A Framework for Synchronous Extraction of Multitype Steel Mesh Features Based on Measured Point Clouds

In large infrastructure projects, the precision and quality of welding large-scale steel mesh sheets are crucial for the safety of the building structure. Traditional manual welding is inefficient and risky; thus, there is an urgent need to replace manual labor with specialized robots equipped with visual recognition systems. However, identifying features like weld points on diverse steel components remains a bottleneck for the application of robotic welding technology. This article proposes a framework for the simultaneous extraction of multitype steel mesh features based on measured point clouds to meet the requirements of a visual-guided robotic welding system. Initially, the Euclidean clustering method is employed to segment the point cloud and obtain all point cloud clusters that belong to the rebar body. Subsequently, the principal component analysis (PCA) and geometric constraints are utilized to merge the point cloud clusters belonging to the same rebar or overlapping rebar. Finally, the feature extraction of rebar’s intersection welding points and lap weld seams is performed using methods such as statistical outlier removal (SOR) and least squares (LSs) cylinder fitting. Additional ablation experiments were conducted to assess the robustness of the framework. To substantiate the feasibility of the framework, the recommended approach was compared with existing point cloud-based and image-based methods. The results indicate that the framework achieves a balance between efficiency and stable robustness while ensuring recognition accuracy.