Easy-to-Use and Accurate Calibration of RGB-D Cameras from Spheres

  • Aaron Staranowicz
  • Garrett R. Brown
  • Fabio Morbidi
  • Gian Luca Mariottini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

RGB-Depth (or RGB-D) cameras are increasingly being adopted for real-world applications, especially in areas of healthcare and at-home monitoring. As for any other sensor, and since the manufacturer’s parameters (e.g., focal length) might change between models, calibration is necessary to increase the camera’s sensing accuracy. In this paper, we present a novel RGB-D camera-calibration algorithm that is easy-to-use even for non-expert users at their home; our method can be used for any arrangement of RGB and depth sensors, and only requires that a spherical object (e.g., a basketball) is moved in front of the camera for a few seconds. A robust image-processing pipeline automatically detects the moving sphere and rejects noise and outliers in the image data. A novel closed-form solution is presented to accurately compute an initial set of calibration parameters which are then utilized in a nonlinear minimization stage over all the camera parameters including lens distortion. Extensive simulation and experimental results show the accuracy and robustness to outliers of our algorithm with respect to existing checkerboard-based methods. Furthermore, an RGB-D Calibration Toolbox for MATLAB is made freely available for the entire research community.

Keywords

RGB-Depth Cameras Camera Calibration Kinect Computer Vision 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Microsoft® Kinect Camera (2011) (Web): http://www.xbox.com/en-US/KINECT.
  2. 2.
    Konolige, K.: Projected texture stereo. In: Proc. IEEE Int. Conf. Robot. Automat. In: Proc. IEEE Int. Conf. Robot. Automat, Anchorage, Alaska, U.S, pp. 148–155 (May 2010)Google Scholar
  3. 3.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Int. Conf. Vis. Pattern Rec. (2011)Google Scholar
  4. 4.
    Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: 10th IEEE Intl. Sym. on Mixed and Aug. Real., pp. 127–136 (2011)Google Scholar
  5. 5.
    Morbidi, F., Ray, C., Mariottini, G.L.: Cooperative active target tracking for heterogeneous robots with application to gait monitoring. In: Proc. IEEE Int. Conf. Intell. Rob. Sys., pp. 3608–3613 (September 2011)Google Scholar
  6. 6.
    Gabel, M., Gilad-Bachrach, R., Renshaw, E., Schuster, A.: Full body gait analysis with kinect. In: Intl. Conf. of the IEEE Eng. in Med. and Bio. Soc. (August 2012)Google Scholar
  7. 7.
    Cai, Q., Gallup, D., Zhang, C., Zhang, Z.: 3D Deformable Face Tracking with a Commodity Depth Camera. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 229–242. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Frank, B., Schmedding, R., Stachniss, C., Teschner, M., Burgard, W.: Learning Deformable Object Models for Mobile Robot Navigation using Depth Cameras and a Manipulation Robot. In: Proc. Robotics: Science and Systems VI’s Workshop on RGB-D: Advanced Reasoning with Depth Cameras (June 2010)Google Scholar
  9. 9.
    Lai, K., Bo, L., Ren, X., Fox, D.: A Large-Scale Hierarchical Multi-View RGB-D Object Dataset. In: Proc. IEEE Int. Conf. Robot. Automat, pp. 1817–1824 (May 2011)Google Scholar
  10. 10.
    Ramey, A., Gonzalez-Pacheco, V., Salichs, M.A.: Integration of a low-cost RGB-D sensor in a social robot for gesture recognition. In: Proc. 6th Int. Conf. Human-robot Inter., Lausanne, Switzerland, pp. 229–230 (March 2011)Google Scholar
  11. 11.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge Univ. Press (2003)Google Scholar
  12. 12.
  13. 13.
    Mihelich, P., Konolige, K.: Technical description of kinect calibration (Web): http://www.ros.org/wiki/kinect_calibration/technical
  14. 14.
    Jung, J., Jeong, Y., Park, J., Ha, H., Kim, D.J., Kweon, I.: A Novel 2.5D Pattern for Extrinsic Calibration of ToF and Camera Fusion System. In: Proc. IEEE/RSJ Intl. Conf. on Intel. Rob. Syst., pp. 3290–3296 (September 2011)Google Scholar
  15. 15.
    Smisek, J., Jancosek, M., Pajdla, T.: 3D with kinect. In: IEEE Intl. Conf. on Computer Vision Workshops, pp. 1154–1160 (November 2011)Google Scholar
  16. 16.
    Zhang, C., Zhang, Z.: Calibration between Depth and Color Sensors for Commodity Depth Cameras. In: Intl. Workshop on Hot Topics in 3D, in Conjunction with ICME (July 2011)Google Scholar
  17. 17.
    Herrera, C., Kannala, J., Heikkilä, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. Pattern Anal. 34(10), 2058–2064 (2012)CrossRefGoogle Scholar
  18. 18.
    Khoshelham, K., Elberink, S.O.: Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications. Sensors 12(2), 1437–1454 (2012)CrossRefGoogle Scholar
  19. 19.
    Mihelich, P.: ROS openni-launch package for Intrin. and Extrin. Kinect Calib (2013), (Web): http://www.ros.org/wiki/openni_launch/Tutorials/
  20. 20.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Cipolla, R., Giblin, P.: Visual motion of curves and surfaces. Cambridge University Press, New York (2000)MATHGoogle Scholar
  22. 22.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Trans. Pattern Anal. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  23. 23.
    Ballard, D.H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recog. 13(2), 111–122 (1981)CrossRefMATHGoogle Scholar
  24. 24.
    Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. 21(5), 476–480 (1999)CrossRefGoogle Scholar
  25. 25.
    Halıř, R., Flusser, J.: Numerically stable direct least squares fitting of ellipses. In: Proc. 6th Int. Conf. Cen. Eur. on Com. Graph. Vis., vol. 21(5), pp. 125–132 (Febraury 1998)Google Scholar
  26. 26.
    Wong, K., Zhang, G., Chen, Z.: A Stratified Approach for Camera Calibration Using Spheres. IEEE Trans. on Img. Proc. 20(2), 305–316 (2011)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Staranowicz, A., Mariottini, G.L.: A comparative study of calibration methods for kinect-style cameras. In: Proc. 5th Intl. Conf on PETRAE, pp. 49:1–49:4 (2012)Google Scholar
  28. 28.
    Camera Calibration Toolbox for Matlab (2010) (Web): http://www.vision.caltech.edu/bouguetj/calib_doc/

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Aaron Staranowicz
    • 1
  • Garrett R. Brown
    • 1
  • Fabio Morbidi
    • 2
  • Gian Luca Mariottini
    • 1
  1. 1.CSE Dept.Univ. of Texas at ArlingtonArlingtonUSA
  2. 2.Inria Grenoble Rhône-AlpesMontbonnotFrance

Personalised recommendations