  The problem visual place recognition is commonly used strategy for
localization. Most successful appearance based methods typically rely on a
large database of views endowed with local or global image descriptors and
strive to retrieve the views of the same location. The quality of the results
is often affected by the density of the reference views and the robustness of
the image representation with respect to viewpoint variations, clutter and
seasonal changes. In this work we present an approach for geo-locating a novel
view and determining camera location and orientation using a map and a sparse
set of geo-tagged reference views. We propose a novel technique for detection
and identification of building facades from geo-tagged reference view using the
map and geometry of the building facades. We compute the likelihood of camera
location and orientation of the query images using the detected landmark
(building) identities from reference views, 2D map of the environment, and
geometry of building facades. We evaluate our approach for building
identification and geo-localization on a new challenging outdoors urban dataset
exhibiting large variations in appearance and viewpoint.
