Fetching the paper…
Reading the bibliography…
Hand-eye calibration aims to estimate the transformation between a camera and a robot.
J. Denavit and R. S. Hartenberg, “A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices,” Journal of Applied Mechanics , vol. 22, no. 2, pp. 215–221, June 1955
1955
Earlier work this paper cites.
B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence - Volume 2 , ser. IJCAI’81. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1981, p. 674–679
1981
Earlier work this paper cites.
K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-squares fitting of two 3-d point sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. PAMI-9, no. 5, pp. 698–700, 1987
1987
Earlier work this paper cites.
R. Tsai and R. Lenz, “A new technique for fully autonomous and efficient 3d robotics hand/eye calibration,” IEEE Transactions on Robotics and Automation , vol. 5, no. 3, pp. 345–358, 1989
1989
Earlier work this paper cites.
M. Black and P. Anandan, “A framework for the robust estimation of optical flow,” in 1993 (4th) International Conference on Computer Vision , 1993, pp. 231–236
1993
Earlier work this paper cites.
F. Park and B. Martin, “Robot sensor calibration: solving AX=XB on the Euclidean group,” IEEE Transactions on Robotics and Automation , vol. 10, no. 5, pp. 717–721, Oct. 1994, conference Name: IEEE Transactions on Robotics and Automation
1994
Earlier work this paper cites.
R. Horaud and F. Dornaika, “Hand-Eye Calibration,” The International Journal of Robotics Research , vol. 14, no. 3, pp. 195–210, June 1995, publisher: SAGE Publications Ltd STM
1995
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 of Software Tools , 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, Mar. 2001, publisher: SAGE Publications Ltd STM
2001
Earlier work this paper cites.
M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking,” IEEE Transactions on Signal Processing , vol. 50, no. 2, pp. 174–188, 2002
2002
Earlier work this paper cites.
M. Fiala, “Artag, a fiducial marker system using digital techniques,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , vol. 2, 2005, pp. 590–596 vol. 2
2005
Earlier work this paper cites.
M. Jaward, L. Mihaylova, N. Canagarajah, and D. Bull, “Multiple object tracking using particle filters,” in 2006 IEEE Aerospace Conference , 2006, p. 8
2006
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, Feb. 2009
2009
Cited alongside, same era.
M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” in Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA) Workshop on Open Source Robotics , Kobe, Japan, May 2009
2009
Cited alongside, same era.
E. Olson, “Apriltag: A robust and flexible visual fiducial system.” in ICRA . IEEE, 2011, pp. 3400–3407
2011
Cited alongside, same era.
J. Heller, M. Havlena, A. Sugimoto, and T. Pajdla, “Structure-from-motion based hand-eye calibration using L$_8$ minimization,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition , ser. CVPR ’11. USA: IEEE Computer Society, June 2011, pp. 3497–3503
2011
Cited alongside, same era.
M. Caron, H. Touvron, I. Misra, H. Jegou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging Properties in Self-Supervised Vision Transformers,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Montreal, QC, Canada: IEEE, Oct. 2021, pp. 9630–9640
2021
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 , Aug. 2022
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and R. Girshick, “Masked Autoencoders Are Scalable Vision Learners,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . New Orleans, LA, USA: IEEE, June 2022, pp. 15 979–15 988
2022
Later among the works it cites.
J. Lu, F. Richter, and M. C. Yip, “Markerless Camera-to-Robot Pose Estimation via Self-Supervised Sim-to-Real Transfer,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Vancouver, BC, Canada: IEEE, June 2023, pp. 21 296–21 306
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Ilonen and V. Kyrki, “Robust robot-camera calibration,” in 2011 15th International Conference on Advanced Robotics (ICAR) , 2011, pp. 67–74
2011
Cited alongside, same era.
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
Cited alongside, same era.
M. Esposito, Y. Li, and R. O’Flaherty, “easy_handeye: automated, hardware-independent hand-eye calibration for ros1,” https://github.com/IFL-CAMP/easy_handeye , 2015
2015
Cited alongside, same era.
M. Antonello, A. Gobbi, S. Michieletto, S. Ghidoni, and E. Menegatti, “A fully automatic hand-eye calibration system,” in 2017 European Conference on Mobile Robots (ECMR) . Paris: IEEE, Sept. 2017, pp. 1–6
2017
Cited alongside, same era.
X. Zhi and S. Schwertfeger, “Simultaneous hand-eye calibration and reconstruction,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sept. 2017, pp. 1470–1477, iSSN: 2153-0866
2017
Cited alongside, same era.
L. Yang, Q. Cao, M. Lin, H. Zhang, and Z. Ma, “Robotic hand-eye calibration with depth camera: A sphere model approach,” in 2018 4th International Conference on Control, Automation and Robotics (ICCAR) . IEEE, 2018, pp. 104–110
2018
Cited alongside, same era.
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, May 2020
2020
Cited alongside, same era.
G. Terzakis and M. Lourakis, “A consistently fast and globally optimal solution to the perspective-n-point problem,” in European Conference on Computer Vision . Springer International Publishing, 2020, pp. 478–494
2020
Cited alongside, same era.
2023
Later among the works it cites.
L. Chen, Y. Qin, X. Zhou, and H. Su, “Easyhec: Accurate and automatic hand-eye calibration via differentiable rendering and space exploration,” IEEE Robotics and Automation Letters , vol. 8, no. 11, p. 7234–7241, Nov. 2023
2023
Later among the works it cites.
C. Doersch, Y. Yang, M. Vecerik, D. Gokay, A. Gupta, Y. Aytar, J. Carreira, and A. Zisserman, “Tapir: Tracking any point with per-frame initialization and temporal refinement,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE, Oct. 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Fu, W. Xu, R. Ye, H. Xue, Z. Yu, T. Tang, Y. Li, W. Du, J. Zhang, and C. Lu, “Demonstrating RFUniverse: A Multiphysics Simulation Platform for Embodied AI,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023
2023
Later among the works it cites.
A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y. Chen, and et. al., “DROID: A large-scale in-the-wild robot manipulation dataset,” in RSS 2024 Workshop: Data Generation for Robotics , 2024
2024
Closest in time.
B. Wen, W. Yang, J. Kautz, and S. Birchfield, “Foundationpose: Unified 6d pose estimation and tracking of novel objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 17 868–17 879
2024
Closest in time.
N. Tumanyan, A. Singer, S. Bagon, and T. Dekel, “Dino-tracker: Taming dino for self-supervised point tracking in a single video,” March 2024
2024
Closest in time.
Y. Xiao, Q. Wang, S. Zhang, N. Xue, S. Peng, Y. Shen, and X. Zhou, “Spatialtracker: Tracking any 2d pixels in 3d space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Closest in time.
X. He, H. Yu, S. Peng, D. Tan, Z. Shen, H. Bao, and X. Zhou, “Matchanything: Universal cross-modality image matching with large-scale pre-training,” 2025
2025
Closest in time.