Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics
Abstract Feature matching is an essential part in areas such as target tracking, and three-dimensional reconstruction. In case of rotational motion in the image, the rotating exercise core 8 statistical motion support volume is applied, resulting in low matching accuracy and long time to eliminate m...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10753-0 |
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| Summary: | Abstract Feature matching is an essential part in areas such as target tracking, and three-dimensional reconstruction. In case of rotational motion in the image, the rotating exercise core 8 statistical motion support volume is applied, resulting in low matching accuracy and long time to eliminate mismatching. A principal component analysis method is proposed to calculate rotation angle, feature points are changed in the grid and its neighborhood grid, which sets Gaussian threshold according to Euclid distance between neighborhood feature point and the matching feature point. And a new fractional statistical model is proposed to increase the number of correct matching pairs, So as to improve the fastness and accuracy of characteristic matching. Aiming at the problem of mismatch caused by local similarity of images, a data set is proposed to determine the data set by using geometric relationship between feature points, which analyzes the similarity between the data by the Person correlation coefficient, and sets the threshold to remove the feature matching pairs with low confidence, so as to improve the accuracy of feature matching. Experimental results show that the feature matching speed of the improved GMS algorithm is 3 times that of original GMS algorithm, and the false matching is eliminated in local similar region, which improves the quality of feature matching. |
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| ISSN: | 2045-2322 |