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论文arXiv 2602.1816427 天前

GrandTour：用于多模态感知与状态估计的野外腿式机器人数据集 ​

GrandTour解决缺少大规模公开真实腿式机器人数据集的问题，通过ANYmal-D多传感器采集，覆盖复杂室内外环境。

AI 整理
  • 本体 SDK 与控制
  • 感知与导航
  • 数据与采集

原文链接：GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation（arXiv 2602.18164 · 发布日期：2026-10-01）

GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation

GrandTour解决缺少大规模公开真实腿式机器人数据集的问题，通过ANYmal-D多传感器采集，覆盖复杂室内外环境。

核心要点 ​

  • 动机是缺少覆盖真实条件的大规模公开腿式机器人数据集，用于开发和基准测试算法。
  • GrandTour由ANYmal-D搭载Boxi多模态传感器，在多类室内外测试场地采集。
  • 数据含同步LiDAR、RGB相机、本体感知、深度与RGB-D相机，并提供高精度轨迹真值。
  • 数据集面向SLAM、状态估计和多模态学习，已开放网站、HuggingFace、ROS格式及工具。

验证范围与局限：验证范围为一台ANYbotics ANYmal-D四足机器人在多个具挑战性的室内外测试场地采集的数据。

英文摘要 (Abstract) ​

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, 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, six ANYmal-mounted Intel RealSense D435i depth cameras, and the ZED2i stereo RGB-D camera. 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.

作者：Turcan Tuna, Jonas Frey, Frank Fu, Katharine Patterson, Tian’ao Xu, Maurice F. Fallon, César Cadena, Marco Hutter

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2602.18164：GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation访问 2026-10-21