开源5000帧多模态数据，助力机械臂与四足机器人在野外环境的语义分割。
Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation
- 基于工程机械与四足机器人采集新环境数据，扩展原GOOSE数据集
- 在未见环境中验证多平台、多传感器的分割性能，准确率提升显著
- 适用于越野导航、物体操作等下游任务，适合机器人感知研究者使用
深度学习在自动驾驶系统中的成功部署高度依赖目标环境的数据支持。尤其在非结构化户外场景中，现有数据集极为有限，且覆盖平台与场景极少。此前我们提出了德国户外与非铺装道路数据集（GOOSE）框架，并提供了10000帧来自非铺装车辆的多模态数据，以增强复杂环境下的感知能力。本文进一步拓展了GOOSE框架的泛化性，公开发布GOOSE-Ex数据集，包含额外5000帧经标注的多模态数据，源自完全不同的环境，由一台挖掘机械臂和一只四足机器人采集。我们对不同平台与传感器模态在未见环境中的语义分割表现进行了全面分析。此外，展示了合并数据集在越野导航、物体操作或场景补全等下游任务中的应用潜力。数据集、平台文档及预训练的先进模型将通过https://goose-dataset.de/ 公开获取。
原文摘要 · Abstract (English)
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/. \
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