  The Global Positioning System (GPS) has become a part of our daily life with
the primary goal of providing geopositioning service. For an unmanned aerial
system (UAS), geolocalization ability is an extremely important necessity which
is achieved using Inertial Navigation System (INS) with the GPS at its heart.
Without geopositioning service, UAS is unable to fly to its destination or come
back home. Unfortunately, GPS signals can be jammed and suffer from a multipath
problem in urban canyons. Our goal is to propose an alternative approach to
geolocalize a UAS when GPS signal is degraded or denied. Considering UAS has a
downward-looking camera on its platform that can acquire real-time images as
the platform flies, we apply modern deep learning techniques to achieve
geolocalization. In particular, we perform image matching to establish latent
feature conjugates between UAS acquired imagery and satellite orthophotos. A
typical application of feature matching suffers from high-rise buildings and
new constructions in the field that introduce uncertainties into homography
estimation, hence results in poor geolocalization performance. Instead, we
extract GIS information from OpenStreetMap (OSM) to semantically segment
matched features into building and terrain classes. The GIS mask works as a
filter in selecting semantically matched features that enhance coplanarity
conditions and the UAS geolocalization accuracy. Once the paper is published
our code will be publicly available at
https://github.com/OSUPCVLab/UbihereDrone2021.
