The GOOSE Dataset for Perception in Unstructured Environments

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 incor- porates 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 unstruc- tured environments. This framework also makes it possible to query data for specific weather conditions or sensor setups from a database in future. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://goose-dataset.de/. This work was supported by the Federal Office of Bundeswehr Equip- ment, Information Technology and In-Service Support (BAAINBw). 1 The authors are with the Institute for Autonomous Systems Technology, University of the Bundeswehr Munich, Germany firstname.lastname@unibw.de 2 The authors are with the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany firstname.lastname@iosb.fraunhofer.de 3 The author is with the Karlsruhe Institute of Technology (KIT), Germany firstname.lastname@kit.edu