Zooming image based false matches elimination algorithms for robot navigation

Feature matching is one of the most important steps in the location technology of zooming images. According to the scale-invariant feature transform matching algorithm, several improved false matches elimination algorithms are proposed and compared in this article. First, features of zooming images and ranging models are introduced in detail in the theory framework of the scale-invariant feature transform feature detection and matching algorithm. The key role of the feature matching algorithm and false matches elimination in the ranging technology of zooming images is discussed and addressed. Second, false matches are eliminated by the proposed approach based on geometry constraint in zooming images with a higher accuracy. Third, false matches are removed by an elimination algorithm based on properties of the scale-invariant feature transform features. Finally, an iterative false matches elimination algorithm based on distance from epipole to epipolar line is proposed and this algorithm can also solve the real-time calibration of the shrink-amplify center for zooming images. Experiments results demonstrate that the three false matches elimination algorithms proposed are stable, and the false matches of feature points can be eliminated effectively with combination of these three methods, and the rest matching points can be applied into robot visual servoing.