Abstract
Unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images. Thus, accurate, stable, and well-distributed matches to follow the accurate pose estimation of the platform are difficult to obtain. To solve such problems, an effective image matching method for mobile platform visual imagery is presented in this study. The proposed method includes three steps, namely, standard initial matching, transformation matrices evaluation and matching propagation. Firstly, an oriented FAST and rotated BRIEF (ORB) method was used to obtain the number of matches and the initial projective transformation relationship between an image pair. Secondly, an evaluation function was set to choose the suitable rotation matrix for the image scene. Finally, geometric correspondence matching was utilized to propagate matches and produce additional reliable matching results. The geometric correspondence matching used the geometric relationship between the image pair and found more suitable matches than the standard ORB matching. Comprehensive experiments on TUM and ICL-NUIM dataset images showed that the proposed algorithm performs better in terms of correct matches, satisfactory matching rate, and higher matching accuracy than the standard ORB and ORB-slam2 initial match methods.
Highlights
Image matching is the process of finding corresponding points on multi-view images of the same area [1]
The GC-oriented FAST and rotated binary robust independent elementary feature (BRIEF) (ORB) algorithm proposed in this study eliminates mismatched pairs through the geometric correspondence between the image and the surrounding matching points
The standard ORB is characterized by fast speed, with an advantage to ensure satisfactory real-time performance for mobile devices in image acquisition and the feature point method
Summary
Image matching is the process of finding corresponding points on multi-view images of the same area [1]. This process is the premise of automatic image registration [2], [3] and the basis for image sensors to acquire their own pose and for track trajectory, positioning [4]–[6], target recognition [7], and 3D reconstruction [8]. Given the requirement for real-time performance, the corresponding points between the mobile platform visual images are difficult to match. This is because the algorithms require time to avoid the influences from the geometric distortion caused by distance transformation and viewing angle conversion.
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