The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Indoor Passage Tracking based Transformed Generic Model

일반화된 모델의 변형에 의한 실내 통로공간 추적

  • 이서진 (이화여자대학교 디지털미디어학부) ;
  • 남양희 (이화여자대학교 디지털미디어학부)
  • Received : 2010.03.03
  • Accepted : 2010.04.15
  • Published : 2010.04.28
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Abstract

In Augmented Reality, it needs restoration and tracking of a real-time scene structure for the augmented 3D model from input video or images. Most of the previous approaches construct accurate 3D models in advance and try to fit them in real-time. However, it is difficult to measure 3D model accurately and requires long pre-processing time to construct exact 3D model specifically. In this research, we suggest a real-time scene structure analysis method for the wide indoor mobile augmented reality, using only generic models without exact pre-constructed models. Our approach reduces cost and time by removing exact modeling process and demonstrates the method for restoration and tracking of the indoor repetitive scene structure such as corridors and stairways in different scales and details.

증강현실에서 3차원적 증강은 입력 비디오나 이미지로부터 3차원 구조 복원 및 추적을 필요로 한다. 이를 위해 제안된 기존의 방법들은 대개 정확한 3차원 모델을 사전에 구축해두고 실시간에 대조하는 방식을 취하였는데, 이 방식은 정확한 측정에 기반한 모델이 있어야 한다는 점과 대상물을 일일이 모델링해야 하는 문제점이 있다. 본 논문은 각 대상물별 정밀한 모델 없이 유형별로 일반화된(generic) 모델만을 사용하는 방식을 제안함으로써 층간 이동 등을 허용하는 광범위 이동형 실내 증강현실 응용 가능성을 제시하고자 하였다. 제안한 방법은 일반화된(generic) 모델을 변형(affine transformation)하여 주어진 장면에서 오류 임계치 이내의 정합을 이루는 모델 변형 값인 스케일, 위치, 회전 값을 찾아냄으로써, 그에 따라 정합된 3차원 공간구조에 관해 일관성 있게 증강객체가 배치될 수 있도록 하는 것이다. 이 방법은 정밀 모델링에 드는 시간과 노력 비용을 줄이며, 실험을 통해 크기나 디테일은 다르지만 유사 패턴이 반복되는 통로구조의 복원과 추적에 사용될 수 있음을 보였다.

Keywords

References

  1. G. Simon, A. W. Fitzgibbon, and A. Zisserman, "Markerless Tracking using Plnar Structures in the Scene," International Symposium on Augmented Reality, pp.120-128, 2000. https://doi.org/10.1109/ISAR.2000.880935
  2. L. Libuda and K. Kraiss, "Identification of Natural Landmarks for Vision Based Navigation," Proceedings of IEEE International Conference on Robotics and Automation, 2000.
  3. R. Gerhard and T. W. Drummond, "Going Out: Robust Model-based Tracking for Outdoor Augmented Reality," IEEE International Symposium on Mixed and Augmented Reality, 2006. https://doi.org/10.1109/ISMAR.2006.297801
  4. I. C. Andrew, M. Eric, and C. Francois, "Robust Model-based Tracking for Robot Vision," IEEE International Conference on Intelligent Robots and Systems, 2004 . https://doi.org/10.1109/IROS.2004.1389433
  5. C. Harris, Tracking with Rigid Objects, MIT Press, 1992.
  6. I. C. Andrew and P. Muriel, "Real-time Markerless Tracking for Augmented Reality: The Virtual Visual Servoing Framework," IEEE Transactions on Visualization and Computer Graphics, Vol.12, issue.4, pp.615-628, 2006. https://doi.org/10.1109/TVCG.2006.78
  7. G. Simon, "Automatic Online Walls Detection for immediate Use in AR Tasks," IEEE International Symposium on Mixed and Augmented Reality, 2006. https://doi.org/10.1109/ISMAR.2006.297792
  8. A. P. Gee, C. Denis, M. Walterio, and C. Andrew, "Discovering Planes and Collapsing the State Space in Visual SLAM," British Machine Vision Conference, 2007.
  9. C. Denis, G. Andrew, C. Andrew, and M. C. Walterio, "Ninja on a Plane: Automatic Discovery on Physical Planes for Augmented Reality using Visual SLAM," IEEE International Symposium on Mixed and Augmented Reality, 2007. https://doi.org/10.1109/ISMAR.2007.4538840
  10. A. P. Gee and M. C. Walterio, "Real-Time Model-Based SLAM Using Line Segments," International Symposium on Visual Computing, 2006.
  11. G. Klein and D. Murray, "Parallel Tracking and Mapping for Small AR Workspaces," In ISMAR'07, Nara, Japan, 2007(11). https://doi.org/10.1109/ISMAR.2007.4538852
  12. R. O. Castle and D. W. Murray, "Object Recognition and Localization while Tracking and Mapping," Proceedings of the 8th IEEE/ACM International Syposium on Mixed and Augmented Reality, 2009. https://doi.org/10.1109/ISMAR.2009.5336477
  13. G. Schall, H. Grabner, M. Grabner, P. Wohlhart, D. Schmlstieg, and H. Bischof, "3D Tracking in Unknown Environments Using On-Line Keypoint Learning for Mobile Augmented Reality," In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.285-288, 2008. https://doi.org/10.1109/CVPRW.2008.4563134
  14. H. P. William, P. F. Brian, A. T. Saul, and T. V. William, Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, 1992.
  15. P. Bouthemy, "A Maximum Likelihood Framework for Determining Moving Edges," IEEE Trans. Pattern Analysis and Machine Inteligence, Vol.11, No.5, pp.499-511, 1989. https://doi.org/10.1109/34.24782
  16. N. Karlsson, E. D. Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, and M. E. Munich, "The vSLAM Algorithm for Robust Localization and Mapping," Proceedings of IEEE International Conference on Robotics and Automation, 2005.
  17. J. M. M. Montiel, J. Civera, and J. D. Andrew, "Unified Inverse Depth Parameterization for Monocular SLAM," Proceedings of Robotics: Science and Systems, 2006.
  18. J. D. Andrew, D. R. Ian, D. M. Nicholas, and S. Olivier, "MonoSLAM: Real-Time Single Camera SLAM," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.29, issue.6, pp.1052-1067, 2007. https://doi.org/10.1109/TPAMI.2007.1049
  19. http://www.doc.ic.ac.uk/~ajd/index.html
  20. R. Edward and T. Drummond, "Machine Learning for High-Speed Corner Detection," In European Conference on Computer Vision, Vol.1, pp.430-443, 2006. https://doi.org/10.1007/11744023_34
  21. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol.60, pp.91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94