用多视角图像和语义分割快速定位焊缝，减少机器人后处理扫描工作量。
Automated Weld Seam Recognition and 3D Mapping for Robotic Post Processing Using Photogrammetry and Semantic Segmentation
- 多视角拍摄+语义分割识别焊缝位置
- 通过摄影测量重建三维模型并投影焊缝
- 适合大件工件的高效焊缝预定位
准确识别焊缝几何形状对于自动化机器人后处理（如打磨、修整和检测）至关重要。对于大型工件，使用高精度激光扫描仪或结构光传感器进行完整表面扫描耗时且常产生大量无关数据。本文提出一种基于视觉的实验性流程，用于焊缝的近似定位，作为高精度测量前的预处理阶段。该方法旨在降低整体扫描负担与提升数据采集效率。具体包括：从多个视角拍摄工件图像，利用语义分割从图像中识别焊缝，通过摄影测量重建工件三维模型，并将识别出的焊缝投影至重建模型中。
原文摘要 · Abstract (English)
Accurate identification of weld seam geometries is essential for automated robotic post processing operations such as grinding, finishing, and inspection. For large workpieces, complete surface scanning using high precision laser scanners or structured light sensors can be time consuming and often generates substantial amount of data that are not relevant. This paper presents an experimental vision based pipeline for the approximate localization of weld seams. This serves as a preliminary stage before high precision measurement. The proposed approach aims to reduce the overall scanning effort and data acquisition efficiency. The proposed method includes capturing images of the workpiece from multiple viewpoints, identifying weld seams from the images using semantic segmentation, reconstructing the workpiece using photogrammetry, and projection of identified weld seams into the reconstructed model.
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