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足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，尚未有大规模公开的腿足机器人数据集能够捕捉真实世界条件，以支撑腿足机器人状态估计、感知与导航算法的开发与基准测试。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足机器人数据集。该数据集采用搭载\boxi多模态传感器载荷的ANYbotics ANYmal-D四足机器人进行采集。GrandTour覆盖了不同测试场地的广泛环境与作业场景，从高山景观、森林到拆除建筑与城市区域，并在规模、复杂度、光照及天气条件上表现出高度多样性。数据集提供时间同步的传感器数据，包括旋转式激光雷达、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。此外，数据集还包含基于卫星的RTK-GNSS与Leica Geosystems全站仪提供的高精度真实轨迹。本数据集支持同步定位与建图（SLAM）、高精度状态估计及多模态学习等领域的研究，为腿足机器人系统中的传感器融合新方法提供了严格的评估与开发基础。凭借其广泛覆盖范围，GrandTour代表了当前最大的开放获取腿足机器人数据集。数据集可通过https://grand-tour.leggedrobotics.com获取，并在HuggingFace平台提供独立于ROS的格式及ROS格式，同时附有工具与演示资源。