# GrandTour：用于多模态感知与状态估计的野外腿式机器人数据集

> 原文链接：https://robotics.hellobike.com/papers/2026-10-01-2602-18164
> 频道：papers | 更新日期：2026-10-01 | 标签：arxiv, cs.RO
> 核心摘要：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。

# GrandTour：用于多模态感知与状态估计的野外腿式机器人数据集

::: v-pre
> 原文链接：[GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation](https://arxiv.org/abs/2602.18164)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2602.18164：GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation](https://arxiv.org/abs/2602.18164)