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.
@article{arxiv.1609.04794,
title = {Semantics for UGV Registration in GPS-denied Environments},
author = {Gordon Christie and Garrett Warnell and Kevin Kochersberger},
journal= {arXiv preprint arXiv:1609.04794},
year = {2016}
}