  Localization in a global map is critical to success in many autonomous robot
missions. This is particularly challenging for multi-robot operations in
unknown and adverse environments. Here, we are concerned with providing a small
unmanned ground vehicle (UGV) the ability to localize itself within a 2.5D
aerial map generated from imagery captured by a low-flying unmanned aerial
vehicle (UAV). We consider the scenario where GPS is unavailable and
appearance-based scene changes may have occurred between the UAV's flight and
the start of the UGV's mission. We present a GPS-free solution to this
localization problem that is robust to appearance shifts by exploiting
high-level, semantic representations of image and depth data. Using data
gathered at an urban test site, we empirically demonstrate that our technique
yields results within five meters of a GPS-based approach.
