GO: The Great Outdoors Multimodal Dataset

2025-01-31

AI Summary

To address degraded robotic perception performance in unstructured野外 environments, this work introduces the first multimodal robot perception dataset tailored for natural scenes. The dataset features synchronized acquisition of six modalities—RGB, stereo infrared, LiDAR, IMU, GNSS, and wheel odometry—capturing extreme terrain, illumination, and vegetation conditions. It includes high-fidelity semantic segmentation masks, object detection bounding boxes, and centimeter-accurate GPS trajectories. We propose a geospatial alignment framework and a cross-modal temporal synchronization method, integrated with a semi-automatic annotation pipeline to ensure data authenticity and task difficulty. Experimental results demonstrate substantial improvements in downstream model robustness: semantic segmentation mIoU increases by 12.3%, and SLAM trajectory error decreases by 27%. This dataset establishes the most comprehensive benchmark to date for outdoor robotic perception research.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web 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/

Problem

• Robotics
• Natural Environment
• Dataset

Innovation

• Outdoor Robotics
• Diverse Sensor Data
• Object Recognition