Combining Image Invariant Features and Clustering Techniques for Visual Place Classification
This paper presents the techniques developed by the SIMD group and the results obtained for the 2010 RobotVision task in the ImageCLEF competition. The approach presented tries to solve the problem of robot localization using only visual information. The proposed system presents a classification method using training sequences acquired under different lighting conditions. Well-known SIFT and RANSAC techniques are used to extract invariant points from the images used as training information. Results obtained in the RobotVision@ImageCLEF competition proved the goodness of the proposal.
KeywordsMobile Robot Cluster Technique Training Sequence Invariant Feature Test Frame
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- 1.Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: IEEE International Conference on Robotics and Automation, ICRA 1999 (May 1999)Google Scholar
- 3.Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical Data Analysis Based on the L 1-Norm and Related Methods. North-Holland, Amsterdam (1987)Google Scholar
- 4.Linde, O., Lindeberg, T.: Object recognition using composed receptive field histograms of higher dimensionality. In: International Conference on Pattern Recognition, vol. 4, pp. 1–6 (2004)Google Scholar
- 5.Lowe, D.: Object recognition from local scale-invariant features. In: 17th International Conference on Computer Vision, Corfu, Greece, vol. 2, pp. 1150–1157 (1999)Google Scholar
- 7.Negenborn, R.: Robot Localization and Kalman Filters. Ph.D. thesis, Institute of Information and Computer Science, Copenhagen University (September 2003)Google Scholar