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A 3D Seam Extraction and Tracking Method Based on Binocular Structured Light Sensor

机译:基于双目结构光传感器的3D煤层提取与跟踪方法

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To make the robot automatic welding systems more automatic and accurate, the 3D weld seam extraction has become a research hot-spot. In this paper, a seam extraction method for five types of straight-line seam based on point cloud obtained by three-dimensional reconstruction of binocular structured light is proposed. Firstly, the second derivative is used to calculate the inflection point for the rough extraction of seam characteristics. Secondly, according to the shape information of the welding workpiece, the center point of the seam is detected and the least square algorithm is used to fit the seam model. Finally, the 3D welding spot position and pose estimation are solved based on the established mathematical model. The experiments are conducted under five different situations, and the average extraction accuracy of the method can reach 0.19mm. The experimental results indicate that the proposed algorithm can efficiently locate five types of seam and generate the tracking path planning to guide robot manipulation.
机译:为了使机器人自动焊接系统更加自动化和准确,3D焊缝提取已成为研究热点。提出了一种基于双目结构光三维重构得到的点云的五种直线接缝的接缝提取方法。首先,使用二阶导数来计算拐点,以粗略提取接缝特征。其次,根据焊接工件的形状信息,检测出焊缝的中心点,并采用最小二乘算法拟合焊缝模型。最后,基于所建立的数学模型求解3D焊接点位置和姿态估计。实验在五种不同情况下进行,该方法的平均提取精度可达0.19mm。实验结果表明,该算法可以有效地定位五种类型的接缝,并生成跟踪路径规划以指导机器人操纵。

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