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.

Defects and vertical short-wave irregularities significantly influence the maintenance of railway tracks and train safety, and hence, they need to be detected and eliminated in the early stages of formation. We studied the principles and algorithms to detect the defects and measure the vertical short-wave irregularities by using a fiber optical gyroscope and an odometer, and the associated software was also developed. The principles of deformation index, namely, the ratio of angular rate to velocity and the method to determine the threshold of defects detection is expatiated. The influence of transfer function in measuring vertical irregularities has long been a tough issue. The relation between the characteristics of deformation index and the wavelength of irregularities is studied by converting the deformation index from time domain to spatial domain. The post-processing module utilizes the results of spectrum analysis of the deformation index to determine the wavelength of track irregularities and eliminate the influence of the transfer function. To improve the accuracy of the calculation results, we analyzed the characteristics of the output of the gyroscope and the odometer, and designed a series of targeted error-elimination methods. The forward and reverse dead-reckoning method is utilized to reduce error accumulation, especially a high frequency noise, when calculating the magnitude of vertical short-wave irregularities. Experimental results show that the repeatability is 0.038 mm when the chord length is 1 m and 0.65 mm when the chord length is 10 m. The accuracy is 0.05 mm when compared with results obtained using a ruler of 1 m. The moving speed of the instrument was approximately 0.6–1.2 m/s during the experiment. Both the accuracy and the efficiency can fulfill the requirements for the measurement of short-wave irregularities.

Wheel polygonal wear of metro deteriorates the vibration environment of the vehicle system, potentially leading to resonance-induced fatigue failure of components. This poses serious risks to operational safety and increases maintenance costs. To address the adverse effects of wheel polygonal wear, dynamic tracking tests and numerical simulations were conducted. The modal analysis focused on the vehicle–track coupling system, incorporating various track structures to explore the formation mechanisms and key influencing factors of polygonization. Test results revealed dominant polygonal wear patterns of the seventh to ninth order, inducing forced vibrations in the 50–70 Hz frequency range. These frequencies closely match the P2 resonance frequency generated by wheel–rail interaction. When vehicle–track coupling is considered, the track’s frequency response shows multiple peaks within this range, indicating susceptibility to resonance excitation. Additionally, rail joint irregularities act as geometric excitation sources that trigger polygonal development, while the P2 force resonance mode plays a critical role in its amplification.

The inertial navigation system (INS)/odometer integrated dead reckoning (DR) is usually used for the positioning and orientation of mobile LiDAR system in the precision surveying of railway tunnels. However, due to the accumulation of pose errors and the creaky method of joint surveying of track control points, it is difficult to achieve the millimeter level absolute accuracy of point clouds relative to the track control network under dynamic conditions, especially when there are gross errors of some control points coordinates. This paper puts forward an algorithm to optimize accuracy of INS/odometer integrated DR with robust constraints of track control network. The errors of DR are divided into two types, which affect the relative accuracy and the absolute accuracy respectively; The concept of virtual mileage coordinate system is proposed, in this coordinate system, the influence of gyro bias error on position is modeled as a circular distribution and estimated indirectly by using the high-precision relative spatial relationship of control points; An absolute accuracy optimization method with constraints by sequence match between positions of surveying equipment and track control points is proposed, and the gross errors of control point coordinates are detected and processed based on reduced weight iteration method. The experimental results of North China high speed railway tunnel show that when the distance interval of control points of correction is 120 m, the root mean square (RMS) error of coordinates in X, Y and Z directions are 0.003, 0.003 and 0.005 m respectively. When the distance interval of control points of correction is 420 m, the RMS in X, Y and Z directions are 0.029, 0.030 and 0.010 m respectively. When there are gross errors of some control point coordinates and the distance interval of control points of correction is 120 m, the RMS of coordinates in X, Y and Z directions are 0.005, 0.006 and 0.006 m respectively, and the gross errors of 4 cm in one direction of the three-dimensional control point coordinates can be detected. The experimental results verified the high accuracy of the algorithm and its robustness to the gross errors of control point coordinates.