An improved GMS-PROSAC algorithm for image mismatch elimination

• Main Authors: Panpan Zhao, Derui Ding, Yongxiong Wang, Hongjian Liu
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Systems Science & Control Engineering
• Subjects: Image matching; epipolar geometric constraint model; projection error function
• Online Access: http://dx.doi.org/10.1080/21642583.2018.1477635

Image matching usually plays a critical role for visual simultaneous localization and mapping (VSLAM). However, the resultant mismatches and low calculation efficiency of current matching algorithms reduces the performance of VSLAM. In order to overcome above two drawbacks, an improved GMS-PROSAC algorithm for image mismatch elimination is developed in this paper by introducing a new epipolar geometric constraint (EGC) model with a projection error function. This improved algorithm, named as GMS-EGCPROSAC, is made up of the traditional GMS algorithm and the improved PROSAC algorithm. First, the GMS algorithm is employed to obtain a rough matching set and then all matching pairs in this set are sorted according to their similarity degree. By selecting some smatching pairs with the highest similarity degree, the parameter of the EGC model is obtained. By resort to the calculated parameter, the improved PROSAC algorithm can be carried out to eliminate false matches. Finally, the real-time and the effectiveness are adequately verified by executing some contrast experiments. Our approach can not only quickly eliminate mismatches but also get more high-quality matching pairs.