China University of Geosciences, NO.29 Xueyuan Road, Beijing 100083, China.
Geophysical Exploration Academy of China Metallurgical Geology Bureau, NO.139 Sunshine North Street, Baoding 071051, China.
Sensors (Basel). 2020 Jan 23;20(3):645. doi: 10.3390/s20030645.
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.
在进行地铁隧道检测时，需要采集大量的数据，而可用的检测时间很短，但对检测精度的要求却很高。本研究采用移动激光扫描系统（MLSS）对地铁隧道进行检测，并对定位定向系统（POS）的关键技术进行了研究。我们利用惯性测量单元（IMU）和里程计作为 POS 的核心传感器。采用静态初始对准方法获得 MLSS 的初始姿态。考虑到地铁中没有全球导航卫星系统（GNSS）信号，采用前向和后向推算（DR）算法，从任意起始点向两个方向计算 MLSS 的位置和姿态。当 MLSS 经过分布在轨道两侧的控制点时，利用激光扫描仪的测距信息，将控制点的局部坐标传输到 MLSS 的中心。然后，采用四参数变换方法对 POS 误差进行修正，将 MLSS 的三维状态信息从导航坐标系（NCS）转换为局部坐标系（LCS）。该方法可完全消除 MLSS 对 GNSS 信号的依赖，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。在北京至张家口高速铁路的一条隧道中，当用于修正的控制点的距离间隔为 120m 时，点云的三维坐标精度为 8mm，实验还表明，完成一个 5-6km 长的隧道的所有检测工作只需不到 4 小时。此外，武汉地铁线路检测结果表明，当用于修正的控制点的距离间隔分别为 60m、120m、240m 和 480m 时，点云在局部坐标系中的三维坐标精度分别为 4mm、6mm、7mm 和 8mm。
