RGB-ICP Method to Calculate Ground Three-Dimensional Deformation Based on Point Cloud from Airborne LiDAR

Abstract

With the rapid development of LiDAR technology in recent years, high-resolution LiDAR data possess a great capability to describe fine surface morphology in detail; thus, differencing multi-temporal datasets becomes a powerful tool to explain the surface deformation process. Compared with other differencing methods, ICP algorithms can directly estimate 3D displacements and rotations; thus, surface deformation parameters can be obtained by aligning window point clouds. However, the traditional ICP algorithm usually requires a good initial pose of the point cloud and relies on calculating the spatial distance to match the corresponding points, which can easily lead the algorithm to the local optimum. To address the above problems, we introduced the color information of the point cloud and proposed an improved ICP method that fuses RGB (RGB-ICP) to reduce the probability of matching errors by filtering color-associated point pairs, thus improving the alignment accuracy. Through simulated experiments, the ability of the two algorithms to estimate 3D deformation was compared, and the RGB-ICP algorithm could significantly reduce the deformation deviation (30%–95%) in the three-dimensional direction. In addition, the RGB-ICP algorithm was applicable to different terrain structures, especially for smooth terrain, where the improvement was the most effective in the horizontal direction. Finally, it is worth believing that the RGB-ICP algorithm can play a unique role in surface change detection and provide a reliable basis for explaining the surface motion process.