GO: The Great Outdoors Multimodal Dataset

TL;DR

The GO dataset is a comprehensive multi-modal resource designed to enhance ground robotics research in unstructured natural environments, supporting tasks like semantic segmentation, object detection, and SLAM.

Contribution

It introduces the most extensive multi-modal dataset with diverse environmental conditions and high-quality annotations, surpassing existing off-road datasets.

Findings

Supports advanced perception tasks in natural environments

Enables development of more robust field robotics solutions

Provides diverse real-world environmental data

Abstract

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality 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 that 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/