EgoWalk：一种用于野外机器人导航的多模态数据集
EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
AI总结：
本文提出EgoWalk数据集，包含50小时人类在多样化环境中的导航数据，提供多模态数据及自动化子集生成流程，展示其在机器人导航中的应用价值。
摘要：
数据驱动的导航算法在成功训练和在现实和非受控条件下实现鲁棒性能方面严重依赖于大规模、高质量的现实世界数据收集。为了增强导航相关现实世界数据集日益增长的家庭，我们引入EgoWalk - 一个包含50小时人类在多样化的室内外、不同季节和地点环境中的导航数据集。除了原始和模仿学习准备的数据外，我们还介绍了几个管道，以自动创建其他导航相关任务的子数据集，即自然语言目标注释和可通行性分割掩码。提供了多样性研究、用例和所提数据集的基准，以展示其实际应用价值。我们公开发布所有数据处理管道和用于数据收集的硬件平台的描述，以支持机器人导航系统未来的研究和开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
