ORB Feature Matching Algorithm Based on Multi-Scale Feature Description Fusion and Feature Point Mapping Error Correction

To improve the accuracy of feature extraction and description of various scales in traditional Oriented FAST and Rotated BRIEF (ORB) feature matching algorithm, this paper proposes an ORB feature matching algorithm based on multi-scale feature description fusion and feature point mapping error correction. Firstly, when establishing the feature scale pyramid, the method of using the same patch-size for description on each feature layer is adopted instead of using different patch-sizes on a unified feature layer in the original algorithm, which enhances the robustness of the descriptor and improves the matching accuracy. Secondly, FAST-SCORE maps are established on different scales, and the coordinates of high-level feature points mapped to the bottom layer are corrected to further improve the positioning accuracy of feature points. The algorithm is verified in remote sensing images, autonomous driving, and industrial automation fields. Experimental results show that when resisting theoretical interference, the average matching accuracy of the proposed algorithm is 67.9%, which is about 2.0 times that of the ORB algorithm, and the average stability is 14.0, which is about 1.5 times that of the ORB algorithm. After correcting the feature point mapping, the matching accuracy can be further improved by 19.2%, indicating that the improved algorithm has excellent robustness when resisting interference. In the experiments on the KITTI and custom datasets, the matching accuracy of the proposed algorithm reached 88.70% and 96.88%, respectively, which is an improvement of 10.15% and 1.2% compared to the ORB algorithm. At the same time, the matching time was reduced by 17.34% and 24.30%, ensuring the accuracy and real-time performance of the algorithm in practical scenarios.