GO: The Great Outdoors Multimodal Dataset
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Abstract (arXiv)
The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. Existing off-road datasets often lack sensor diversity and exclude vital modalities like thermal and radar that are critical for operation in degraded conditions (e.g., low visibility or adverse weather). To address these gaps, we introduce a large-scale multimodal off-road dataset with six complementary sensor modalities, along with 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, which 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/
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2025.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025UAVScenes: A Multi-Modal Dataset for UAVscited by 26
- 2026The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environmentscited by 1
- 2026FLINT: Fast Lightweight Inference for Traversabilitycited by 0
- 2026ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenescited by 0
- 2026ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes29 Sep 2026
- 2026FLINT: Fast Lightweight Inference for Traversability22 Sep 2026
- 2026The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments27 Jan 2026
- 2025UAVScenes: A Multi-Modal Dataset for UAVs30 Jul 2025