Advanced tracking through efficient image processing and visual-inertial sensor fusion

Computers and Graphics (Pergamon)

Volumn 33, Issue 1, 2009, Pages 59-72

  Bleser, Gabriele ^a   Stricker, Didier ^a  

^a FRAUNHOFER IGD   (Germany)

Author keywords

Augmented reality; Extended Kalman filter; Inertial sensors; Markerless model based camera tracking; Sensor fusion

Indexed keywords

3D MODELING; ACCELEROMETERS; AUGMENTED REALITY; EXTENDED KALMAN FILTERS; IMAGE PROCESSING; INERTIAL NAVIGATION SYSTEMS; KALMAN FILTERS; THREE DIMENSIONAL COMPUTER GRAPHICS; VIRTUAL ADDRESSES; VIRTUAL REALITY;

ACCELEROMETER DATA; AUGMENTED AND VIRTUAL REALITIES; INERTIAL SENSOR; LINEAR ACCELEROMETERS; MODEL-BASED CAMERA TRACKING; SENSOR FUSION; SENSOR FUSION ALGORITHMS; TRACKING PERFORMANCE;

SENSOR DATA FUSION;

EID: 59449108819     PISSN: 00978493     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cag.2008.11.004     Document Type: Article

References (41)

1. Titterton D., and Weston J. Strapdown inertial navigation technology. 2nd ed. (2004), American Institute of Aeronautics and Astronautics, NJ
2. Yokokohji Y, Sugawara Y, Yoshikawa T. Accurate image overlay on video see-through HMDs using vision and accelerometers. In: IEEE virtual reality conference, New Brunswick, NJ, USA, March 2000. p. 247-54.
3. Qian G., Chellappa R., and Zheng Q. Robust structure from motion estimation using inertial data. Optical Society of America 18 (2001) 2982-2997
4. Klein G, Drummond T. Tightly integrated sensor fusion for robust visual tracking. In: British machine vision conference, vol. 2, Cardiff, September 2002. p. 787-96.
5. Aron M, Simon G, Berger M-O. Handling uncertain sensor data in vision-based camera tracking. In: International symposium on mixed and augmented reality, Arlington, VA, November 2004. p. 58-67.
6. Pinies P, Lupton T, Sukkarieh S, Tardos JD. Inertial aiding of inverse depth SLAM using a monocular camera. In: IEEE international conference on robotics and automation, Rome, Italy, April 2007.
7. Reitmayr G, Drummond T. Going out: robust model-based tracking for outdoor augmented reality. In: International symposium on mixed and augmented reality, Santa Barabara, CA, October 2006. p. 109-18.
8. Qian G, Chellappa R, Zheng Q. Bayesian structure from motion using inertial information. In: International conference on image processing, Rochester, NY, USA, September 2002.
9. You S, Neumann U. Fusion of vision and gyro tracking for robust augmented reality registration. In: Virtual reality, Yokohama, Japan, March 2001.
10. Jiang B, Neumann U, You S. A robust hybrid tracking system for outdoor augmented reality. In: IEEE virtual reality conference, Chicago, Illinois, USA, March 2004.
11. Foxlin E, Naimark L. VIS-tracker: a wearable vision-inertial self-tracker. In: IEEE virtual reality, Los Angeles, CA, March 2003.
12. Rehbinder H., and Gosh B. Pose estimation using line-based dynamic vision and inertial sensors. IEEE Transactions on Automatic Control 48 (2003) 186-199
13. Schön T, Gustafsson F. Integrated navigation of cameras for augmented reality. In: International federation of automatic control (IFAC) World congress, Prague, Czech Republic. International Federation of Automatic Control; July 2005.
14. Hol J, Schön T, Gustafsson F, Slycke P. Sensor fusion for augmented reality. In: International conference on information fusion, Florence, Italy, August 2006.
15. Schön T, Karlsson R, Törnqvist D, Gustafsson F. A framework for simultaneous localization and mapping utilizing model structure. In: International conference on information fusion, Quebec, Canada, July 2007.
16. Mikolajczyk K., and Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 1615-1630
17. Lepetit V., and Fua P. Keypoint recognition using randomized trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 9 (2006) 1465-1479
18. Boffy A, Genc Y. Real-time feature matching using adaptive and spatially distributed classification. In: British machine vision conference, Edinburgh, September 2006.
19. Grabner M, Grabner H, Horst B. Tracking via discriminative online learning of local features. In: Conference on computer vision and pattern recognition, Minneapolis, MN, June 2007.
20. Wuest H, Vial F, Stricker D. Adaptive line tracking with multiple hypotheses for augmented reality. In: International symposium on mixed and augmented reality, Vienna, Austria, October 2005. p. 62-9.
21. Shi J, Tomasi C. Good features to track. In: Conference on computer vision and pattern recognition, Seattle, June 1994. p. 593-600.
22. Heikkilae J, Silven O. A four-step camera calibration procedure with implicit image correction. In: Conference on computer vision and pattern recognition, San Juan, Puerto Rico, June 1997.
23. Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: IEEE international conference on computer vision, vol. 2, Beijing, China, October 2005. p. 1508-11.
24. Bleser G, Wuest H, Stricker D. Online camera pose estimation in partially known and dynamic scenes. In: International symposium on mixed and augmented reality, Santa Barabara, CA, October 2006. p. 56-65.
25. Bleser G, Stricker D. Advanced tracking through efficient image processing and visual-inertial sensor fusion. In: IEEE virtual reality conference, Reno, Nevada, March 2008.
26. Kim J., and Sukkarieh S. Real-time implementation of airborne inertial-SLAM. Robotics and Autonomous Systems 55 (2007) 62-71
27. Zinßer T, Gräßl C, Niemann H. Efficient feature tracking for long video sequences. In: Deutsche Arbeitsgemeinschaft Mustererkennung, Täbingen, Germany, August 2004. p. 326-33.
28. Kalman R.E. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering 82 Series D (1960) 35-45
29. Welch G, Bishop G. An introduction to the Kalman filter. Technical Report, Department of Computer Science, University of North Carolina at Chapel Hill; 2001.
30. Thrun S., Burgard W., and Fox D. Probabilistic robotics (2005), MIT Press, Cambridge
31. Zhang Z., and Faugeras O. 3D dynamic scene analysis (1992), Springer, Berlin
32. Gustafsson F. Adaptive filtering and change detection (2000), Wiley, New York
33. Julier S., and LaViola J. On Kalman filtering with nonlinear equality constraints. IEEE Transactions on Signal Processing 55 (2007) 2774-2784
34. Horn BKP. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 1987;4:629-42.
35. Pollefeys M, Koch R, Gool LJV. Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: International conference on computer vision, Bombay, India, January 1998. p. 90-5.
36. Pollefeys M, Gool LV, Vergauwen M, Verbiest F, Cornelis K, Tops J, et al. Visual modeling with a hand-held camera. International Journal of Computer Vision 2004;207-32.
37. Rehbinder H, Hu X. Drift-free attitude estimation for accelerated rigid bodies. In: IEEE international conference on robotics and automation, Seoul, Korea, May 2001.
38. Harada T., Mori T., and Sato T. Development of a tiny orientation estimation device to operate under motion and magnetic disturbance. The International Journal of Robotics Research 26 6 (2007) 547-559
39. Thomas G. Real-time camera pose estimation for augmenting sports scenes. In: Conference on Visual Media Production, London, UK, November 2006.
40. Wuest H, Wientapper F, Stricker D. Adaptable model-based tracking using analysis-by-synthesis techniques. In: International conference on computer analysis of images and patterns, Vienna, Austria, August 2007.
41. Koch O, Teller S. Wide-area egomotion estimation from known 3D structure. In: Conference on computer vision and pattern recognition, Minneapolis, Minnesota, June 2007.