GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation
GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation
The GrandTour dataset addresses the lack of large-scale, real-world legged-robot data by providing time-synchronized sensor data from multiple modalities, including LiDAR, RGB cameras, depth sensors, and GNSS. It supports research in SLAM, state estimation, and multi-modal learning, with data available in ROS and HuggingFace formats.
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The GrandTour dataset provides a comprehensive, multi-modal collection of sensor data from a quadruped robot in diverse real-world environments, supporting research in state estimation, SLAM, and sensor fusion. It is the largest open-access legged-robotics dataset to date, available in multiple formats and platforms.
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Dataset licensing and commercial-use rights are not established here.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
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Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments.
Open source E1
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Source：International Journal of Robotics Research — Journal metadata · doi.org