Abstract
Workpiece recognition plays a significant role in automatic painting and provides a basis for the selection path of robot spraying. However, it is not trivial to quickly and accurately recognize the multiple types of complex workpieces densely arranged in the production line. To solve this issue, we propose an online recognition approach for robotic spray-painting applications. Firstly, with a consumer RGB-D camera, the RGB images and depth images only containing a single workpiece are obtained from the densely arranged workpieces by the proposed event-triggered collection method. Secondly, a coarse-to-fine recognition method is proposed to efficiently and accurately identify the workpieces. Specifically, such method applies the S-Mask R-CNN network for quickly coarse classification and segmentation of the workpieces based on the RGB images. Then, incorporating the strong distinctiveness of the FPFH (Fast Point Feature Histogram) feature and the affine invariance in the 4PCS (4-Points Congruent Sets) method, the workpieces are recognized finely based on the point clouds, which is obtained by the depth images and the 2D segmentation results. To evaluate the system performance, over 1500 workpieces from 34 different categories on the spray-painting production line had been tested. According to our experiments, the proposed approach can accurately recognize different types of workpieces within 1.5s, and the recognition accuracy can reach 99.32%.
Published Version
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