Innovative geometric pose reconstruction for marker-based single camera tracking

ABSTRACT

Mobile augmented reality applications are in need of tracking systems which can be wearable and do not cause a high processing load, while still offering reasonable performance, robustness and accuracy. The motivation to develop yet another tracking algorithm is two-fold. Most of the existing approaches use classical optimization techniques such as the Gauss-Newton method. However, since those algorithms were developed to address general optimization problems, they do not fully exploit the structure of the pose estimation problem with its geometric constraint targets. Also, mixed reality applications demand that pose estimation be not only accurate but also robust and computationally efficient. Hence there is a need for algorithms that are as accurate as classical algorithms, yet are also globally convergent and fast enough for real-time applications. In this paper we introduce a new iterative geometric method for pose estimation from four co-planar points and we present the current status of PTrack, an infrared marker-based single camera tracking system benefiting from this approach. Our novel pose estimation algorithm identifies possible labels composed of retro-reflective markers in a 2D post-processing using a divide-and-conquer strategy to segment the camera's image space and attempts an iterative geometric 3D reconstruction of position and orientation in camera space. Tracking results are made available to applications through OpenTracker [OpenTracker 2006] framework. To analyse tracking accuracy and precision, we built a generic test-bed and compared PTrack to ARToolKit [Kato and Billinghurst 1999; Kato et al. 2000], one of the most wide-spread low-cost tracking solutions. Results show that our tracking system achieves competitive accuracy levels better than ARToolKit and close to commercial systems, while being highly stable and affordable.

References

1. Ababsa, F., Mallem, M. 2004. Robust Camera Pose Estimation Using 2D Fiducials Tracking for Real-time Augmented Reality Systems. In Proceedings of ACM SIGGRAPH 04 Int. Conf. on Virtual Reality continuum. .. 431--435.
2. Abawi, D., et al. 2004. Accuracy in Optical Tracking with Fiducial Markers: An Accuracy Function for ARToolKit. In Proceedings of the 3rd IEEE and ACM International Symposium on Mixed and Augmented Reality ISMAR.
3. Abdel-Aziz, Y. I., Karara, H. M. 1971. Direct Linear Transformation into Object Space Coordinates in Close-range Photogrammetry. In Proceedings of Symp. Close-Range Photogrammetry, Univ. of Illinois at Urbana-Champaign. 1--18.
4. Advanced Real Time Tracking GmbH. 2006. A. R. T. System. Website: http://www.ar-tracking.de/1
5. Arvika Consortium 2006. Website: http://www.arvika.de1
6. Benölken, P., et al. 2004. Texture-Based Flow Visualization in Augmented and Virtual Reality Environments. In Proceedings of the 12TH INTERNATIONAL CONFERENCE IN CENTRAL EUROPE ON COMPUTER GRAPHICS; VISUALIZATION AND COMPUTER VISION WSCG 2004. 21--24.
7. Chung, J., et al. 2001. POSTRACK: a low cost real-time motion tracking system for VR application. In Proceedings of the 7th International Conference on Virtual Systems and Multimedia. 383--392.
8. Dementhon, D., Davis, L. S. 1992. Model-Based Object Pose in 25 Lines of Code. In Proceedings of European Conference on Computer Vision ECCV. 335--343.
9. Fischler, M. A., and Bolles, R. C. 1981. Random Sample Cnsensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. In Comm. ACM, Vol. 24, No.6. 381--395.
10. Foxlin, E., et al. 1998. Constellation: A Wide-Range Wireless Motion-Tracking System for Augmented Reality and Virtual Set Applications. In Proceedings of the Annual Conference of Computer Graphics and Interactive Techniques ACM SIGGRAPH 98, M. F. Cohen, ed., ACM Press, New York. 371--378.
11. Hildebrand, A., et al. 2000. ARCHEOGUIDE: An Augmented Reality based System for Personalized Tours in Cultural Heritage Sites. In Cultivate Interactive, Issue 1.
12. Kato, H. et al, 2000. Virtual Object Manipulation on a Table-Top AR Environment. In Proceedings of the INTERNATIONAL SIMPOSIUM ON AUGMENTED REALITY ISAR.
13. Kato, H., and Billinghurst, M. 1999. Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System. In Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality 1999, 85.
14. Lowe, D. G. 1991. Fitting Parameterized Three-Dimensional Models to Images. In IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 13. 441--450.
15. Malbezin, P., et al. 2002. Measuring ARToolKit Accuracy in Long Distance Tracking Experiments. In 1st International Augmented Reality Toolkit Workshop.
16. Mancini, F., et al. 2004. The Virtual Factory -- A Semi-immersive Interactive 3D Environment. In SUMMER COMPUTER SIMULATION CONFERENCE 2004 SCSC2004.
17. Motion Analysis Corporation 2006. Eagle-4 Digital System. Website: http://www.motionanalysis.com/applications/movement/engineer/eagle4system.html1
18. Opencv 2006, from Intel Corporation. Open Source Computer Vision Library. Website: http://www.intel.com/technology/computing/opencv/1
19. Opentracker, Unified Abstract Tracking Layer 2006. Website: http://www.studierstube.org/opentracker1
20. Piekarski, W., and Thomas, B. 2002. Using ARToolKit for 3D Hand Position Tracking in Mobile Outdoor Environments. In 1 st. International Augmented Reality Toolkit Workshop ART 02.
21. Ribo, M., et al. 2001. A New Optical Tracking System for Virtual and Augmented Reality Applications. In Proceedings of the IEEE Instrumentation and Measurement Technology Conference, IMTC 2001, Vol.3. 1932--1936.
22. Santos, P. 2005. A 2D to 3D Geometric Interpolation Algorithm for Marker-based Single-camera Tracking. Master Thesis, Instituto Superior Técnico, Technical University of Lisbon.
23. Santos, P., et al 2003. 3D Interactive Augmented Reality in Early Stages of Product Design. In Proceedings of the 10th International Conference on Human-Computer Interaction (HCI InternationaL). 1203--1207.
24. Schwald, B., et al. 2002. A Flexible Tracking Concept Applied to Medical Scenarios Using an AR Window. In Proceedings of the 2002 IEEE/ACM INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY ISMAR 2002. 261--262.
25. Sezgin, M., Sankur, B. 2004. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. In Journal of Electronic Imaging 13 (1). 146--165.
26. Shortis, M. R., et al. 1994. A Comparison of Some Techniques for The Subpixel Location of Discrete Target Images. In Proceedings of SPIE Videometrics III. 239--250.
27. Tsai, R. Y. 1987. A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology using Off-the-shelf TV Cameras and Lenses. In IEEE Journal of Robotics and Automation RA-3, 4. 323--344.
28. Ueye cameras by IDS Imaging GmbH. 2006. Website: http://www.ids-imaging.de1
29. Viconpeak 2006. Vicon MX System. Website: http://www.viconpeak.com/products/viconmx.html1
30. Welch, G. et al. 2001. High-Performance Wide-Area Optical Tracking -- The HiBall Tracking System. In Presence: Teleoperators and Virtual Environments, Vol. 10, No. 1. 1--21.
31. Welch, G., and Foxlin, E. 2002. Motion Tracking: No Silver Bullet, but a Respectable Arsenal. In IEEE Computer Graphics and Applications, special issue on "Tracking", November/December 2002, 22(6). 24--38.
32. Zhang, Z. 2000. A Flexible New Technique for Camera Calibration. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11). 1330--1334.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Tracking

      • Computer vision tasks

        • Scene understanding

  • Computer graphics

    • Graphics systems and interfaces

      • Graphics processors