GO: The Great Outdoors Multimodal Dataset

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Authors: Peng Jiang, Kasi Viswanath, Akhil Nagariya, George Chustz, Maggie Wigness, Philip R. Osteen, Timothy Overbye, Christian Ellis, Long Quang, Jia Huang, Srikanth Saripalli
Cited by: 0
References: 25

Abstract

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/

Fields of Study & Subjects

Digital Storytelling and Education Digital Marketing and Social Media Mobile and Web Applications

Publication details

Journal / source
2026 IEEE Intelligent Vehicles Symposium (IV)
Publisher
IEEE
Pages
1496–1502
Publication date
1 June 2026
Language
EN