GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

TL;DR

GrandTour is the largest open-access multi-modal dataset for legged robots, capturing diverse real-world environments to advance perception, state estimation, and navigation algorithms.

Contribution

It provides a comprehensive, multi-modal dataset from outdoor and indoor environments, enabling improved benchmarking and development of perception and localization methods for legged robots.

Findings

Supports research in SLAM, state estimation, and sensor fusion.

Includes synchronized multi-modal sensor data and high-precision ground-truth trajectories.

Enables rigorous evaluation of perception and navigation algorithms.

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, 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.