An Initialization Tool for Installing Visual Markers in Wearable Augmented Reality

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.

[1]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[2]  Naokazu Yokoya,et al.  A wearable augmented reality system using positioning infrastructures and a pedometer , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[3]  Naokazu Yokoya,et al.  Localization of wearable users using invisible retro-reflective markers and an IR camera , 2005, IS&T/SPIE Electronic Imaging.

[4]  Michael Haller,et al.  Authoring of Mixed Reality Applications including Multi-Marker Calibration for Mobile Devices , 2004, EGVE.

[5]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[6]  Naokazu Yokoya,et al.  A Localization System Using Invisible Retro-reflective Markers , 2005, MVA.

[7]  Holger Regenbrecht,et al.  Interactive multi-marker calibration for augmented reality applications , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[8]  Naokazu Yokoya,et al.  3-D Modeling of an Outdoor Scene from Multiple Image Sequences by Estimating Camera Motion Parameters , 2003, SCIA.

[9]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  R. Y. Tsai,et al.  An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision , 1986, CVPR 1986.

[11]  Bruce H. Thomas,et al.  First Person Indoor/Outdoor Augmented Reality Application: ARQuake , 2002, Personal and Ubiquitous Computing.

[12]  Dieter Schmalstieg,et al.  Structured visual markers for indoor pathfinding , 2002, The First IEEE International Workshop Agumented Reality Toolkit,.

[13]  Vincent Lepetit,et al.  Combining edge and texture information for real-time accurate 3D camera tracking , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[14]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[15]  Steven K. Feiner,et al.  Exploring MARS: developing indoor and outdoor user interfaces to a mobile augmented reality system , 1999, Comput. Graph..

[16]  Eric Foxlin,et al.  Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[17]  Naokazu Yokoya,et al.  Estimating Camera Position and Posture by Using Feature Landmark Database , 2005, SCIA.