构建首个用于无人机在恶劣环境深度感知的红外双目数据集
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
- 采集城市与森林场景下的红外双目图像、激光雷达等多模态数据
- 在烟雾、雨天等条件下验证模型，表现优于传统可见光方法
- 适合灾害救援、无人机自主导航等实际应用研究者使用
在视觉退化环境中实现鲁棒的深度感知对自主飞行系统至关重要。热成像相机通过捕捉红外辐射，具备抗视觉退化能力。然而由于缺乏大规模数据集，热成像在无人航空系统（UAS）深度感知中的应用仍鲜有探索。本文提出一个面向自主空中感知应用的立体热成像深度感知数据集。该数据集包含城市与森林场景下昼夜、雨天、烟雾等不同条件的立体热成像图、激光雷达（LiDAR）、惯性测量单元（IMU）及真实深度图。我们对代表性立体深度估计算法进行了基准测试，结果表明基于本数据集训练的模型能有效泛化至未见过的烟雾场景，凸显了立体热成像在深度感知中的鲁棒性。本工作旨在提升灾后环境下机器人感知能力，实现此前无法到达区域的探索与作业。数据集与源代码已公开于 https://firestereo.github.io。
原文摘要 · Abstract (English)
Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has remained largely unexplored. This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications. The dataset consists of stereo thermal images, LiDAR, IMU and ground truth depth maps captured in urban and forest settings under diverse conditions like day, night, rain, and smoke. We benchmark representative stereo depth estimation algorithms, offering insights into their performance in degraded conditions. Models trained on our dataset generalize well to unseen smoky conditions, highlighting the robustness of stereo thermal imaging for depth perception. We aim for this work to enhance robotic perception in disaster scenarios, allowing for exploration and operations in previously unreachable areas. The dataset and source code are available at https://firestereo.github.io.
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