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

机译:基于结构光的高精度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:本文提出了高精度,鲁棒3D测量方法和快速,准确的姿态估计方法。引入了基于RGB的灰码移位和正/负编码方法(RGB-GSPN)以改善结构化光测量系统。该编码方法易于解码。它可以消除散射光的影响,减少物体表面颜色对测量精度的影响。然后,我们组合2D和3D信息来执行姿势估计。 2D图像的SIFT功能用于大致匹配位置。随后,我们将此姿势发送到迭代最接近点(ICP)算法作为其初始匹配位置,并使用ICP进行准确估计。这可以避免进入局部最佳和加快算法的收敛。实验表明,该系统具有高精度,可以精确估计工件的姿势。

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