Fast Outdoor Robot Localization Using Integral Invariants

Global Integral Invariant Features have shown to be useful for robot localization in indoor environments. In this paper, we present a method that uses integral invariants for outdoor environments. To make the integral invariant features more distinctive for outdoor images, we split the image into a grid of subimages and calculate integral invariants for each grid cell individually. We then concatenate the results to get the feature vector for the image. Additionally, we combine this method with a particle filter to improve the localization results. We compare our approach to a Scale Invariant Feature Transform (SIFT)-based approach on images of two outdoor areas under different illumination conditions. The results show that the SIFT approach is more exact, but the integral invariant approach is faster and allows localization in significantly less than one second.