隧道轮廓激光雷达点云线特征共面约束标定方法

Line feature coplanar constraint calibration method for lidar point cloud of tunnel contour

地铁隧道断面轮廓参数测量过程中,需要对高速激光雷达点云进行高精度系统标定。特别是大场景隧道环境,对标定模板要求高,标定过程复杂,检测精度影响大。针对此问题,本论文提出了一种新颖的适用于地铁隧道轮廓点云的标定方法,并基于双激光雷达测量系统,进行了算法研究。本方法设计了专用的手推行便携式标定系统,利用由点云数据提取的标定板线特征,建立目标函数,通过混合了遗传算法和Levenberg-Marquardt算法的非线性优化方法,寻找全局最优解从而实现对激光雷达的标定。实验结果表明:本方法对于两侧钢轨顶轨的标定误差在±1.5 mm以内;静态测量精度X误差在±1 mm内、Y误差在±4 mm内;当采集系统以5 km/h的速度进行数据采集时,动态测量精度X误差在±4 mm内、Y误差在±6 mm内。本方法能够实现激光雷达的高精度标定,算法鲁棒性强,具有易操作、环境适应性强的特点。

In the measurement of subway tunnel profile parameters,it is necessary to calibrate the highspeed Lidar point cloud with high precision.In particular,the tunnel environment in large scenes has high requirements for calibration templates,complicated calibration process,and great influence on detection accuracy.To solve this problem,a novel calibration method for subway tunnel contour point cloud was proposed in this paper,and the algorithm was studied based on dual lidar measurement system.In this method,a special manual portable calibration system was designed.The objective function was estab⁃lished by using the calibration plate line features extracted from point cloud data,and the global optimal so⁃lution was found through the nonlinear optimization method combining genetic algorithm and Levenberg-Marquardt algorithm to realize the calibration of lidar.The experimental results show that the calibration error of the top rail is within±1.5 mm.Static measurement accuracy X error within±1 mm,Y error within±4 mm;When the acquisition system performs data acquisition at a speed of 5 km/h,the dynamic measurement accuracy X error is within±4 mm and Y error is within±6 mm.This method can realize the high-precision calibration of lidar,the algorithm is robust,easy to operate,and has the characteristics of strong environmental adaptability.