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Accurate Intrinsic Calibration of Depth Camera with Cuboids

  • Bingwen Jin
  • Hao Lei
  • Weidong Geng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

Due to the low precision, the consumer-grade depth sensor is often calibrated jointly with a color camera, and the joint calibration sometimes presents undesired interactions. In this paper, we propose a novel method to carry out the high-accuracy intrinsic calibration of depth sensors merely by the depth camera, in which the traditional calibration rig, checker-board pattern, is replaced with a set of cuboids with known sizes, and the objective function for calibration is based on the length, width, and height of cuboids and its angle between the neighboring surfaces, which can be directly and robustly calculated from the depth-map. We experimentally evaluate the accuracy of the calibrated depth camera by measuring the angles and sizes of cubic object, and it is empirically shown that the resulting calibration accuracy is higher than that in the state-of-the-art calibration procedures, making the commodity depth sensors applicable to more interesting application scenarios such as 3D measurement and shape modeling etc.

Keywords

intrinsic calibration depth camera 3D measurement depth map cuboids 

References

  1. 1.
    Blake, J., et al.: Openkinect, http://openkinect.org
  2. 2.
  3. 3.
    Dal Mutto, C., Zanuttigh, P., Cortelazzo, G.M.: Time-of-Flight Cameras and Microsoft KinectTM. Springer (2012)Google Scholar
  4. 4.
    Draelos, M., Deshpande, N., Grant, E.: The kinect up close: Adaptations for short-range imaging. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 251–256. IEEE (2012)Google Scholar
  5. 5.
    Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the rgb-d slam system. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 1691–1696. IEEE (2012)Google Scholar
  6. 6.
    Geiger, A., Moosmann, F., Car, O., Schuster, B.: Automatic camera and range sensor calibration using a single shot. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 3936–3943. IEEE (2012)Google Scholar
  7. 7.
    Heikkila, J.: Geometric camera calibration using circular control points. PAMI 22(10), 1066–1077 (2000)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. PAMI, 1106–1112 (1997)Google Scholar
  9. 9.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In: Khatib, O., Kumar, V., Sukhatme, G. (eds.) Experimental Robotics. Springer Tracts in Advanced Robotics, vol. 79, pp. 477–491. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Herbst, E., Henry, P., Ren, X., Fox, D.: Toward object discovery and modeling via 3-d scene comparison. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2623–2629. IEEE (2011)Google Scholar
  11. 11.
    Herrera, C., Kannala, J., et al.: Joint depth and color camera calibration with distortion correction. PAMI 34(10), 2058–2064 (2012)CrossRefGoogle Scholar
  12. 12.
    Herrera, D., Kannala, J., Heikkilä, J.: Accurate and practical calibration of a depth and color camera pair. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 437–445. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Andrew, Davison, o.: Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In: UIST, pp. 559–568. ACM (2011)Google Scholar
  14. 14.
    Karan, B.: Accuracy improvements of consumer-grade 3d sensors for robotic applications. In: 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY), pp. 141–146. IEEE (2013)Google Scholar
  15. 15.
    Karan, B.: Calibration of depth measurement model for kinect-type 3d vision sensors (2013)Google Scholar
  16. 16.
    Khoshelham, K., Elberink, S.O.: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)CrossRefGoogle Scholar
  17. 17.
    Kim, J.-H., Choi, J.S., Koo, B.-K.: Calibration of multi-kinect and multi-camera setup for full 3d reconstruction. In: 2013 44th International Symposium on Robotics (ISR), pp. 1–5. IEEE (2013)Google Scholar
  18. 18.
    Konolige, K., Mihelich, P.: kinect calibration/technical.ros.org, http://www.ros.org/wiki/kinectcalibration/technical
  19. 19.
    Kummerle, R., Grisetti, G., Burgard, W.: Simultaneous calibration, localization, and mapping. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3716–3721. IEEE (2011)Google Scholar
  20. 20.
    Lourakis, M.: levmar: Levenberg-marquardt nonlinear least squares algorithms in C/C++, http://users.ics.forth.gr/%7elourakis/levmar/
  21. 21.
    Macknojia, R., Chávez-Aragón, A., Payeur, P., Laganière, R.: Calibration of a network of kinect sensors for robotic inspection over a large workspace. In: 2013 IEEE Workshop on Robot Vision (WORV), pp. 184–190. IEEE (2013)Google Scholar
  22. 22.
    Menna, F., Remondino, F., Battisti, R., Nocerino, E.: Geometric investigation of a gaming active device. In: SPIE Optical Metrology. pp. 80850G–80850G. International Society for Optics and Photonics (2011)Google Scholar
  23. 23.
    Paolo Cignoni, F.G.: The visualization and computer graphics library, http://vcg.isti.cnr.it/~cignoni/newvcglib/html/index.html
  24. 24.
    Raposo, C., Barreto, J.P., Nunes, U.: Fast and accurate calibration of a kinect sensor. In: 2013 International Conference on 3DTV-Conference, pp. 342–349. IEEE (2013)Google Scholar
  25. 25.
    Shibo, L., Qing, Z.: A new approach to calibrate range image and color image from kinect. In: 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 252–255. IEEE (2012)Google Scholar
  26. 26.
    Smisek, J., Jancosek, M., Pajdla, T.: 3d with kinect. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1154–1160. IEEE (2011)Google Scholar
  27. 27.
    Teichman, A., Miller, S., Thrun, S.: Unsupervised intrinsic calibration of depth sensors via slam. In: Robotics: Science and Systems, RSS (2013)Google Scholar
  28. 28.
    Yamazoe, H., Habe, H., Mitsugami, I., Yagi, Y.: Easy depth sensor calibration. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 465–468. IEEE (2012)Google Scholar
  29. 29.
    Zhang, C., Zhang, Z.: Calibration between depth and color sensors for commodity depth cameras. In: 2011 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2011)Google Scholar
  30. 30.
    Zollhöfer, M., Martinek, M., Greiner, G., Stamminger, M., Süßmuth, J.: Automatic reconstruction of personalized avatars from 3d face scans. Computer Animation and Virtual Worlds 22(2-3), 195–202 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bingwen Jin
    • 1
  • Hao Lei
    • 1
  • Weidong Geng
    • 1
  1. 1.State Key Lab. of CAD&CG, College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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