EgoWalk提供50小时多模态真实场景导航数据，助力机器人在复杂环境中学习行走。
EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
- 采集50小时人类在室内外、不同季节的自然行走数据，覆盖多样环境。
- 自动生成自然语言目标标注与可通行性分割掩码，支持多种导航任务。
- 开源数据处理流程与硬件平台信息，适合研究真实世界机器人导航。
数据驱动的导航算法依赖大规模、高质量的真实世界数据来实现鲁棒训练与实际表现。为丰富现有导航类真实数据集，我们推出EgoWalk——一个包含50小时人类在多样化室内/室外、不同季节及地理位置环境中自然行走的数据集。除原始数据和可用于模仿学习的数据外，我们还提供了多个自动构建子数据集的流程，涵盖自然语言目标注释和可通行性分割掩码。通过多样性分析、应用场景与基准测试，展示了该数据集的实际应用价值。我们公开发布所有数据处理流程及数据采集所用硬件平台说明，以支持未来机器人导航系统的研究与开发。
原文摘要 · Abstract (English)
Data-driven navigation algorithms are critically dependent on large-scale, high-quality real-world data collection for successful training and robust performance in realistic and uncontrolled conditions. To enhance the growing family of navigation-related real-world datasets, we introduce EgoWalk - a dataset of 50 hours of human navigation in a diverse set of indoor/outdoor, varied seasons, and location environments. Along with the raw and Imitation Learning-ready data, we introduce several pipelines to automatically create subsidiary datasets for other navigation-related tasks, namely natural language goal annotations and traversability segmentation masks. Diversity studies, use cases, and benchmarks for the proposed dataset are provided to demonstrate its practical applicability. We openly release all data processing pipelines and the description of the hardware platform used for data collection to support future research and development in robot navigation systems.
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