Caltech Aerial RGB-Thermal Dataset in the Wild

Summary
This is the first publicly available RGB-thermal dataset tailored for aerial field robotics operating in natural environments. It encompasses various terrains across the continental United States, including rivers, lakes, coastlines, deserts, and forests. The dataset captures synchronized RGB, long-wave thermal, GPS, and IMU data.  Additionally, semantic segmentation annotations for 10 classes commonly encountered in natural settings are provided to aid in the development of thermal perception algorithms. 
Benchmarks
The dataset introduces benchmarks for:  thermal semantic segmentation, RGB-Thermal semantic segmentation, RGB-to-Thermal image translation, and visual-inertial odometry (VIO) / simultaneous Localization and Mapping (SLAM). 
Code
Please use the accompanying code and scripts to help process the data: https://github.com/aerorobotics/caltech-aerial-rgbt-dataset 
Citation: 
If this dataset has been useful to you, please cite our paper:

@article{lee2024cart,
 title={CART: Caltech aerial RGB-thermal dataset in the wild},
 author={Lee, Connor and Anderson, Matthew and Raganathan, Nikhil and Zuo, Xingxing and Do, Kevin and Gkioxari, Georgia and Chung, Soon-Jo},
 journal={arXiv preprint arXiv:2403.08997},
 year={2024}
}

搜集汇总

背景与挑战

背景概述

该数据集是首个公开可用的RGB-热数据集，专为自然环境中运行的空中野外机器人设计，覆盖美国大陆多种地形（如河流、湖泊、沙漠和森林）。它包含同步的RGB、长波热、GPS和IMU数据，并提供10个常见自然场景类别的语义分割标注，支持热感知算法开发和相关基准测试（如语义分割、图像转换和SLAM）。