  Publicly available satellite imagery can be an ubiquitous, cheap, and
powerful tool for vehicle localisation when a prior sensor map is unavailable.
However, satellite images are not directly comparable to data from ground range
sensors because of their starkly different modalities. We present a learned
metric localisation method that not only handles the modality difference, but
is cheap to train, learning in a self-supervised fashion without metrically
accurate ground truth. By evaluating across multiple real-world datasets, we
demonstrate the robustness and versatility of our method for various sensor
configurations. We pay particular attention to the use of millimetre wave
radar, which, owing to its complex interaction with the scene and its immunity
to weather and lighting, makes for a compelling and valuable use case.
