Semantics for UGV Registration in GPS-denied Environments
This addresses localization for multi-robot operations in unknown environments, but it is incremental as it builds on existing semantic methods for robustness.
The paper tackles the problem of localizing an unmanned ground vehicle (UGV) within a 2.5D aerial map in GPS-denied environments with appearance changes, achieving results within five meters of a GPS-based approach.
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.