Color Landmark Based Self-Localization for Indoor Mobile Robots

이동 로봇을 위한 컬러 표식 기반 자기 위치 추정 기법

  • Yoon, Kuk-Jin (Korea Advanced Institute of Science and Technology) ;
  • Jang, Gi-Jeong (Korea Advanced Institute of Science and Technology) ;
  • Kim, Sung-Ho (Korea Advanced Institute of Science and Technology) ;
  • Kweon, In-So (Korea Advanced Institute of Science and Technology)
  • Published : 2001.09.01

Abstract

We present a simple artificial landmark model and robust landmark tracking algorithm for mobile robot localization. The landmark model, consisting of symmetric and repetitive color patches, produces color histograms that are invariant under the geometric and photometric distortions. A stochastic approach based on the CONDENSATION tracks the landmark model robustly even under the varying illumination conditions. After the landmark detection, relative position of the mobile robot to the landmark is calculated. Experimental results show that the proposed landmark model is effective and can be detected and tracked in a clustered scene robustly. With the tracked single landmark, we extract geometrical information than achieve accurate localization.

본 논문에서는 이동 로봇의 자기 위치 추(self-localization)을 위해 간단하고 효육적인 컬러 표식 모델과 추적 기법을 제안하고, 제안된 표식을 이용한 위치 추정 기법을 제안한다. 본 논문에서 제안한 표식모델은 대칭적이고 반복적인 컬러 패턴을 갖는데. 이러한 기하학적 형태로 인해 표식 모델은 기하학적 변형이나 광학적 변형에 대해 불변인 히스토그램 특성을 나타낸다. 이러한 특징을 영상 내 표식 검출 및 추적을 위한 유사 척도로 사용하고 컨데세이션(CONDENSATION)에 기반한 확률적 접근 방식을 통해 복잡한 환경 하에서도 표식 모델을 강인하게 추적할 수 있다. 표식 모델이 검출된 후에는 표식이 갖는 기하작적 정보를 이용하여 이동 로봇과표식간의 상대적인 위치를 정확하게 추정한다.

Keywords

References

  1. A. Carbonaro and P. Zingaretti, 'Landmark matching in a varying enviroment,' Proc. of Euromicro Workshop on Advanced Mobile Robots, pp. 147-153, 1997 https://doi.org/10.1109/EURBOT.1997.633621
  2. A. J. Briggs, D. Scharstein, D. Braziunas, C. Dima, and P. Wall, 'Mobile robot navigation using self-similar landmarks,' Proc. of IEEE International Conference on Robotics and Automation, pp. 1428-1434, 2000 https://doi.org/10.1109/ROBOT.2000.844798
  3. C. F. Olson, 'Probabilistic self-localization for mobile robots,' IEEE Transaction on Robotics and Automation, vol. 16 1, pp. 55-66, Feb., 2000 https://doi.org/10.1109/70.833191
  4. E. B. Meier and F. Ade, 'Using the condensation algorithm to implement tracking for mobile robots,' Third European Workshop on Advanced Mobile Robots(Eurobot '99), pp. 73-80, 1999 https://doi.org/10.1109/EURBOT.1999.827624
  5. F. Dellaert, W. Burgard, and D. Fox, S. Thurn, 'Using the CONDENSATION algorithm for robust, vision-based mobile robot localization,' IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 588-594, 1999 https://doi.org/10.1109/CVPR.1999.784976
  6. M. Betke and L. Gurvits, 'Mobile robot localization using landmarks,' IEEE Transaction on Robotics and Automation, vol. 13, no. 2, pp. 251-263, 1997 https://doi.org/10.1109/70.563647
  7. M. Isard and A. Blake, 'CONDENSATION-conditional density propagation for visual tracking,' Int. J. Computer vision, 1998 https://doi.org/10.1023/A:1008078328650
  8. P. E. Trahanias, S. Velissaris, and T. Garavelos, 'Visual landmark extraction and recognition for autonomous robot navigation,' IEEE/RSJ Intl. Conf. on Intell. Robots and Systems, Artria, Grenoble, Frence, pp. 1036-1042, Sep. 7-12, 1997 https://doi.org/10.1109/IROS.1997.655138
  9. R. Murrieta-Cid, M. Briot, and N. Vandapel, 'Landmark identification and tracking in natural enviroment,' Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 179-184, 1998 https://doi.org/10.1109/IROS.1998.724616
  10. R. Schuste, 'Color object tracking with adaptive modeling,' Proc. of IEEE Symposium on Visual Languages, pp. 91-96, 1994
  11. R. Sim and G. Dudek, 'Mobile robot localization from learned landmarks,' Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 1060-1065, 1998 https://doi.org/10.1109/IROS.1998.727439
  12. S. Li and S. Tsuji, 'Finding landmarks autonomously along a route,' Proc. of 11th IAPR International Conference on Computer Vision and Application, pp. 316-319, 1992 https://doi.org/10.1109/ICPR.1992.201565
  13. S. Thrun, 'Finding landmarks for mobile robot navigation,' Proc. of IEEE International Conference on Robotics and Automation, vol. 2, pp. 958-963, 1998
  14. P. Lamon, I. Nourbakhsh, B. Jensen, and R. Siegwart 'Deriving and matching image fingerprint sequences for mobile robot localization,' Proc. of IEEE International Conference on Robotics and Automation, pp. 1609-1614, 2001 https://doi.org/10.1109/ROBOT.2001.932841
  15. L. Palerra, S. Frintrop, and J. Hertzberg, 'Robust localization using context in omnidirectional imaging,' Proc. of IEEE International Conference on Robotics and Automation, pp. 2072-2077, 2001 https://doi.org/10.1109/ROBOT.2001.932912
  16. K. Yoon, G. Gang, S. Kim, and I. Kweon, 'Fast landmark tracking and localization algorithm for the mobile self-localization,' IFAC Workshop on Mobile Robot Technology, pp. 190-195, 2001
  17. I. Shimshoni, 'A fast linear method for for mobile robot localization from landmark bearings,' IFAC Workshop on Mobile Robot Technology, pp. 121-126, 2001
  18. R. Y. Tsai, 'A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-self TV cameras and lenses,' IEEE Journal of robotics and Automation, vol. RA-3, no. 4, pp. 323-344, 1987
  19. H. Kim, J. Cho, and I. Kweon, 'A novel image-based contol-law for the visual servoing system under large pose error,' IEEL/RJS International Conference on Intelligent Robots and Systems, pp. 263-268, 2000 https://doi.org/10.1109/IROS.2000.894615
  20. R. Hartley and A. Zisserman, 'Multiple view geometry in computer vision,' Cambridge University Press, UK, 2000