Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.18164v1</id>\n    <title>GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation</title>\n    <updated>2026-02-20T11:57:52Z</updated>\n    <link href='https://arxiv.org/abs/2602.18164v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.18164v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robot dataset captures the real-world conditions needed to develop and benchmark algorithms for legged-robot state estimation, perception, and navigation. To address this, we introduce the GrandTour dataset, a multi-modal legged-robotics dataset collected across challenging outdoor and indoor environments, featuring an ANYbotics ANYmal-D quadruped equipped with the \\boxi multi-modal sensor payload. GrandTour spans a broad range of environments and operational scenarios across distinct test sites, ranging from alpine scenery and forests to demolished buildings and urban areas, and covers a wide variation in scale, complexity, illumination, and weather conditions. The dataset provides time-synchronized sensor data from spinning LiDARs, multiple RGB cameras with complementary characteristics, proprioceptive sensors, and stereo depth cameras. Moreover, it includes high-precision ground-truth trajectories from satellite-based RTK-GNSS and a Leica Geosystems total station. This dataset supports research in SLAM, high-precision state estimation, and multi-modal learning, enabling rigorous evaluation and development of new approaches to sensor fusion in legged robotic systems. With its extensive scope, GrandTour represents the largest open-access legged-robotics dataset to date. The dataset is available at https://grand-tour.leggedrobotics.com, on HuggingFace (ROS-independent), and in ROS formats, along with tools and demo resources.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n    <published>2026-02-20T11:57:52Z</published>\n    <arxiv:comment>Jonas Frey and Turcan Tuna contributed equally. Submitted to Sage The International Journal of Robotics Research</arxiv:comment>\n    <arxiv:primary_category term='cs.RO'/>\n    <author>\n      <name>Jonas Frey</name>\n    </author>\n    <author>\n      <name>Turcan Tuna</name>\n    </author>\n    <author>\n      <name>Frank Fu</name>\n    </author>\n    <author>\n      <name>Katharine Patterson</name>\n    </author>\n    <author>\n      <name>Tianao Xu</name>\n    </author>\n    <author>\n      <name>Maurice Fallon</name>\n    </author>\n    <author>\n      <name>Cesar Cadena</name>\n    </author>\n    <author>\n      <name>Marco Hutter</name>\n    </author>\n  </entry>"
}