铁路长大隧道路基表面沉降链式图像监测方法及模型

The subgrade settlement has become the main indicators of safety monitoring in long and large railway tunnel. To meet the requirements of efficient, non-destructive, real-time and automatic detection of subgrade settlement in tunnels, and aiming at the difficulties of long distance without visibility, no large target signs, and monitoring the multiple sections simultaneously, a method of chain image detection for subgrade surface settlement has been proposed. The variation of pose can be indicated with the help of the characteristics of laser collimating, and the information of variation can be transformed by the target spot with the support of camera monitoring. The measurement results can be transferred by the multi-stage combination relay. The images of cameras and spots in the detecting of chain image of subgrade surface settlement in railway tunnels are taken as the objects of research. According to the principle of image detection and the means of numerical simulation and field testing, many technologies have been researched, including the image acquisition of target spot with high efficiency and stability and the positioning of spot center with high precision. Meanwhile, the high-precision calibration method of pose among cameras under the combination mode of multiple cameras has been analyzed. The effect and compensation of environmental factors on the measurement accuracy of system has been explored. The error transfer rules and the optimal topology structure has been found. Finally, the mechanism of the long-distance laser drift and the real-time compensation in relatively closed field has been revealed, and the rules of the production and accumulation of detection error of chain image detection systems with the long linear structure under the constraint conditions have been found. The proposed model of the chain image detection systems of subgrade surface settlement in railway tunnels has been established to further provide the theoretic guidance in the practice engineering.

铁路长大隧道内路基沉降已成为安全监测的主要指标。为了满足隧道内路基沉降检测的高效、无损、实时和自动化要求，针对病害监测路段距离长不通视，不能装设较大目标标志且要多个断面同时监测的困难，借助激光准直特性指示位姿变化，利用相机检测靶面光斑解译变化信息，再多级组合接力传递测量结果，形成了路基表面沉降的链式图像测量方法。并以铁路路基表面沉降链式图像检测中相机和光斑图像为对象，依据图像测量的原理，采用数值仿真、现场试验等手段，通过参数辨识和数据优化，研究高效稳定靶面光斑图像获得和高精度光斑中心定位技术，分析多相机组合模式下的相机间位姿高精度标定方法，探寻环境因素对系统检测精度的影响及其补偿，找寻系统的误差传递规律及最优拓扑结构，揭示相对封闭场中长距离激光漂移及实时补偿机理和约束条件下长线性结构接力式激光图像测量系统的测量误差产生及累积规律，建立铁路路基表面沉降链式图像测量系统模型，完善测量方法。

铁路长大隧道内路基沉降已成为安全监测的主要指标。为了满足隧道内路基沉降检测的高效、无损、实时和自动化要求，本项目针对病害监测路段距离长不通视，不能装设较大目标标志且要多个断面同时监测的困难，借助激光准直特性指示位姿变化，利用相机检测靶面光斑解译变化信息，再多级组合接力传递测量结果，形成了路基表面沉降的链式图像测量方法。以铁路路基表面沉降链式图像检测中相机和光斑图像为对象，依据图像测量的原理，和铁路沉降病害区段地理特征，构建链式图像沉降监测系统模型，并依据现场需求建立了沉降监测相机的成像模型，完成了相机参数的实验室标定，实现从采集图像到真实世界的转换。在对沉降监测装置光斑形状和靶面材质，激光波长等多个方面研究了高效稳定靶面光斑图像获得和光斑图像质量评价方法，确定了链式沉降监测系统激光发射和接收系统的最优光斑质量标准，以此形成激光器和接收靶面脏污清洗的标准。针对几何特征明显的十字激光光斑图像研究了基于灰度质心法的高精度光斑中心定位方法。借助机器人手眼标定方法，提出了无公共视场的监测站内多相机组合模式下的相机间位姿高精度标定方法，用以解决多相机中间监测站内相机精确位姿传递。针对轨旁监测站相机和靶面易受列车振动，出现靶面或相机轻微偏移，造成沉降误差，提出了基于5特征点和4特征点的监测站内相机和靶面位姿标定算法，修正了振动带来的定位误差。通过粉尘积污实验分析了环境粉尘对光斑中心定位带来的影响；借助相邻监测站间往返测量的平差方法，构建了监测站闭合回路的全系统平差方法。通过系统的内在约束条件完成了链式图像沉降监测系统的累计误差消除，在此基础上，利用嵌入式技术研发了沉降监测终端，借助无线通信和数据库处理技术实现了沉降变形数据的集中监控和预警。并在实验室完成了模拟实验，验证了改监测方法能够满足现场监测要求。并通过该项目培养的硕士研究生有1人获兰州交通大学优秀硕士论文和优秀毕业生称号。