An Attention-Based 6D Pose Estimation Network for Weakly Textured Industrial Parts

The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts. In the industrial robot production scenarios, the 6D pose estimation of industrial parts mainly faces two challenges: one is the loss of information and interference caused by occlusion and stacking in the sorting scenario, the other is the difficulty of feature extraction due to the weak texture of industrial parts. To address the above problems, this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts, namely CB-PVNet. On the one hand, the voting scheme can predict the keypoints of affected pixels, which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion. On the other hand, the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings. Extensive comparative experiments were conducted on both public datasets (including LINEMOD, Occlusion LINEMOD and T-LESS datasets) and self-made datasets. The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s) comparable to state-of-the-art using only RGB images while ensuring real-time performance. Additionally, we also conducted robot grasping experiments in the real world. The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.