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 %.

焊接还没有完全自动化，主要受限于焊接模型的手工绘制。针对该问题，提出了一种自动建立管板密集点云模型并检测焊缝的新方法。采用快速标定法对多传感器系统进行标定，然后应用激光滤波算法对多传感器数据进行融合。采用词汇树法进行管板图像相似性分析，然后采用随机样本一致性（RANSAC）算法进行特征匹配，旨在建立基于对极约束、EPNP和PMVS算法的点云模型。最后，提出了一种基于体素点云密度的焊缝检测算法，用于检测点云中的焊缝。经过比较，重建方法比参考具有更好的鲁棒性。点云测量结果表明，重建点云的平均行长和列长误差均小于1%，可以满足当前焊接应用的要求。并且所提出的焊缝检测方法可以将检测错误率从 20.83% 降低到 9.03%。