Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1109/IROS.2015.7353799guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article

Unsupervised model-free camera calibration algorithm for robotic applications

Published: 28 September 2015 Publication History

Abstract

This paper presents an algorithm for camera calibration. The algorithm, inspired by work in the field of developmental robotic on the concept of space in naive agents, is particularly suitable for robotic applications: it is completely unsupervised, and it does not assume any model of the camera, making it applicable to many kinds of optical devices. Testing of the algorithm, in a simulated environment, shows very good results, outperforming the main unsupervised and model-free calibration algorithm in the literature.

References

[1]
J.- Y. Bouguet, “Camera calibration toolbox for matlab,” 2004.
[2]
Z. Zhang, “A flexible new technique for camera calibration,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 11, pp. 1330–1334, 2000.
[3]
O. D. Faugeras, Q.- T. Luong, and S. J. Maybank, “Camera self-calibration: Theory and experiments,” in Computer VisionECCV'92. Springer, 1992, pp. 321–334.
[4]
J. Kannala and S. S. Brandt, “A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, no. 8, pp. 1335–1340, 2006.
[5]
J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Transactions on pattern analysis and machine intelligence, vol. 14, no. 10, pp. 965–980, 1992.
[6]
A. Censi and D. Scaramuzza, “Calibration by correlation using metric embedding from nonmetric similarities,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 10, pp. 2357–2370, 2013.
[7]
E. Grossmann, J. A. Gaspar, and F. Orabona, “Discrete camera calibration from pixel streams,” Computer Vision and Image Understanding, vol. 114, no. 2, pp. 198–209, 2010.
[8]
J. Modayil, “Discovering sensor space: Constructing spatial embed-dings that explain sensor correlations,” in Development and Learning (ICDL), 2010 IEEE 9th International Conference on. IEEE, 2010, pp. 120–125.
[9]
J. Stober, L. Fishgold, and B. Kuipers, “Sensor map discovery for developing robots.” in AAAI Fall Symposium: Manifold Learning and Its Applications, 2009.
[10]
D. Philipona, J. K. O'Regan, and J.-P. Nadal, “Is there something out there? inferring space from sensorimotor dependencies,” Neural computation, vol. 15, no. 9, pp. 2029–2049, 2003.
[11]
D. Philipona, J. K. O'Regan, J.-P. Nadal, and O. Coenen, “Perception of the structure of the physical world using unknown multimodal sensors and effectors,” in Advances in neural information processing systems, 2003, p. None.
[12]
A. V. Terekhov and J. K. O'Regan, “Space as an invention of biological organisms,” arXiv preprint arXiv:1308.2124, 2013.
[13]
A. V. Terekhov and J. K. O'Regan, “Learning abstract perceptual notions: The example of space,” in Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2014 Joint IEEE International Conferences on, Oct 2014, pp. 368–373.
[14]
T. F. Cox and M. A. Cox, Multidimensional scaling. CRC Press, 2010.
[15]
D. Pierce and B. J. Kuipers, “Map learning with uninterpreted sensors and effectors,” Artificial Intelligence, vol. 92, no. 1, pp. 169–227, 1997.
[16]
S. Agarwal, J. Wills, L. Cayton, G. Lanckriet, D. J. Kriegman, and S. Belongie, “Generalized non-metric multidimensional scaling,” in International Conference on Artificial Intelligence and Statistics, 2007, pp. 11–18.

Index Terms

  1. Unsupervised model-free camera calibration algorithm for robotic applications
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image Guide Proceedings
          2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
          Sep 2015
          6501 pages

          Publisher

          IEEE Press

          Publication History

          Published: 28 September 2015

          Qualifiers

          • Research-article

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 0
            Total Downloads
          • Downloads (Last 12 months)0
          • Downloads (Last 6 weeks)0
          Reflects downloads up to 03 Feb 2025

          Other Metrics

          Citations

          View Options

          View options

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media