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Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping.
D. Gabay, “Minimizing a differentiable function over a differential manifold,” Journal of Optimization Theory and Applications , vol. 37, pp. 177–219, 1982
1982
Earlier work this paper cites.
R. Y. Tsai, R. K. Lenz et al. , “A new technique for fully autonomous and efficient 3 d robotics hand/eye calibration,” IEEE Transactions on robotics and automation , vol. 5, no. 3, pp. 345–358, 1989
1989
Earlier work this paper cites.
P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures , vol. 1611. Spie, 1992, pp. 586–606
1992
Earlier work this paper cites.
F. C. Park and B. J. Martin, “Robot sensor calibration: solving ax= xb on the euclidean group,” IEEE Transactions on Robotics and Automation , vol. 10, no. 5, pp. 717–721, 1994
1994
Earlier work this paper cites.
K. Daniilidis, “Hand-eye calibration using dual quaternions,” The International Journal of Robotics Research , vol. 18, no. 3, pp. 286–298, 1999
1999
Earlier work this paper cites.
G. Bradski, “The opencv library.” Dr. Dobb’s Journal: Software Tools for the Professional Programmer , vol. 25, no. 11, pp. 120–123, 2000
2000
Earlier work this paper cites.
N. Andreff, R. Horaud, and B. Espiau, “Robot hand-eye calibration using structure-from-motion,” The International Journal of Robotics Research , vol. 20, no. 3, pp. 228–248, 2001
2001
Earlier work this paper cites.
V. Lepetit, F. Moreno-Noguer, and P. Fua, “Epnp: An accurate o (n) solution to the pnp problem,” International journal of computer vision , vol. 81, no. 2, pp. 155–166, 2009
2009
Earlier work this paper cites.
J. Ilonen and V. Kyrki, “Robust robot-camera calibration,” in 2011 15th International Conference on Advanced Robotics (ICAR) . IEEE, 2011, pp. 67–74
2011
Earlier work this paper cites.
J. Heller, M. Havlena, A. Sugimoto, and T. Pajdla, “Structure-from-motion based hand-eye calibration using l ∞ minimization,” in CVPR 2011 . IEEE, 2011, pp. 3497–3503
2011
Earlier work this paper cites.
S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognition , vol. 47, no. 6, pp. 2280–2292, 2014
2014
Earlier work this paper cites.
X. Zhi and S. Schwertfeger, “Simultaneous hand-eye calibration and reconstruction,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 1470–1477
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
A. Kundu, Y. Li, and J. M. Rehg, “3d-rcnn: Instance-level 3d object reconstruction via render-and-compare,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3559–3568
2018
Cited alongside, same era.
S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4561–4570
2019
Cited alongside, same era.
E. Valassakis, K. Dreczkowski, and E. Johns, “Learning eye-in-hand camera calibration from a single image,” in Conference on Robot Learning . PMLR, 2022, pp. 1336–1346
2022
Later among the works it cites.
J. Lu, F. Richter, and M. C. Yip, “Pose estimation for robot manipulators via keypoint optimization and sim-to-real transfer,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4622–4629, 2022
2022
Later among the works it cites.
B. C. Sefercik and B. Akgun, “Learning markerless robot-depth camera calibration and end-effector pose estimation,” in Conference on Robot Learning . PMLR, 2023, pp. 1586–1595
2023
Closest in time.
J. Lu, F. Liu, C. Girerd, and M. C. Yip, “Image-based pose estimation and shape reconstruction for robot manipulators and soft, continuum robots via differentiable rendering,” IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
Closest in time.
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T. E. Lee, J. Tremblay, T. To, J. Cheng, T. Mosier, O. Kroemer, D. Fox, and S. Birchfield, “Camera-to-robot pose estimation from a single image,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 9426–9432
2020
Cited alongside, same era.
A. Kirillov, Y. Wu, K. He, and R. Girshick, “Pointrend: Image segmentation as rendering,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9799–9808
2020
Cited alongside, same era.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su, “SAPIEN: A simulated part-based interactive environment,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Cited alongside, same era.
S. Laine, J. Hellsten, T. Karras, Y. Seol, J. Lehtinen, and T. Aila, “Modular primitives for high-performance differentiable rendering,” ACM Transactions on Graphics (TOG) , vol. 39, no. 6, pp. 1–14, 2020
2020
Cited alongside, same era.
Y. Labbé, J. Carpentier, M. Aubry, and J. Sivic, “Single-view robot pose and joint angle estimation via render & compare,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1654–1663
2021
Cited alongside, same era.
O. Bahadir, J. P. Siebert, and G. Aragon-Camarasa, “A deep learning-based hand-eye calibration approach using a single reference point on a robot manipulator,” in 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) . IEEE, 2022, pp. 1109–1114
2022
Cited alongside, same era.
X. Zhang, Y. Xi, Z. Huang, L. Zheng, H. Huang, Y. Xiong, and K. Xu, “Active hand-eye calibration via online accuracy-driven next-best-view selection,” The Visual Computer , vol. 39, no. 1, pp. 381–391, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. Jia, F. Liu, V. Thumuluri, L. Chen, Z. Huang, and H. Su, “Chain-of-thought predictive control with behavior cloning,” in Workshop on Reincarnating Reinforcement Learning at ICLR 2023
2023
Closest in time.
L. Chen, Y. Song, H. Bao, and X. Zhou, “Perceiving unseen 3d objects by poking the objects,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 4834–4841
2023
Closest in time.
K. Yang, X. Zhang, Z. Huang, X. Chen, Z. Xu, and H. Su, “Movingparts: Motion-based 3d part discovery in dynamic radiance field,” Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2023
2023
Closest in time.