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Improving Data Association in Robot SLAM with Monocular Vision*
| Content Provider | CiteSeerX |
|---|---|
| Author | Sun, Chung-Hsun Hung, Duan-Yan Wang, Yin-Tien |
| Abstract | 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 recognition 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. Experi-ments are carried out on a hand-held camera to verify the performances of the proposed algorithm for dealing with 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 fea-ture matching. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Likelihood-based Tracking Window Data Association Robot Visual Simultaneous Localization Improving Data Association Hand-held Camera Nn Method Monocular Vision Data Association Problem Robot Slam Tracking Window Visual Slam System High-dimensional Data Set Robust Description Successful Fea-ture Matching Surf Feature Robot Visual Slam Speeded-up Robust Feature |
| Content Type | Text |
