6D Pose Estimation Method of Metal Parts for Robotic Grasping Based on Semantic-Level Line Matching
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6498-7_1
Reference17 articles.
1. Du, G., Wang, K., Lian, S., Zhao, K.: Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: A review. Artif. Intell. Rev. 54(3), 1677–1734 (2021). https://doi.org/10.1007/s10462-020-09888-5
2. Zhang, H., Cao, Q.: Detect in RGB, optimize in edge: Accurate 6D pose estimation for texture-less industrial parts. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 3486–3492. IEEE (2019). https://doi.org/10.1109/ICRA.2019.8794330
3. Zhang, X., Jiang, Z., Zhang, H.: Real-time 6d pose estimation from a single RGB image. Image Vis. Comput. 89, 1–11 (2019). https://doi.org/10.1016/j.imavis.2019.06.013
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5. Tombari, F., Franchi, A., Di Stefano, L.: BOLD features to detect texture-less objects. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1265–1272. IEEE, Sydney, Australia (2013). https://doi.org/10.1109/ICCV.2013.160
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