Research on the method of synchronous extraction of 3D surface deformation based on UAV LiDAR point cloud

The surface subsidence is a three-dimensional movement process synchronized in time and space, and the traditional monitoring process is to separate the subsidence and horizontal movement. This processing method brings errors and cannot reflect the synchronization of deformation. Based on the UAV-LiDAR point cloud, this paper proposes a rigid point cloud transformation method to simultaneously extract the three-dimensional movement deformation of the ground surface. The main steps include: connecting domain segmentation of LiDAR point cloud to obtain ground feature point cloud; The ground feature point cloud is regarded as rigid body, FPFH combined with ICP algorithm was used to register the rigid body point cloud, and 3D ground point movement was extracted according to the transformation matrix; The M3C2 algorithm was used to correct the registration error. Take a Wangjiata coal mine of Ordos as an example to apply and evaluate the accuracy. The results show that: the maximum surface subsidence caused by mining is 2. 723 m, and the maximum horizontal movement in X and Y directions is 0. 65 and 0. 75 m, respectively. The internal coincidence accuracy of the three-dimensional surface deformation is 57 mm, while the deformation error corrected by the M3C2 algorithm is 41 mm, and the accuracy is increased by 28%. Compared with the measured deformation values of RTK-GPS, the deformation errors in X, Y and Z directions are 56, 83 and 36 mm. Therefore, it is feasible to extract 3D deformation based on point cloud segmentation and registration. © 2023 China University of Mining and Technology. All rights reserved.