Mobile Augmented Reality: Composing the feature map retrieval process for robust and ready-to-use monocular tracking

  • Authors:

  • Folker Wientapper;Harald Wuest;Arjan Kuijper

  • Affiliations:

  • Department of Virtual and Augmented Reality, Fraunhofer Institute for Graphics Research (IGD), Fraunhoferstr. 5, D-64283 Darmstadt, Germany;Department of Virtual and Augmented Reality, Fraunhofer Institute for Graphics Research (IGD), Fraunhoferstr. 5, D-64283 Darmstadt, Germany;Department of Virtual and Augmented Reality, Fraunhofer Institute for Graphics Research (IGD), Fraunhoferstr. 5, D-64283 Darmstadt, Germany

  • Venue:
  • Computers and Graphics
  • Year:
  • 2011

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Abstract

This paper focuses on the preparative process of natural feature map retrieval for a mobile camera-based tracking system. We cover the most important aspects of a general purpose tracking system including the acquisition of the scene's geometry, tracking initialization and fast and accurate frame-by-frame tracking. To this end, several state-of-the-art techniques - each targeted at one particular subproblem - are fused together, whereby their interplay and complementary benefits form the core of the system and the thread of our discussion. The choice of the individual sub-algorithms in our system reflects the scarcity of computational resources on mobile devices. In order to allow a more accurate, more robust and faster tracking during run-time, we therefore transfer the computational load into the preparative customization step wherever possible. From the viewpoint of the user, the preparative stage is kept very simple. It only involves recording the scene from various viewpoints and defining a transformation into a target coordinate frame via manual definition of only a few 3D to 3D point correspondences. Technically, the image sequence is used to (1) capture the scene's geometry by a SLAM-Method and subsequent refinement via constrained Bundle Adjustment, (2) to train a Randomized-Trees classifier for wide-baseline tracking initialization, and (3) to analyze the view-point dependent visibility of each feature. During run-time, robustness and performance of the frame-to-frame tracking are further increased by fusing inertial measurements within a combined pose estimation.