  The potential for deploying autonomous systems can be significantly increased
by improving the perception and interpretation of the environment. However, the
development of deep learning-based techniques for autonomous systems in
unstructured outdoor environments poses challenges due to limited data
availability for training and testing. To address this gap, we present the
German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset
specifically designed for unstructured outdoor environments. The GOOSE dataset
incorporates 10 000 labeled pairs of images and point clouds, which are
utilized to train a range of state-of-the-art segmentation models on both image
and point cloud data. We open source the dataset, along with an ontology for
unstructured terrain, as well as dataset standards and guidelines. This
initiative aims to establish a common framework, enabling the seamless
inclusion of existing datasets and a fast way to enhance the perception
capabilities of various robots operating in unstructured environments. The
dataset, pre-trained models for offroad perception, and additional
documentation can be found at https://goose-dataset.de/.
