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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.21282 |
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| _version_ | 1866915943905492992 |
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| author | Akhtyamov, Timur Mdfaa, Mohamad Al Benavides, Javier Antonio Ramirez Nigmatzyanov, Arthur Bakulin, Sergey Devchich, German Fatykhov, Denis Salinas, Diego Ruiz Mazurov, Alexander Zipa, Kristina Mohrat, Malik Kolesnik, Pavel Sosin, Ivan Ferrer, Gonzalo |
| author_facet | Akhtyamov, Timur Mdfaa, Mohamad Al Benavides, Javier Antonio Ramirez Nigmatzyanov, Arthur Bakulin, Sergey Devchich, German Fatykhov, Denis Salinas, Diego Ruiz Mazurov, Alexander Zipa, Kristina Mohrat, Malik Kolesnik, Pavel Sosin, Ivan Ferrer, Gonzalo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild Akhtyamov, Timur Mdfaa, Mohamad Al Benavides, Javier Antonio Ramirez Nigmatzyanov, Arthur Bakulin, Sergey Devchich, German Fatykhov, Denis Salinas, Diego Ruiz Mazurov, Alexander Zipa, Kristina Mohrat, Malik Kolesnik, Pavel Sosin, Ivan Ferrer, Gonzalo Robotics 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. |
| title | EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.21282 |