GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous
field applications. In this work, we present a GNSS-free global localization
solution that contains a method of registering imaging radar on the ground with
overhead RGB imagery, with joint optimization of relative poses from odometry
and global poses from our overhead registration. Previous works have used
various combinations of ground sensors and overhead imagery, and different
feature extraction and matching methods. These include various handcrafted and
deep-learning-based methods for extracting features from overhead imagery. Our
work presents insights on extracting essential features from RGB overhead
images for effective global localization against overhead imagery using only
ground radar and a single georeferenced initial guess. We motivate our method
by evaluating it on datasets in diverse geographic conditions and robotic
platforms, including on an Unmanned Surface Vessel (USV) as well as urban and
suburban driving datasets.

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