GO: The Great Outdoors Multimodal Dataset
This dataset benefits researchers in field robotics and autonomous exploration by offering a more comprehensive resource for developing robust perception systems in degraded conditions.
The GO dataset provides a large-scale multimodal off-road dataset with six sensor modalities and semantic annotations to advance ground robotics in unstructured environments, addressing gaps in existing datasets that lack sensor diversity and modalities like thermal and radar.
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/