The GOOSE Dataset for Perception in Unstructured Environments
Mortimer, Peter and Hagmanns, Raphael and Granero, Miguel and Luettel, Thorsten and Petereit, Janko and Wuensche, Hans-Joachim

@article{,
title= {The GOOSE Dataset for Perception in Unstructured Environments},
journal= {arXiv preprint arXiv:2310.16788},
author= {Mortimer, Peter and Hagmanns, Raphael and Granero, Miguel and Luettel, Thorsten and Petereit, Janko and Wuensche, Hans-Joachim},
year= {2023},
url= {https://goose-dataset.de/},
abstract= {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 10000 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. 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/.},
keywords= {training, semantic segmentation, traversability, navigation, point cloud, robot navigation, 3D point cloud, autonomous systems, calibration target, camera images, classification datasets, color camera, high grass, image segmentation, LiDaR point clouds, LiDaR scans, neural architecture search, ontologies, point cloud compression, point cloud data, point cloud segmentation, raw sensor data, RGB images, robot sensing systems, robotic platform, segmentation model, semantic segmentation models, sensor data, synchronization, terrain, test split, types of obstacles, unstructured environments},
terms= {},
license= {CC BY-SA: https://creativecommons.org/licenses/by-sa/4.0/},
superseded= {}
}