FAST VISION-BASED LOCALIZATION FOR OUTDOOR ROBOTS USING A COMBINATION OF GLOBAL IMAGE FEATURES

In this paper, we present a geometrical localization method based on a combination of global image features. Our method represents each image by two feature vectors. The first feature vector is a Weighted Gradient Orientation Histogram (WGOH). The second feature vector is a Weighted Grid Integral Invariant (WGII) feature vector based on Integral Invariants. For localization, we use a particle filter which updates the weights of the particles based on image similarities calculated from the two feature vectors. We evaluate our approach on outdoor images of two different areas and under varying illumination and compare it to a SIFT-based approach. The comparison shows that the SIFT approach is slightly more exact than our method, but our method is more than four times faster than the SIFT approach and allows a localization frequency of more than 2 Hz.