Abstract
With the advent of Industry 4.0, the demand for estimating target pose keeps increasing. However, the accuracy of the existing pose estimation algorithms for texture-less targets is still poor. Traditional methods require approximately accurate initial pose or else they are easy to fall into a local optimum while the deep learning methods are limited in unconstrained environments where the unpredictable data can not be captured ahead for model training. Therefore, the paper proposes an innovative method which can cover the shortage of these two classes of methods. In our method, a multi-task model which can predict the pose of target and simultaneously obtain the edge map is designed. Then, the predicted pose and edge map are transferred to pose optimization module which is implemented based on edge matching. In addition, considering the lack of the pose datasets for texture-less objects, we design an effective pose dataset generation method based on 3D reconstruction. At last, the proposed system is tested on the public dataset and the rendered dataset. Experimental results demonstrate that the proposed algorithm is more accurate compared with the state-of-the-art methods.
Supported by the National Key R&D Program of China under Grant No. 2018YFB1305300, the National Natural Science Foundation of China under Grant No. 61873189, the Natural Science Foundation of Shanghai under Grant No. 18ZR1442500, Shanghai Municipal Science and Technology Major Project under Grant No. 2021SHZDZX0100 and Shanghai Municipal Commission of Science and Technology Project under Grant No. 19511132101.
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Xu, S., Gong, P., Dong, Y., Gi, L., Huang, C., Wang, S. (2021). Pose Estimation of Texture-Less Targets for Unconstrained Grasping. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13017. Springer, Cham. https://doi.org/10.1007/978-3-030-90439-5_37
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