  We present a novel approach to geolocalising panoramic images on a 2-D
cartographic map based on learning a low dimensional embedded space, which
allows a comparison between an image captured at a location and local
neighbourhoods of the map. The representation is not sufficiently
discriminatory to allow localisation from a single image, but when concatenated
along a route, localisation converges quickly, with over 90% accuracy being
achieved for routes of around 200m in length when using Google Street View and
Open Street Map data. The method generalises a previous fixed semantic feature
based approach and achieves significantly higher localisation accuracy and
faster convergence.
