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Structured Light Based High Precision 3D Measurement and Workpiece Pose Estimation

机译:基于结构化光的高精度3D测量和工件姿态估计

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This paper proposes a high precision, robust 3D measurement method and a fast, accurate pose estimation method. The RGB-based Gray code Shifting and Positive/Negative coding method (RGB-GSPN) is introduced to improve the structured light measurement system. This coding method is easy to be decoded. It can eliminate the influence of scattered light and reduce the influence of the objects surface color on the measurement precision. Then, we combine 2D and 3D information to perform pose estimation. SIFT features of 2D images are used to roughly match the position. Subsequently, we send this pose to the Iterative Closest Point (ICP) algorithm as its initial matching position and use ICP for accurate estimation. This can avoid getting into a local optimum and speed up the convergence of the algorithm. Experiments show that the system has a high precision and can estimate the pose of the workpiece accurately.
机译:本文提出了一种高精度,鲁棒的3D测量方法和一种快速,准确的姿态估计方法。引入了基于RGB的格雷码移位和正负编码方法(RGB-GSPN),以改进结构化的光测量系统。这种编码方法易于解码。它可以消除散射光的影响,并减少物体表面颜色对测量精度的影响。然后,我们结合2D和3D信息来执行姿势估计。 2D图像的SIFT特征用于大致匹配位置。随后,我们将此姿势发送给迭代最近点(ICP)算法作为其初始匹配位置,并使用ICP进行准确估算。这样可以避免陷入局部最优状态并加快算法的收敛速度。实验表明,该系统具有较高的精度,可以准确估计出工件的姿态。

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