EAGLE: The First Event Camera Dataset Gathered by an Agile Quadruped Robot

When legged robots perform agile movements, traditional RGB cameras often produce blurred images, posing a challenge for accurate state estimation. Event cameras, inspired by biological vision mechanisms, have emerged as a promising solution for capturing high-speed movements and coping with challenging lighting conditions, owing to their significant advantages, such as low latency, high temporal resolution, and a high dynamic range. However, the integration of event cameras into agile-legged robots is still largely unexplored. Notably, no event camera-based dataset has yet been specifically developed for dynamic legged robots. To bridge this gap, we introduce EAGLE (Event dataset of an AGile LEgged robot), a new dataset comprising data from an event camera, an RGB-D camera, an IMU, a LiDAR, and joint angle encoders, all mounted on a quadruped robotic platform. This dataset features more than 100 sequences from real-world environments, encompassing various indoor and outdoor environments, different lighting conditions, a range of robot gaits (e.g., trotting, bounding, pronking), as well as acrobatic movements such as backflipping. To our knowledge, this is the first event camera dataset to include multi-sensory data collected by an agile quadruped robot.