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.
@article{arxiv.2403.08997,
title = {Caltech Aerial RGB-Thermal Dataset in the Wild},
author = {Connor Lee and Matthew Anderson and Nikhil Raganathan and Xingxing Zuo and Kevin Do and Georgia Gkioxari and Soon-Jo Chung},
journal= {arXiv preprint arXiv:2403.08997},
year = {2024}
}