FLAG: Feature-based Localization between Air and Ground

In GPS-denied environments, robot systems typically revert to navigating with dead-reckoning and relative mapping, accumulating error in their global pose estimate. In this paper, we propose Feature-based Localization between Air and Ground (FLAG), a method for computing global position updates by matching features observed from ground to features in an aerial image. Our method uses stable, descriptorless features associated with vertical structure in the environment around a ground robot in previously unmapped areas, referencing only overhead imagery, without GPS. Multiple-hypothesis data association with a particle filter enables efficient recovery from data association error and odometry uncertainty. We implement a stereo system to demonstrate our vertical feature based global positioning approach in both indoor and outdoor scenarios, and show comparable performance to laser-scan-matching results in both environments.