Landmark Real-Time Recognition and Positioning for Pedestrian Navigation

  • Antonio Adán
  • Alberto Martín
  • Enrique Valero
  • Pilar Merchán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


The aim of this paper is to propose a new monocular-vision strategy for real-time positioning under augmented reality conditions. This is an important aspect to be solved in augmented reality (AR) based navigation in non-controlled environments. In this case, the position and orientation of the moving observer, who usually wears a head mounted display and a camera, must be calculated as accurately as possible in real time. The method is based on analyzing the properties of the projected image of a single pattern consisting of eight small dots which belong to a circle and one dot more at the center of it. Due to the simplicity of the pattern and the low computational cost in the image processing phase, the system is capable of working under on-line requirements. This paper presents a comparison of our strategy with other pose solutions which have been applied in AR or robotic environments.


augmented reality camera pose landmark occlusion real-time 


  1. 1.
    Cobzas, D., Jagersand, M., Sturm, P.: 3D SSD tracking with estimated 3D planes. Journal of Image and Vision Computing 27, 69–79 (2009)CrossRefGoogle Scholar
  2. 2.
    Duan, F., Wu, F., Hu, Z.: Pose determination and plane measurement using a trapezium. Pattern Recognition Letters 29(3), 223–231 (2008)CrossRefGoogle Scholar
  3. 3.
    Feng, W., Liu, Y., Cao, Z.: Omnidirectional Vision Tracking and Positioning for Vehicles. In: ICNC 2008. Fourth International Conference on Natural Computation, vol. 6, pp. 183–187 (2008)Google Scholar
  4. 4.
    Fiala, M.: Linear Markers for Robots Navigation with Panoramic Vision. In: First Canadian Conf. Computer and Robot Vision, 2004. Proceedings, pp. 145–154 (2004)Google Scholar
  5. 5.
    Jang, G., et al.: Metric Localization Using a Single Artificial Landmark for Indoor Mobile Robots. In: International Conference on Intelligent Robots and Systems, 2005 (IROS 2005), pp. 2857–2862 (2005)Google Scholar
  6. 6.
    Josephson, K., et al.: Image-Based Localization Using Hybrid Feature Correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  7. 7.
    Neumann, U., et al.: Augmented Reality Tracking in Natural Environments. In: International Symposium on Mixed Reality, 1999. ISMR 1999, pp. 101–130 (1999)Google Scholar
  8. 8.
    Xu, K., Chia, K.W., Cheok, A.D.: Real-time camera tracking for marker-less and unprepared augmented reality environments. Image and Vision Computing 26(5), 673–689 (2008)CrossRefGoogle Scholar
  9. 9.
    Se, S., Lowe, D., Little, J.: Mobile Robot Localization and Mapping with Uncertainly using Scale-Invariant Visual Landmarks. The International Journal of Robotics Research 21(8), 735–757 (2002)CrossRefGoogle Scholar
  10. 10.
    Vachetti, L., Lepetit, V., Fua, P.: Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking. In: Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 2004. ISMAR 2004, pp. 48–56 (2004)Google Scholar
  11. 11.
    Vachetti, L., Lepetit, V., Fua, P.: Stable Real-Time 3D Tracking using Online and Offline Information. IEEE Transactions on PAMI 26(10), 1385–1391 (2004)Google Scholar
  12. 12.
    Briggs, A.J., et al.: Mobile Robot Navigation Using Self-Similar Landmarks. In: IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA 2000, vol. 2, pp. 1428–1434 (2000)Google Scholar
  13. 13.
    Kato, H., et al.: Virtual Object Manipulation on a Table-Top AR Environment. In: Proceedings of IEEE and ACM International Symposium on Augmented Reality, 2000 (ISAR 2000), pp. 111–119 (2000)Google Scholar
  14. 14.
    Koller, D., et al.: Real-time Vision-Based Camera Tracking for Augmented Reality Applications. In: ACM Symp. on Virtual Reality Software and Technology, pp. 87–94 (1997)Google Scholar
  15. 15.
    Hager, G.D., Belhumeur, P.N.: Efficient Region Tracking with Parametric Models of Geometry and Illumination. IEEE PAMI 20(10), 1025–1039 (1998)Google Scholar
  16. 16.
    Adan, A., Martín, A., Chacón, R., Dominguez, V.: Monocular Model-Based 3D Location for Autonomous Robots. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 594–604. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antonio Adán
    • 1
  • Alberto Martín
    • 1
  • Enrique Valero
    • 1
  • Pilar Merchán
    • 2
  1. 1.Escuela Superior de InformáticaUniversidad de Castilla-La ManchaCiudad RealSpain
  2. 2.Escuela de Ingenierías IndustrialesUniversidad de ExtremaduraBadajozSpain

Personalised recommendations