  Robot localization remains a challenging task in GPS denied environments.
State estimation approaches based on local sensors, e.g. cameras or IMUs, are
drifting-prone for long-range missions as error accumulates. In this study, we
aim to address this problem by localizing image observations in a 2D
multi-modal geospatial map. We introduce the cross-scale dataset and a
methodology to produce additional data from cross-modality sources. We propose
a framework that learns cross-scale visual representations without supervision.
Experiments are conducted on data from two different domains, underwater and
aerial. In contrast to existing studies in cross-view image geo-localization,
our approach a) performs better on smaller-scale multi-modal maps; b) is more
computationally efficient for real-time applications; c) can serve directly in
concert with state estimation pipelines.
