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In order to improve industrial production efficiency, a hand–eye system based on 3D vision is proposed and the proposed system is applied to the assembly task of workpieces. First, a hand–eye calibration optimization algorithm based on data filtering is proposed in this paper. This method ensures the accuracy required for hand–eye calibration by filtering out part of the improper data. Furthermore, the improved U-net is adopted for image segmentation and SAC-IA coarse registration ICP fine registration method is adopted for point cloud registration. This method ensures that the 6D pose estimation of the object is more accurate. Through the hand–eye calibration method based on data filtering, the average error of hand–eye calibration is reduced by 0.42 mm to 0.08 mm. Compared with other models, the improved U-net proposed in this paper has higher accuracy for depth image segmentation, and the Acc coefficient and Dice coefficient achieve 0.961 and 0.876, respectively. The average translation error, average rotation error and average time-consuming of the object recognition and pose estimation methods proposed in this paper are 1.19 mm, 1.27°, and 7.5 s, respectively. The experimental results show that the proposed system in this paper can complete high-precision assembly tasks.

Research of Hand–Eye System with 3D Vision towards Flexible Assembly Application