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データセットarXiv:2501.19274

GO: 大規模屋外マルチモーダルデータセット

GO: The Great Outdoors Multimodal Dataset

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非構造化環境での地上ロボット研究向けに、熱・レーダーを含む6種類のセンサと意味注釈・GPS軌跡を備えた大規模オフロードマルチモーダルデータセットを構築した。

著者: Peng Jiang, Kasi Viswanath, Akhil Nagariya, George Chustz, Maggie Wigness, Philip Osteen, Timothy Overbye, Christian Ellis, Long Quang, Jia Huang, Srikanth Saripalli

分類: cs.RO

原文アブストラクト

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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