A Slope K method for image based localization

In this paper, we present a SIFT based Slope K method which is faster and more robust than the classical SIFT in landmark based localization. First, the slope k value can be used to erase mismatched feature points (outliers) of the two compared images. Second, the y position is determined by the slope k value. Therefore, the Slope K method is able to localizes about twice as more accurate as the classical SIFT. Another advantage of the proposed method is that the number of database images needed to be matched is significantly reduced, compared to the classical SIFT. Therefore the time cost is approximate 4 times less than that of the classical SIFT.