Recognition and Pose Estimation Method for Stacked Sheet Metal Parts

To address issues such as detection failure and the difficulty in locating gripping points caused by the stacked placement of irregular parts in the automated sheet metal production process, a highly robust method for the recognition and pose estimation of parts is proposed. First, a decoding framework for parts of a two-dimensional code is established. The morphological closed operation and topology of contours are used to locate the two-dimensional code, and the type of the part is decoded according to the structure of the two-dimensional code extracted by the projection method. Second, the recognition model of the occluded part type is constructed. The edge information of parts is extracted. The contour convex hull is used to split the part contours, and the similarity of segmented contours is calculated based on the Fourier transform. Finally, the occluded parts are located. The corner points of the metal parts are extracted by the adjacency factor of the differential chain code sequence and the contour radius of curvature. The transformation matrix between the part and the standard template is calculated using similar contour segments and contour corner points. A stereo vision system is built to detect and localize the irregular sheet metal parts for experiments, including detection and information extraction experiments of the two-dimensional laser-generated code and detection and positioning experiments of parts under different occlusion rates. The experimental results show that the decoding framework can accurately decode the two-dimensional code made by a laser under low-contrast conditions, the average recognition rate can reach 93% at multiple occlusion rates, the geometric feature extraction algorithm is more accurate than common algorithms and no pseudo-corner points, the localization error is less than 0.8 mm, and the pose angle error is less than 0.6°. The methods proposed in this paper have high accuracy and robustness.