  We present the first publicly-available RGB-thermal dataset designed for
aerial robotics operating in natural environments. Our dataset captures a
variety of terrain across the United States, including rivers, lakes,
coastlines, deserts, and forests, and consists of synchronized RGB, thermal,
global positioning, and inertial data. We provide semantic segmentation
annotations for 10 classes commonly encountered in natural settings in order to
drive the development of perception algorithms robust to adverse weather and
nighttime conditions. Using this dataset, we propose new and challenging
benchmarks for thermal and RGB-thermal (RGB-T) semantic segmentation, RGB-T
image translation, and motion tracking. We present extensive results using
state-of-the-art methods and highlight the challenges posed by temporal and
geographical domain shifts in our data. The dataset and accompanying code is
available at https://github.com/aerorobotics/caltech-aerial-rgbt-dataset.
