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

doi:10.13474/j.cnki.11-2246.2019.0009

Abstract

Abstract: 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.

Key words: discrete feature point, point cloud, deformation analysis, 3D reconstruction, multistation

CLC Number:

P258

WANG Chong, FAN Baixing, PI Gang, WANG Deli, XIANG Minzhi. Point cloud deformation analysis method of feature point self-constrained based on multistation[J]. 测绘通报, 2019, 0(1): 44-49.

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