三维激光扫描在边坡监测中的应用研究

Application of 3D Laser Scanning in Slope Monitoring

为了提高边坡位移监测精度,增强边坡灾害防范效果,研究三维激光扫描在边坡监测中的应用。利用三维激光扫描仪,获取边坡的点云数据;采用卡尔曼滤波算法,参考边坡点云数据自身的状态转移矩阵与观测资料判断边坡点云数据最优状态,对所采集的含噪边坡点云数据进行降噪处理。扫描降噪后边坡点云数据,利用曲率提取该点云数据的特征点,利用不在同一坐标系内的特征点构建空间坐标系,设置部分特征点为坐标系位置参数,对比不同时间点下坐标系位置参数,确定边坡某点的位移变化情况。结果显示:所研究方法可实现边坡点云数据降噪处理,各特征点所得位移监测与实际位移差异小于0.6 mm,在监测边坡位移情况时所得位移误差均低于3 cm,提高了边坡位移监测精度,为边坡滑坡地质灾害防范提供了科学依据。

In order to improve the accuracy of slope displacement monitoring and enhance the effect of slope disaster prevention,the application of 3D laser scanning in slope monitoring was studied.The point cloud data of slope is obtained by using 3D laser scanner;the Kalman filtering algorithm is used to judge the optimal state of the slope point cloud data by referring to the state transition matrix of the slope point cloud data itself and the observation data,and the noise reduction processing is carried out for the collected noisy slope point cloud data.Scan the slope point cloud data after noise reduction,extract the characteristic points of the point cloud data by using curvature,build a spatial coordinate system by using the characteristic points not in the same coordinate system,set some characteristic points as the coordinate system position parameters,compare the coordinate system position parameters at different time points,and determine the displacement change of a point on the slope.The results show that the research method can realize the noise reduction of the slope point cloud data,the difference between the displacement monitoring and the actual displacement of each characteristic point is less than 0.6 mm,and the displacement error obtained when monitoring the slope displacement is less than 3 cm,which improves the slope displacement monitoring accuracy and provides a scientific basis for the prevention of slope landslide geological disasters.