Coarse-to-Fine Detection of Multiple Seams for Robotic Welding

TL;DR

This paper introduces a novel coarse-to-fine framework combining RGB images and 3D point clouds for efficient, accurate detection of multiple weld seams in industrial robotic welding, improving over previous single-seam methods.

Contribution

The paper presents a new multi-seam detection framework that integrates RGB and 3D data with deep learning acceleration, enhancing efficiency and accuracy in complex welding scenarios.

Findings

Effective detection of multiple weld seams in various workpieces

High accuracy with sub-millimeter precision

Demonstrated real-world applicability in industrial settings

Abstract

Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The performance of the proposed method has been comprehensively tested on various workpieces featuring both linear and curved weld seams and in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method's efficiency and effectiveness. Videos of the real-world experiments can be found at https://youtu.be/pq162HSP2D4.