Automatic measurement of grid structures displacement through fusion of panoramic camera and laser scanning data

Abstract

Measuring a large grid structure is always based on the traditional total station, which requires a significant workforce. Aiming at the ring structure's characteristics, this paper fully uses multi-sensor advantages and proposes an automatic identification method of grid structure nodes. This method combines deep learning, panoramic camera, and laser scanning and improves the existing method of measuring the lifting process of the grid structure. The main contributions of this paper are as follows: (1) Aiming at the issue with existing measurement methods relying on complex multi-camera systems, a method based on a single panoramic camera and deep learning is proposed to quickly complete the full-field image target points detection, which provides a basis for the identification of target areas in the point clouds; (2) For the challenge posed by a large number of point clouds models and complex processing, a method of automatically capturing point clouds nodes by integrating panoramic camera and laser scanning was proposed. The projection points of the interest region of the image corresponding to the target region in the point clouds and precise identification of nodes were completed; (3) A node classification method and optimization method based on density peak clustering was proposed to solve the problem of node aliasing and processing node center in some areas of interest, for the classification and displacement calculation of grid structure nodes. The proposed method is validated on the scale frame model of a gymnasium, demonstrating the practicability of the proposed approach.