Kriging点云滤波改进算法及监测试验研究

Improved Kriging point cloud filtering algorithm and monitoring experiment study

为进一步提高三维激光扫描技术的量测精度,从优化滤波算法的角度出发,基于Kriging改进算法,考虑描述对象的空间相关性质,针对点云数据的滤波处理问题,研究点云格网化滤波的优化方法。以实际工程为依托,通过现场监测比对试验,对三维激光点云数据进行格网化处理和分析,将试验得出的变形数据与传统方法的量测数据进行对比。结果表明:基于Kriging滤波的改进算法不仅能够高效识别和提取隧道轮廓断面可视化数据,而且可以高效、准确地获得隧道变形;试验的拱顶下沉数据与传统量测数据较接近,而周边收敛数据则有一定的差异。三维激光扫描技术下的隧道变形监测在一定的环境条件下能较好地反映隧道变形的真实情况,为隧道工程的施工提供有效的安全预警。

In order to further improve the measurement accuracy of 3D laser scanning technology,this paper studies the optimization method of point cloud grid filtering from the perspective of optimization filtering algorithm and improved algorithm based on Kriging,considering the space-related properties of description objects and aiming at the filtering processing problem of point cloud data.Based on engineering practice,grid processing and analysis of 3D laser point cloud data are carried out through field monitoring and comparison test,and the deformation data obtained from the test is compared with the measured data by traditional methods.The results show that the improved algorithm based on Kriging filtering can identify and extract the tunnel contour cross-section's visual data efficiently and obtain the deformation of the tunnel efficiently and accurately.The experimental data of vault subsidence is close to the traditional measurement data,while the peripheral convergence data has some differences.Therefore,tunnel deformation monitoring under 3D laser scanning can better reflect tunnel deformation's real condition under certain environmental conditions and provide a sufficient safety warning for tunnel construction.