一种基于全站扫描的特征点自约束点云变形分析方法

为了对大坝、高层建筑、滑坡区、采空区等危险变形体进行变形观测，针对智能全站仪、GNSS测量、三维激光扫描等变形监测技术无法很好地实现变形特征点和点云的综合变形分析问题，提出了基于全站扫描特征点自约束点云变形分析方法，获取变形体的离散特征点和整体点云变形数据，利用特征点形变矢量求取点云至模型的形变量，从而刻画变形体的整体形变信息。试验结果表明，利用本文所提出的方法能够成功计算出点云至模型的形变量，经统计分析，所有点的形变量真误差的期望值为-0.04 mm，结果精度为1.2 mm。试验结果能够反映变形体的整体形变信息，且具有较高精度。

Point cloud deformation analysis method of feature point self-constrained based on multistation

In order to monitor the dangerous deformation body, such as dam, high-rise buildings, landslide area and goaf, the intelligent total station, GNSS measurement, 3D laser scanning and other deformation monitoring technology cannot achieve the comprehensive deformation analysis of deformation feature points and point clouds commendably, the point cloud deformation analysis method of feature point self-constrained based on multistation is proposed. The discrete feature points and the whole point cloud deformation data of the deformable body are obtained, and the shape variables of point cloud to the model are obtained using the feature point deformation vector, so as to depict the whole deformation information of the deformable body. The experimental results show that the shape variables of point cloud to model can be successfully calculated by using the proposed method. By statistical analysis, the expected value of the true error of all points is -0.04 mm and the precision of the results is 1.2 mm. The experimental results can reflect the whole deformation information of the deformable body and have high accuracy.