Tracking and matching of track closed loop sequence wide baseline image feature

In the three-dimensional reconstruction of unmanned aerial vehicle (UAV) oblique photography, the variation of illumination and viewing angle will lead to the instability of interest points extraction. The wide-baseline images are invalidated by neighborhood cross-correlation methods. Based on the analysis of continuous closed-loop image data, a feature tracking and matching algorithm of track closed loop sequence wide-baseline image is introduced in this paper. Firstly, Interest points of each image are extracted by SuperPoint algorithm, and the continuous pairwise matching is carried out by SuperGlue algorithm; then, the matching results are used for feature tracking in both positive and negative directions, and the feature tracking results of the two directions are fused; finally, DEGENSAC is used to filter outliers, so as to obtain the optimal matching result. The experimental results show that, t for wide-baseline image data, the matching points obtained by this algorithm are more uniform than those obtained by ASIFT +FLANN algorithm, and more feature points can be matched than those obtained by SuperPoint+SuperGlue algorithm based on machine learning, and this algorithm is more robust in wide-baseline feature matching.