Selection of an Algorithm for Assessing the Verticality of Complex Slender Objects Using Semi-Automatic Point Cloud Analysis

Abstract

Remote technologies, including laser scanning, are frequently employed to acquire data describing the geometric condition of engineering objects. The automation of point cloud processing becomes essential for promptly and reliably monitoring changes in their current shape. The article introduces a methodology for generating point clouds, focusing on detecting the shape of the object’s cross profiles and subsequently determining its inclination through simulations and real data recorded using terrestrial laser scanning technology. The simulations enabled the identification of variations in the characteristics of changes in the course of the axis of a slender structure, depending on the adopted calculation method. Point clouds derived from measurements of complex engineering objects facilitated the validation of the assumptions of the proposed methodology. The suggested solution enables the semi-automatic extraction of data from point clouds and the assessment of the geometric state of engineering object axes based on multi-temporal point clouds.