CART: Caltech Aerial RGB-Thermal Dataset in the Wild
Connor Lee Matthew Anderson Nikhil Raganathan Xingxing Zuo Kevin Do Georgia Gkioxari Soon-Jo Chung
We present the first publicly available RGB-thermal dataset designed for
aerial robotics operating in natural environments. Our dataset captures a
variety of terrains across the continental United States, including rivers,
lakes, coastlines, deserts, and forests, and consists of synchronized RGB,
long-wave thermal, global positioning, and inertial data. Furthermore, we
provide semantic segmentation annotations for 10 classes commonly encountered
in natural settings in order to facilitate 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
semantic segmentation, RGB-to-thermal image translation, and visual-inertial
odometry. We present extensive results using state-of-the-art methods and
highlight the challenges posed by temporal and geographical domain shifts in
our data. Dataset and accompanying code will be provided at
https://github.com/aerorobotics/caltech-aerial-rgbt-dataset