The successful deployment of deep learning-based techniques for autonomous systems is highly dependent on the data availability for the respective system in its deployment environment. Especially for unstructured outdoor environments, very few datasets exist for even fewer robotic platforms and scenarios. In an earlier work, we presented the German Outdoor and Offroad Dataset (GOOSE) framework along with 10000 multimodal frames from an offroad vehicle to enhance the perception capabilities in unstructured environments. In this work, we address the generalizability of the GOOSE framework. To accomplish this, we open-source the GOOSE-Ex dataset, which contains additional 5000 labeled multimodal frames from various completely different environments, recorded on a robotic excavator and a quadruped platform. We perform a comprehensive analysis of the semantic segmentation performance on different platforms and sensor modalities in unseen environments. In addition, we demonstrate how the combined datasets can be utilized for different downstream applications or competitions such as offroad navigation, object manipulation or scene completion. The dataset, its platform documentation and pre-trained state-of-the-art models for offroad perception will be made available on https://goose-dataset.de/. \

翻译：基于深度学习的自主系统技术能否成功部署，高度依赖于其在部署环境中相应系统的数据可用性。特别是在非结构化户外环境中，现有的数据集非常稀少，且覆盖的机器人平台和场景极为有限。在先前的工作中，我们提出了德国户外与越野数据集（GOOSE）框架，并发布了一个越野车辆采集的10000帧多模态数据，以增强在非结构化环境中的感知能力。在本工作中，我们致力于提升GOOSE框架的泛化能力。为此，我们开源了GOOSE-Ex数据集，该数据集包含了额外5000帧已标注的多模态数据，这些数据采集自多种完全不同的环境，记录平台包括一台机器人挖掘机和一台四足机器人平台。我们对不同平台和传感器模态在未见环境中的语义分割性能进行了全面分析。此外，我们还展示了如何将组合数据集用于不同的下游应用或竞赛，例如越野导航、物体操作或场景补全。该数据集、其平台文档以及用于越野感知的预训练最先进模型将在 https://goose-dataset.de/ 上提供。