VIEW-BASED ROBOT LOCALIZATION USING ILLUMINATION-INVARIANT SPHERICAL HARMONICS DESCRIPTORS

Abstract

In this work we present a view-based approach for robot self-localization using a hemispherical camera system. We use view descriptors that are based upon Spherical Harmonics as orthonormal basis functions on the sphere. The resulting compact representation of the image signal enables us to efficiently compare the views taken at different locations. With the view descriptors stored in a database, we compute a similarity map for the current view by means of a suitable distance metric. Advanced statistical models based upon principal component analysis introduced to that metric allows to deal with severe illumination changes, extending our method to real-world applications.