Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM

ABSTRACT

Most work in visual augmented reality (AR) employs predefined markers or models that simplify the algorithms needed for sensor positioning and augmentation but at the cost of imposing restrictions on the areas of operation and on interactivity. This paper presents a simple game in which an AR agent has to navigate using real planar surfaces on objects that are dynamically added to an unprepared environment. An extended Kalman filter (EKF) simultaneous localisation and mapping (SLAM) framework with automatic plane discovery is used to enable the player to interactively build a structured map of the game environment using a single, agile camera. By using SLAM, we are able to achieve real-time interactivity and maintain rigorous estimates of the system's uncertainty, which enables the effects of high quality estimates to be propagated to other features (points and planes) even if they are outside the camera's current field of view.

References

1. OGRE graphics engine, www.ogre3d.org.
2. T. Bailey and H. Durrant-Whyte. Simultaneous localisation and mapping (SLAM): Part I - The essential algorithms. IEEE Robotics and Automation Magazine, 13(2), June 2006.
3. T. Bailey and H. Durrant-Whyte. Simultaneous localisation and mapping (SLAM): Part II - State of the art. IEEE Robotics and Automation Magazine, 13(3), Sept. 2006.
4. Y. Bar-Shalom, T. Kirubarajan, and X.-R. Li. Estimation with Applications to Tracking and Navigation. 2002.
5. I. Barakonyi, M. Weilguny, T. Psik, and D. Schmalstieg. Monkey-Bridge: autonomous agents in augmented reality games. In ACM SIGCHI Int. Conf. on Advances in Computer Entertainment Technology , 2005.
6. R. Castle, D. Gawley, G. Klein, and D. Murray. Video-rate recognition and localization for wearable cameras. In British Machine Vision Conf., 2007.
7. D. Chekhlov, M. Pupilli, W. Mayol-Cuevas, and A. Calway. Real-time and robust monocular slam using predictive multi-resolution descriptors. In Int. Symp. on Visual Computing, 2006.
8. J. Civera, A. Davison, and J. Montiel. Inverse depth to depth conversion for monocular SLAM. In Int. Conf. Robotics and Automation, 2007.
9. A. Davison, W. Mayol, and D. Murray. Real-time localisation and mapping with wearable active vision. In Int. Symp. on Mixed and Augmented Reality, 2003.
10. A. J. Davison, I. Reid, N. Molton, and O. Stasse. MonoSLAM: Real-time single camera SLAM. IEEE Trans. PAMI, 29(6), 2007.
11. M. Fischler and R. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM, 24(6):381-395, 1981.
12. A. Gee, D. Chekhlov, W. Mayol, and A. Calway. Discovering planes and collapsing the state space in visual slam. In British Machine Vision Conf., 2007.
13. A. Henrysson, M. Billinghurst, and M. Ollila. Face to face collaborative AR on mobile phones. In Int. Symp. on Mixed and Augmented Reality, 2005.
14. J. Montiel, J. Civera, and A. Davison. Unified inverse depth parametrization for monocular SLAM. In Robotics: Science and Systems Conf., 2006.
15. E. Rosten and T. Drummond. Machine learning for high-speed corner detection. In European Conf on Computer Vision, 2006.
16. G. Simon. Automatic online walls detection for immediate use in AR tasks. In Int. Symp. on Mixed and Augmented Reality, 2006.

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision tasks

  • Computer graphics

    • Graphics systems and interfaces

• Human-centered computing

  • Human computer interaction (HCI)

    • Interaction paradigms