FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909313100939264 |
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| author | Dhrafani, Devansh Liu, Yifei Jong, Andrew Shin, Ukcheol He, Yao Harp, Tyler Hu, Yaoyu Oh, Jean Scherer, Sebastian |
| author_facet | Dhrafani, Devansh Liu, Yifei Jong, Andrew Shin, Ukcheol He, Yao Harp, Tyler Hu, Yaoyu Oh, Jean Scherer, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07715 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments Dhrafani, Devansh Liu, Yifei Jong, Andrew Shin, Ukcheol He, Yao Harp, Tyler Hu, Yaoyu Oh, Jean Scherer, Sebastian Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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. |
| title | FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2409.07715 |