The Great Outdoors Dataset: Off-Road Multi-Modal Dataset

Texas A&M University    The DEVCOM Army Research Laboratory

The Great Outdoors Dataset: Off-Road Multi-Modal Dataset is a comprehensive resource aimed at advancing autonomous navigation research in challenging off-road environments. Collected using an unmanned ground vehicle (UGV) designed for unstructured terrain, this dataset offers a rich combination of sensor data to support robust and safe navigation. The sensor setup includes a 64-channel LiDAR for detailed 3D point cloud generation, multiple RGB cameras for high-resolution visual capture, and a thermal camera for infrared imaging in low-visibility or night-time conditions. In addition, the dataset features data from an inertial navigation system (INS) that provides accurate motion and orientation measurements, a 2D mmWave radar for enhanced perception in adverse weather conditions, and an RTK GPS system for precise geolocation. The Great Outdoors Dataset places a strong emphasis on semantic scene understanding, addressing the gap in off-road autonomy research by offering multimodal data with annotated labels for 3D semantic segmentation. Unlike many existing datasets that focus on urban environments, this dataset is specifically tailored for off-road applications, providing a crucial resource for the development of advanced machine learning models and sensor fusion techniques. By building on the foundation of RELLIS-3D, it is designed to push the boundaries of autonomous navigation in unstructured environments, enabling the development of algorithms that can effectively navigate and perceive the complex dynamics of off-road settings.

LiDAR Scans Statics

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