# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 1 Introduction
However, due to the few features and strong repeatability of the tunnel scene, it is difficult to complete the registration of point clouds between the front and rear frames, resulting in low positioning accuracy. A possible solution for the above problems and to obtain high-precision 3D point cloud data with absolute coordinates is to use control points combined with inertial navigation to obtain high-precision trajectory data. The rail transit scene is limited by the short skylight time and high operating pressure, and the control points cannot be arranged in a large number and densely distributed. Therefore, it is necessary to make full use of the observation data of the control points to constrain the inertial navigation. This is a typical fixed interval filtering and smoothing problem. The fixed interval smoothing focuses on the optimal estimation of the state of a fixed measurement interval, and all the observations in the measurement interval can be used to estimate the state at any time in the interval. The two main types of fixed interval smoothing algorithms are the two-filter smoothing algorithm [15; 16; 17] and Rauch-Tung-Striebel (RTS) smoothing algorithm [18]. The two-filter smoothing algorithm includes two independent filtering processes, which makes it more complicated to implement and requires storing more variables. The RTS smoothing algorithm combines the reverse filter and the smoothing process into a single reverse recursive form, including a forward filtering process and the reverse smoothing process. The structure is relatively simple, the implementation is convenient, and the calculation efficiency is relatively high. It is more extensively used in engineering [19; 20]. [PERSON] [21] used the RTS smoothing algorithm for track smoothness detection and proposed the concept of dimensionality reduction using the Kalman filter in the vertical and horizontal directions. We draw on the above concepts and apply the RTS smoothing algorithm to solve the problem of constrained inertial navigation and odometry to solve the trajectory in a non-GNSS scenario. For the convenience of expression, we took the high-precision laser scanning sensor as the main body and integrated multi-type sensors (inertial navigation system, odometer, inclination sensor, gauge sensor, etc.) as the track mobile laser measurement system. The scanner was integrated with the inertial navigation system, odometer and inclinometer to establish and track the mobile laser measurement system, and the control points were integrated to establish inertial navigation and odometer constraints. Combined with the RTS smoothing algorithm, a 3D point cloud generation method based on multi-sensor fusion was established. The error between track center points was calculated according to the inertial navigation trajectory and the control point coordinates. The mileage and inertial navigation attitude angle were corrected by the RTS smoothing algorithm to obtain high-precision trajectory data; high precision was used for the inertial navigation roll angle. The static inclinometer was used to correct the inclination, and then the three-axis attitude angle was optimized by the recursive average filtering algorithm to suppress the jitter noise generated by the high-frequency acquisition. Finally, the corrected data were used to generate high-precision three-dimensional point cloud data in the absolute coordinate system, which effectively avoided the sharp increase of deformation and errors in the tunnel-moving scanning-point cloud. The 3D point cloud data could be used for tunnel leakage detection, intrusion detection, completion survey, disease system management and other follow-up tunnel analyses. They also provided good data support for applications such as rail transit three-dimensional scene reconstruction, intelligent railway construction and track measurement.