Abstract
In order to solve the problem that the ORB algorithm increases the probability of feature point loss and mis-matching in some cases such as insufficient light intensity, low texture, large camera rotation, etc. This paper introduces an enhanced graphical local adaptive thresholding (EGLAT) feature extraction algorithm, which enhances the front-end real-time input image to make the blurred texture and corners clearer, replacing the existing ORB extraction method based on static thresholding, the local adaptive thresholding algorithm makes the extraction of feature points more uniform and good quality, avoiding the problems of over-concentration of feature points and partial information loss. Comparing the proposed algorithm with ORB-SLAM2 in a public dataset and a real environment, the results show that our proposed method outperforms the ORB-SLAM2 algorithm in terms of the number of extracted feature points, the correct matching rate and the matching time, especially the matching rate of feature points is improved by 18.7% and the trajectory error of the camera is reduced by 16.5%.
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Wang, S., Zhang, A., Wang, H. (2022). A Feature Extraction Algorithm for Enhancing Graphical Local Adaptive Threshold. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_23
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