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

翻译：摘要：我们首次公开了一个专为自然环境中运行的空中机器人设计的RGB-热数据集。该数据集捕捉了美国大陆多种地形场景，包括河流、湖泊、海岸线、沙漠和森林，并包含同步的RGB、长波热、全球定位和惯性数据。此外，我们提供了自然环境中常见10个类别的语义分割标注，以促进鲁棒于恶劣天气和夜间条件的感知算法开发。基于此数据集，我们提出了热成像与RGB-热语义分割、RGB到热图像转换以及视觉惯性里程计等具有挑战性的新基准。我们采用最先进方法呈现了广泛的结果，并重点揭示了数据中时间与地理域偏移带来的挑战。数据集及配套代码将发布于https://github.com/aerorobotics/caltech-aerial-rgbt-dataset