Abstract
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling. Orientation filtering combined with Euclidean clustering and Principal Component Analysis (PCA)-based iterative segmentation was employed to accurately extract the target body. Lastly, a multi-target feature fusion method was applied for pose estimation to compute an accurate grasping pose. To validate the effectiveness of the proposed method, 102 sets of experiments were conducted and compared with classical methods such as Fast Point Feature Histograms (FPFH) and Point Pair Features (PPF). The results showed that the proposed method achieved a recognition rate of 99.02%, processing time of 2 s, pose error rate of 1.31%, and spatial position error of 3.278 mm, significantly outperforming the comparative methods. These findings demonstrated the effectiveness of the method in addressing the issue of accurate 6D pose estimation of molecular sieve drying bags, with potential for future applications to other complex-shaped objects.