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}
}

### 概述
本数据集是首个专为自然环境中作业的空中现场机器人研发的公开可用RGB-热成像（RGB-thermal）数据集。其覆盖美国本土的多样地形，涵盖河流、湖泊、海岸线、沙漠与森林。数据集同步采集RGB图像、长波热成像（long-wave thermal）数据、全球定位系统（GPS）数据以及惯性测量单元（IMU）数据。此外，数据集还提供了自然场景中常见的10个类别的语义分割标注，以助力热感知算法的研发。
### 基准测试任务
本数据集为以下任务提供了基准测试方案：热成像语义分割、RGB-热成像语义分割、RGB到热成像图像转换，以及视觉惯性里程计（visual-inertial odometry, VIO）/同时定位与地图构建（simultaneous Localization and Mapping, SLAM）。
### 代码
请使用配套的代码与脚本处理数据集：https://github.com/aerorobotics/caltech-aerial-rgbt-dataset
### 引用说明
若本数据集对你的研究有所助益，请引用如下论文：
@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）。