An Improved RANSAC Algorithm for Simultaneous Localization and Mapping
| Autor: | Xiangyin Zhang, Li Ming'ai, Li Xiuzhi, Zheng Zeling, Guoliang Zhang, Jia Songmin, Jinhui Fan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
For loop
0209 industrial biotechnology History business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Binary number Pattern recognition 02 engineering and technology RANSAC Simultaneous localization and mapping Sample (graphics) Computer Science Applications Education Image (mathematics) Euclidean distance 020901 industrial engineering & automation Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Journal of Physics: Conference Series. 1069:012170 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1069/1/012170 |
| Popis: | Image feature matching is an important part of SLAM (Simultaneous Localization and Mapping algorithm). In order to improve the implementation efficiency of standard RANSAC algorithm, this paper proposed a novel improved RANSAC algorithm to deal with the mismatch in the image matching procedure. Our method deals with raw sample data and predict the inliers in the sample data according to the Euclidean distance between feature descriptors. And then we estimated the homography matrix with the selected sample. The homography matrix is used to eliminate the characteristics of mismatch. Furthermore, a binary environment dictionary is created for loop detection and the experimental results demonstrate that this method improves the speed of loading time of the dictionary and the accuracy of SLAM. |
| Databáze: | OpenAIRE |
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