The GOOSE Dataset for Perception in Unstructured Environments
This provides a common framework for improving perception in autonomous robots operating in unstructured environments, though it is incremental as it builds on existing dataset standards.
The authors tackled the problem of limited data for training deep learning models in unstructured outdoor environments by introducing the GOOSE dataset, which includes 10,000 labeled image-point cloud pairs and supports state-of-the-art segmentation models.
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/.