Open Access Open Access  Restricted Access Subscription Access

Vision-based Location Recognition using Scale-Invariant Feature Transform for Airship

K. S. Kwon, S. C. Jung, H. J. Kim

Abstract


In this paper, we present a vision-based airship location recognition system using scale-invariant feature transform. The proposed system is consists of three steps: the location database building, the feature extraction and the location recognition. In location database building step, scale-invariant features detected from obtained images in the pre-exploration are indexed into a location database. The next step extracts the features from the images which are acquired from airship mounted camera during its exploration. In final step, we use the voting scheme through matching results between the indexed features of location database and features of images acquired by an airship to determine its location. The proposed system applies to indoor scenes of our building. The experimental results show the recognition performance.

Full Text:

PDF

References


S. Se, D. Lowe and J. Little, Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks, The International Journal of Robotics Research, vol. 21, No. 8, pp. 735-758, 2002.

J. Kosecka and X. Yang, Location Recognition and Global Localization Based on Scale Invariant Features, Wokshop on Statistical Learning in Computer Vision, European Conference on Computer Vision, 2004.

Wang Junqiu, Zha Hongbin and R. Cipolla, Coarse-to-fine vision-based localization by indexing scale-Invariant features, IEEE Transactions on Systems, Man and Cybernetics-Part B, Vol. 36, No. 2, pp. 413-422, 2006.

A. Torralba and P. Sinha, Recognizing indoor scenes, AI Memo, Tech. Rep. 2001-015, 2001.

P. Blaer and P. Allen, Topological mobile robot localization using fast vision techniques, in Proc ICRA 2002, 2002.

J. Wolf, W. Burgard, and H. Burkhardt, Robust vision-based localization for mobile robots using an image retrieval system based on invariant features, In Proc. of the IEEE Int. Conf. On Robotics and Automation, 2002.

T. Starner, B. Schiele, and A. Pentland, Visual contextual awareness in wearable computing, In Intl. Symposium on wearable Computing, pp. 50-57, 1998.

J. Gaspar, N.Winters, and J. Santos-Victor, Vision-based navigation and environmental representations with an omni-directional camera, IEEE Trans RA, vol. 16, no. 6, 2000.

E. Menegatti, M. Zoccarato, E. Pagello, and H. Ishiguro, Image-based monte-carlo localization with omnidirectional images, Robotics and Autonomous Systems, vol. 48, no. 1, 2004.

H. Tamimi and A. Zell, Vision based localization of mobile robots using kernel approaches, in Proc IROS 2004, 2004.

M. Mata, J. M. Armingol, A. de la Escalera, and S. M. A., Using learned visual landmarks for intelligent topological navigation of mobile robots, in Proc ICRA 2003, 2003.

D. G. Lowe, Distinctive image features from scale-invariant keypoints, The international Journal of Computer Vision, vol. 60, pp. 91-110, 2004.

K. Mikoljczyk, T. Tuyelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, A comparison of affine region detectors, International Journal of Computer Vision, Issue 1-2, pp. 43-72, Nov., 2005.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2007.v3.586

Refbacks

  • There are currently no refbacks.