When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS's dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing-Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5-6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

中文翻译：

---

基于稀疏点辅助DR的地铁隧道MLSS的绝对定位和定向

进行地铁隧道检查时，需要收集大量数据，检查时间短。但是，对检查精度的要求很高。在这项研究中，移动激光扫描系统（MLSS）用于地铁隧道的检查，并研究了定位和定位系统（POS）的关键技术。我们将惯性测量单元（IMU）和里程表用作POS的核心传感器。MLSS的初始姿态是通过使用静态初始对齐方法获得的。考虑到地铁中没有全球导航卫星系统（GNSS）信号，使用向前和向后航位推算（DR）算法从两个方向的任何起点计算MLSS的位置和姿态。当MLSS通过分布在轨道两侧的控制点时，控制点的局部坐标通过使用激光扫描仪的测距信息传输到MLSS的中心。然后，使用四参数转换方法来校正POS的误差，并将MLSS的3-D状态信息从导航坐标系（NCS）转换为局部坐标系（LCS）。该方法可以完全消除MLSS对GNSS信号的依赖性，获得的定位和姿态信息可用于点云数据融合，直接获得LCS中的坐标。在京张高速铁路隧道中，用于校正的控制点的距离间隔为120 m，点云的3-D坐标精度为8 mm，实验还表明，完成5-6 km长隧道的所有检查工作所需的时间不到4小时。此外，武汉地铁线路检查工作的结果表明，当用于校正的控制点的距离间隔为60 m，120 m，240 m和480 m时，该点的3-D坐标的精度局部坐标系中的云分别为4毫米，6毫米，7毫米和8毫米。