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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.19274 |
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| _version_ | 1866916592477011968 |
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| author | Jiang, Peng Viswanath, Kasi Nagariya, Akhil Chustz, George Wigness, Maggie Osteen, Philip Overbye, Timothy Ellis, Christian Quang, Long Saripalli, Srikanth |
| author_facet | Jiang, Peng Viswanath, Kasi Nagariya, Akhil Chustz, George Wigness, Maggie Osteen, Philip Overbye, Timothy Ellis, Christian Quang, Long Saripalli, Srikanth |
| contents | The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality semantic annotations and GPS traces to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges that provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19274 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GO: The Great Outdoors Multimodal Dataset Jiang, Peng Viswanath, Kasi Nagariya, Akhil Chustz, George Wigness, Maggie Osteen, Philip Overbye, Timothy Ellis, Christian Quang, Long Saripalli, Srikanth Robotics The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality semantic annotations and GPS traces to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges that provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/ |
| title | GO: The Great Outdoors Multimodal Dataset |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.19274 |