An Initialization Tool for Installing Visual Markers in Wearable Augmented Reality

  • Yusuke Nakazato
  • Masayuki Kanbara
  • Naokazu Yokoya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4282)


It is necessary to precisely measure pose (position and orientation) of a user in order to realize an augmented reality (AR) system with a wearable computer. One of major methods for measuring user’s pose in AR is visual marker-based approach which calculates them by recognizing markers pasted up on the ceilings or walls. The method needs 3D pose information of visual markers in advance. However, much cost is necessary to calibrate visual markers pasted up on the ceiling in a wide environment. In this paper, an initialization tool for installing visual markers in wearable AR is proposed. The administrator is assisted in installing visual markers in a wide environment by the proposed tool. The tool calibrates alignment of visual markers which exist in the real environment with high accuracy by recognizing them in the images captured by a high-resolution still camera. Additionally, the tool assists the administrator in repairing the incorrect pattern of marker using a wearable AR system.


Augmented Reality Visual Marker World Coordinate System Augment Reality System Reprojection Error 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Höller, T., Feiner, S., Terauchi, T., Rashid, G., Hallaway, D.: Exploring mars: Developing indoor and outdoor user interfaces to a mobile augmented reality system. Computers and Graphics 23(6), 779–785 (1999)CrossRefGoogle Scholar
  2. 2.
    Kourogi, M., Kurata, T.: Personal positioning based on walking locomotion a alysis with self-contained sensors and wearable camera. In: Proc. 2nd IEEE/ACM Int. Symp. on Mixed and Augmented Reality (ISMAR 2003), pp. 103–112 (2003)Google Scholar
  3. 3.
    Tenmoku, R., Kanbara, M., Yokoya, N.: A wearable augmented reality system using positioning infrastructures and a pedometer. In: Proc. 7th IEEE Int. Symp. on Wearable Computers (ISWC 2003), pp. 110–117 (2003)Google Scholar
  4. 4.
    Vacchetti, L., Lepetit, V., Fua, P.: Combining edge and texture information for real-time accurate 3D camera tracking. In: Proc. 3rd IEEE/ACM Int. Symp. on Mixed and Augmented Reality (ISMAR 2004), pp. 48–57 (2004)Google Scholar
  5. 5.
    Oe, M., Sato, T., Yokoya, N.: Estimating camera position and posture by using feature landmark database. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 171–181. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Thomas, B., Close, B., Donoghue, J., Squires, J., De Bondi, P., Piekarski, W.: First person indoor/outdoor augmented reality application: Arquake. Personal and Ubiquitous Computing 6(1), 75–86 (2002)CrossRefGoogle Scholar
  7. 7.
    Kalkusch, M., Lidy, T., Lnapp, M., Reitmayr, G., Kaufmann, H., Schmalstieg, D.: Structured visual markers for indoor pathfinding. In: Proc. 1st IEEE Int. Augmented Reality Toolkit Workshop, ART 2002 (2002)Google Scholar
  8. 8.
    Naimark, L., Foxlin, E.: Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker. In: Proc. 1st IEEE/ACM Int.Symp. on Mixed and Augmented Reality (ISMAR 2002), pp. 27–36 (2002)Google Scholar
  9. 9.
    Nakazato, Y., Kanbara, M., Yokoya, N.: A localization system using invisible retroreflective markers. In: Proc. IAPR Conf. on Machine Vision Applications (MVA 2005), pp. 140–143 (2005)Google Scholar
  10. 10.
    Baratoff, G., Neubeck, A., Regenbrecht, H.: Interactive multi-marker calibr tion for augmented reality applications. In: Proc. 1st IEEE/ACM Int. Symp. on Mixed and Augmented Reality (ISMAR 2002), pp. 107–116 (2002)Google Scholar
  11. 11.
    Maeda, M., Habara, T., Machida, T., Ogawa, T., Kiyokawa, K., Takemura, H.: Indoor localization methods for wearable mixed reality. In: Proc. 2nd CREST Workshop on Advanced Computing and Communicating Techniques for Wearable Information Playing, pp. 63–65 (2003)Google Scholar
  12. 12.
    Zauner, J., Haller, A.M.: Authoring of mixed reality applications including multimarker calibration for mobile devices. In: Proc. 10th Eurographics Symp. Virtual Environments (EGVE 2004), pp. 87–90 (2004)Google Scholar
  13. 13.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  14. 14.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man, and Cybernetics SMC 9(1), 63–66 (1979)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Sato, T., Kanbara, M., Yokoya, N.: 3-D modeling of an outdoor scene from multiple image sequences by estimating camera motion parameters. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 717–724. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Tsai, R.Y.: An efficient and accurate camera calibration technique for 3D machine vision. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 364–374 (1986)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yusuke Nakazato
    • 1
  • Masayuki Kanbara
    • 1
  • Naokazu Yokoya
    • 1
  1. 1.Nara Institute of Science and TechnologyNaraJapan

Personalised recommendations