An Improved Point Clouds Model for Displacement Assessment of Slope Surface by Combining TLS and UAV Photogrammetry

Abstract

TLS can quickly and accurately capture object surface coordinates. However, TLS point clouds cannot cover the entire surface of the target object, due to block of view and limitation of measurement condition. Thus, using it to monitor deformation of slope reduces the detection accuracy of slope surface deformation. To overcome the drawbacks, a method to improve TLS point clouds by UAV photogrammetric point clouds is proposed. The two kinds of point clouds are registered as the new multi-view point clouds by PCA and ICP. The locations of monitoring points are extracted based on HSL color space recognition method from the new multi-view point clouds to analyze the surface displacement. At present, the proposed method has applied in a highway slope in Yunnan Province, and complete point clouds were successfully constructed. A RTK survey was used to compare and verify the proposed method. The verification result demonstrate that the difference of displacement between two measurement methods is less than 10 mm. Comprehensive experiments demonstrate that the proposed method is reliable and meets the slope displacement monitoring standard.