Abstract: Aiming at the many disadvantages of traditional theodolite measurement method,a novel LiDAR-based non-destructive detection algorithm of tower crane verticality is proposed. The point cloud data of the tower crane is obtained by using LiDAR,and the effective point cloud data of the standard section of the tower body is obtained through the pre-processing of point cloud alignment,stitching,de-noising and de-redundancy. The standard node cloud cross-section slice segmentation scheme is designed for the tower body,and the Marching Square algorithm is used to extract the cross-section slice polygon contour lines and determine the vertices of the contour lines and their corresponding centroids. The least squares parameter estimation method is used to fit the linear equation of the center point space,the direction vector of the tower axis line is determined,and vector operations are performed with the z-axis and x-axis of the station-center spatial coordinate system,respectively,and then the tilt angle,tilt azimuth and verticality parameters of the tower axis line are solved. The experimental results show that the tower crane verticality parameters obtained by the two kinds of segmentation scheme designed by the algorithm in this paper are 2.04‰ and 2.49‰ respectively,which are smaller than the results of the traditional theodolite measurement method of 3.02‰. The algorithm in this paper is effective,which can provide a kind of non-destructive detection algorithm for the high-precision monitoring of the verticality of tower crane.

Key words: tower crane verticality, LiDAR, point cloud data, non-destructive detection algorithm, theodolite measurement method