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arxiv: 2501.19274 · v3 · pith:QE572NRAnew · submitted 2025-01-31 · 💻 cs.RO

GO: The Great Outdoors Multimodal Dataset

classification 💻 cs.RO
keywords datasetconditionsenvironmentsgreatmodalitiesmultimodaloff-roadoutdoors
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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/

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges

    cs.RO 2026-04 unverdicted novelty 4.0

    Two radar odometry baselines improve trajectory estimates on challenging off-road routes in the GO dataset.