Pose estimation of industrial pipe fittings based on large kernel attention improvement
In the task of six-degree-of-freedom pose estimation,the existing algorithms cannot accurately recognize the workpieces with weak texture and occluded placement in real settings.To improve the accuracy of workpiece recognition,an improved pose estimation algorithm based on depth learning was proposed.A large kernel attention was first added to the encoder-decoder architecture to construct a visual attention network so that the network could focus on uncertain key points and enhance feature extraction capability.Then,the candidate pose was obtained according to the key points corresponding to building a dense point-to-point relationship.The experimental results show that the recognition accuracy of the algorithm is 57.4%on the public dataset and 62.1%on the self-built industrial pipe fittings dataset,respectively.Compared with the surface embeddings(Surfemb)algorithm,the accuracy is improved by 5.5%and 1.9%,respectively.This proves that the proposed algorithm has a higher accuracy and robustness in occluded scenes.
万方数据
维普
