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GO: The Great Outdoors Multimodal Dataset
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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/
Forward citations
Cited by 3 Pith papers
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Learning Traversability-Aware Global Planners for Long Horizon Off-Road Navigation
Overhead multi-modal learning with PU human-trajectory supervision and LiDAR priors yields global off-road costmaps that nearly match human path length and sharply cut interventions versus local planners.
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UAVScenes: A Multi-Modal Dataset for UAVs
UAVScenes adds frame-wise image and LiDAR semantic labels, reconstructed 6-DoF poses, and 3D maps to 120k frames of the MARS-LVIG dataset, with six benchmark tasks.
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Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
Two radar odometry baselines improve trajectory estimates on challenging off-road routes in the GO dataset.
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