关键词: 焊缝起始向量检测, 双目立体视觉, CenterNet网络, 特征融合, 姿态估计

Abstract: To solve the problem that the traditional methods were difficult to obtain the initial weld position and pose of robot,a novel method was proposed to estimate the initial weld position and pose of robot.Feature fusion and the Convolutional Block Attention Module (CBAM) were used to improve CenterNet to extract features effectively.The initial weld vector was obtained by the improved CenterNet,and the edge condition selection algorithm was used to extract the edge line of the workpiece.The initial vector and some corresponding points on the edge were obtained by epipolar constraint matching.Then the initial vectors and some corresponding points on the edge line were obtained by epipolar constraint matching,and the 3D information of the feature points on the workpiece surface was obtained.Experimental results showed that the improved CenterNetout was superior to other algorithms in detecting the initial weld seam vector,and the error of workpiece attitude estimation satisfied the requirements of robustness and precision.

Key words: weld initial vector detection, binocular stereo vision, CenterNet, feature fusion, pose estimation