构建多模态户外数据集，助力机器人在复杂自然环境中的感知与导航。
GO: The Great Outdoors Multimodal Dataset
- 融合六种传感器模态，包含热成像与雷达，覆盖恶劣天气场景。
- 提供语义标注与GPS轨迹，支持语义分割、目标检测与SLAM等任务。
- 适用于野外机器人、自主探索系统的研究，尤其关注鲁棒感知设计。
《Great Outdoors》（GO）数据集是一个多模态标注数据资源，旨在推动非结构化环境中的地面机器人研究。现有越野数据集普遍缺乏传感器多样性，且缺少热成像和雷达等关键模态，难以支撑低能见度或恶劣天气下的运行需求。为此，我们构建了一个大规模多模态越野数据集，包含六种互补的传感器模态，以及语义标注和GPS轨迹，可支持语义分割、目标检测和SLAM等任务。数据集涵盖多样化的环境条件，带来真实世界挑战，为发展更鲁棒的野外机器人、自主探索及自然环境感知系统提供支持。数据集可从 https://www.unmannedlab.org/the-great-outdoors-dataset/ 下载。
原文摘要 · Abstract (English)
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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