DOI QR코드

DOI QR Code

An Approach for Localization Around Indoor Corridors Based on Visual Attention Model

시각주의 모델을 적용한 실내 복도에서의 위치인식 기법

  • 윤국열 (인하대학교 정보통신공학부) ;
  • 최선욱 (인하대학교 정보통신공학부) ;
  • 이종호 (인하대학교 정보통신공학부)
  • Received : 2010.11.15
  • Accepted : 2010.12.20
  • Published : 2011.02.01

Abstract

For mobile robot, recognizing its current location is very important to navigate autonomously. Especially, loop closing detection that robot recognize location where it has visited before is a kernel problem to solve localization. A considerable amount of research has been conducted on loop closing detection and localization based on appearance because vision sensor has an advantage in terms of costs and various approaching methods to solve this problem. In case of scenes that consist of repeated structures like in corridors, perceptual aliasing in which, the two different locations are recognized as the same, occurs frequently. In this paper, we propose an improved method to recognize location in the scenes which have similar structures. We extracted salient regions from images using visual attention model and calculated weights using distinctive features in the salient region. It makes possible to emphasize unique features in the scene to classify similar-looking locations. In the results of corridor recognition experiments, proposed method showed improved recognition performance. It shows 78.2% in the accuracy of single floor corridor recognition and 71.5% for multi floor corridors recognition.

Keywords

References

  1. P. Newman and K. L. Ho, “SLAM-loop closing with visually salient features,” IEEE International Conference on Robotics and Automation, pp. 635-642, April 2005.
  2. D. C. K. Yuen and B. A. Macdonald, “Vision-based localization algorithm based on landmark matching, triangulation, reconstruction and comparison,” IEEE Transactions on Robotics, vol. 21, no. 2, pp. 217-226, April 2005. https://doi.org/10.1109/TRO.2004.835452
  3. J. Civera, O. G. Grasa, A. J. Davison, and J. M. M. Montiel, “1-point RANSAC for EKF-based structure from motion,” Proc. of the IEEE/RSJ Conference on Intelligent Robots and Systems, pp. 3498-3504, Oct. 2009.
  4. A. J. Davison, I. Reid, N. Molton, and O. Stasse, “MonoSLAM: real-time single camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052-1067, June 2007. https://doi.org/10.1109/TPAMI.2007.1049
  5. M. Cummins and P. Newman, “FAB-MAP: Probabilistic Localization and mapping in the space of appearance,” The International Journal of Robotics Research, vol. 27, no. 6, pp. 647-665, June 2008. https://doi.org/10.1177/0278364908090961
  6. G. Klein and D. Murray, “Parallel tracking and mapping on a camera phone,” Proc. of 8th IEEE International Symposium on Mixed and Augmented Reality, pp. 83-86, Oct. 2009.
  7. L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” Proc. of IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 524-531, June 2005. https://doi.org/10.1109/CVPR.2005.16
  8. K. S. Jones, “A statistical interpretation of term specificity and its application in retrieval,” Journal of Documentation, vol. 28, no. 1, pp. 11-21, Mar. 1972. https://doi.org/10.1108/eb026526
  9. D. G. Lowe, “Object recognition from local scale-invariant features,” Proc. of the International Conference on Computer Vision, vol. 2, pp. 1150-1157, Sep. 1999.
  10. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998. https://doi.org/10.1109/34.730558
  11. J. Harel, A Saliency Implementation in MATLAB: http://www.klab.caltech.edu /~harel/share/gbvs.php.
  12. R. Pal, A. Mukherjee, P. Mitra, and J. Mukherjee, “Modelling visual saliency using degree centrality,” IET Computer Vision, vol. 4, no. 3, pp. 218-229, Sep. 2010. https://doi.org/10.1049/iet-cvi.2009.0067
  13. J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” Advances in Neural Information Processing Systems, vol. 19, pp. 545-552, Dec. 2006.
  14. D. Nist´er and H. Stew´enius, “Scalable recognition with a vocabulary tree,” 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2161-2168, June 2006. https://doi.org/10.1109/CVPR.2006.264