North** & **East** & **Height** & **Horizontal** & **Elevation** \\  & **Error(m)** & **Error(m)** & **Error(m)** & **Deviation(m)** & **Deviation(m)** \\ \hline
1 & \(-\)0.0126 & 0.0105 & 0.0009 & 0.0164 & 0.0009 \\
2 & 0.0081 & \(-\)0.0082 & 0.0031 & 0.0115 & 0.0031 \\
3 & \(-\)0.0089 & 0.0294 & 0.0391 & 0.0307 & 0.0391 \\
4 & 0.0072 & 0.0294 & \(-\)0.0054 & 0.0303 time synchronization between the scanner and the IMU equipment is synchronized with the computer system time, and there is a risk of time misalignment.