Two-Stage High-Precision Visual 6-D Pose Measurement Method of Thin Parts With Irregular Shape for Precision Assembly

Thin parts are important industrial components in 3C precision assembly tasks. The six-degree-of-freedom (6-D) pose measurement of thin parts is crucial during the alignment process for robotic precision assembly. Due to their small thickness and weak texture, image geometric features from two surfaces couple together, reducing the pose measurement precision. Furthermore, multiple irregular shapes of thin parts bring significant challenges for high-precision pose measurement. In this study, a general two-stage 6-D pose measurement framework based on monocular vision for thin parts with arbitrary irregular cross-sectional shape is proposed. In the first stage, the coarse pose is predicted by a deep neural network with the input of the outer contour of the part in image. The network is rapidly trained exclusively in simulation and subsequently transferred directly for real application. In the second stage, an index named model reprojection contour mismatch (MRCM) is proposed to evaluate the estimated pose, and then, two-step iterative optimization based on the MRCM is executed to obtain the fine pose. The secondary optimization strategy is designed to further improve the precision and make the method applicable to parts with higher thickness. Both the applicability and precision of the proposed method are validated through adequate simulations and experiments. The proposed method demonstrates significantly higher precision in 6-D pose measurement compared to existing methods while maintaining acceptable time efficiency.