A novel method for dense point cloud reconstruction and weld seam detection for tubesheet welding robot

Abstract

Welding is not yet fully automated, which is mainly limited by the manual drawing of the welding model. A novel method of automatically establishing a dense point cloud model of the tubesheet and detecting the weld seam was proposed to solve the issue in this paper. The multi-sensor system was calibrated with a fast calibration method, and a laser filter algorithm was then applied to fuse the multi-sensor data. The vocabulary tree method was carried out for tubesheet image similarity analysis, followed by the Random Sample Consensus (RANSAC) algorithm used for feature matching, aiming to establish a point cloud model based on epipolar constraint, EPNP and PMVS algorithm. Finally, a weld seam detection algorithm based on voxel point cloud density was proposed to detect the weld seam in the point cloud. After comparison, the reconstruction method has better robustness than the reference. The point cloud measurement results showed that the average row and column length errors of the reconstructed point cloud were both less than 1 %, which can meet the requirements in current welding applications. And the proposed weld seam detection method can reduce the detection error rate from 20.83 % to 9.03 %.