EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild

TL;DR

EgoWalk is a comprehensive multimodal dataset of 50 hours of human navigation data across diverse environments, supporting research in robot navigation with various auxiliary data and benchmarks.

Contribution

The paper introduces EgoWalk, a large-scale, diverse dataset with automatic annotations and processing pipelines, advancing data-driven robot navigation research.

Findings

Dataset covers indoor and outdoor environments across seasons.

Includes natural language goal annotations and traversability segmentation masks.

Provides benchmarks demonstrating practical applicability.

Abstract

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.