Robotic systems such as unmanned ground vehicles (UGVs) often depend on GPS for navigation in outdoor environments. In GPS-denied environments, one approach to maintain a global state estimate is localizing based on preexisting georeferenced aerial or satellite imagery. However, this is inherently challenged by the significantly differing perspectives between the UGV and reference images. In this thesis, we introduce a system for global localization of UGVs in remote, natural environments. We use multi-stereo visual inertial odometry (MSVIO) to provide local tracking. To overcome the challenge of differing viewpoints we use a probabilistic occupancy model to generate synthetic orthographic images from color images taken by the UGV. We then derive global information by scan matching local images to existing reference imagery and then use a pose graph to fuse the measurements to provide uninterrupted global positioning after loss of GPS signal. We show that the system generates visually accurate orthographic images of the environment, provides reliable global measurements, and maintains an accurate global state estimate in GPS-denied conditions.