EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
For researchers in robot navigation, this dataset provides a large-scale, diverse real-world resource to train and benchmark navigation models, though it is an incremental contribution to existing navigation datasets.
The paper introduces EgoWalk, a 50-hour multimodal dataset of human navigation in diverse indoor/outdoor environments across seasons, along with pipelines for generating natural language goal annotations and traversability masks, to support data-driven robot navigation algorithms.
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.