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YIN-TIEN WANG, DUAN-YAN HUNG AND CHUNG-HSUN SUN
Department of Mechanical and Electro-Mechanical Engineering
Tamkang University
Tamsui, 251 Taiwan
In the paper, an algorithm is proposed for improving the data association in robot visual
Simultaneous Localization and Mapping (SLAM). The detection of speeded-up robust
feature (SURF) is employed in the algorithm to provide a robust description for image features
as well as a better representation of landmarks in the map of a visual SLAM system.
Meanwhile, a likelihood-based tracking window and a nearest-neighbor (NN) method are
utilized to match the high-dimensional data sets created for SURF. Experiments are carried
out on a hand-held camera to verify the performances of the proposed algorithm for dealing
with the data association problem in robot visual SLAM. The results show that the integration
of the SURF features, the tracking window and the NN method is efficient in reducing
the computational time and increasing the rate of successful feature matching.
Received October 7, 2010; revised December 27, 2010 & February 22, 2011; accepted March 1, 2011.
Communicated by Chung-Lin Huang.
* This work was partially supported by the National Science Council of Taiwan, under Grants No. NSC 99-2221-E-032-064 and NSC 99-2221-E-032-065.