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.

翻译：精确的状态估计与多模态感知是腿式机器人在复杂、大规模环境中实现自主运行的前提条件。迄今为止，尚无大规模公开的腿式机器人数据集能够捕捉开发和评测腿式机器人状态估计、感知与导航算法所需的真实世界条件。为此，我们推出GrandTour数据集——一个在具有挑战性的室外与室内环境中采集的多模态腿式机器人数据集，其搭载了Boxi多模态传感器载荷的ANYbotics ANYmal-D四足机器人。GrandTour涵盖了不同测试地点中广泛的环境与操作场景，范围从高山景观、森林到拆除的建筑物和城市区域，并在规模、复杂性、光照及天气条件上呈现出高度多样性。该数据集提供了来自旋转激光雷达、具有互补特性的多台RGB相机、本体感知传感器以及立体深度相机的时间同步传感器数据。此外，它还包含基于卫星的RTK-GNSS和徕卡测量系统全站仪提供的高精度真值轨迹。本数据集支持SLAM、高精度状态估计及多模态学习等领域的研究，能够为腿式机器人系统中的传感器融合新方法提供严格的评估与开发基础。凭借其广泛的范围，GrandTour代表了迄今为止规模最大的开源腿式机器人数据集。该数据集可通过HuggingFace（独立于ROS）以ROS格式获取，访问地址为 https://grand-tour.leggedrobotics.com，同时提供相关工具与演示资源。