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.
@article{arxiv.2505.21282,
title = {EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild},
author = {Timur Akhtyamov and Mohamad Al Mdfaa and Javier Antonio Ramirez Benavides and Arthur Nigmatzyanov and Sergey Bakulin and German Devchich and Denis Fatykhov and Diego Ruiz Salinas and Alexander Mazurov and Kristina Zipa and Malik Mohrat and Pavel Kolesnik and Ivan Sosin and Gonzalo Ferrer},
journal= {arXiv preprint arXiv:2505.21282},
year = {2026}
}
Comments
This work has been submitted to the IEEE for possible publication