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

arXiv: https://arxiv.org/abs/2602.18164 · HTML: https://mlsanity.com/paper/2602.18164/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2026.

## Built on by (most cited first, 8 of 8)

- [Green for Go, Red for No: Visual Grounding via Semantic Segmentation for VLA Navigation Policies](https://mlsanity.com/paper/2607.05122/) (2026)
- [Multimodal embodiment-aware navigation transformer](https://mlsanity.com/paper/2604.19267/) (2026)
- [Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots](https://mlsanity.com/paper/2606.19067/) (2026)
- [PROBE-X: Learning-Free Cross-FOV Place Recognition](https://mlsanity.com/paper/2607.06782/) (2026)
- [CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts](https://mlsanity.com/paper/2607.06824/) (2026)
- [Vernata: Self-Supervised Learning of LiDAR Point Representations](https://mlsanity.com/paper/2608.06919/) (2026)
- [DRS-VPT: Directly Relocalizing in a Scan with Vision Point Transformers](https://mlsanity.com/paper/2609.12557/) (2026)
- [M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis](https://mlsanity.com/paper/2609.30056/) (2026)

## Abstract (arXiv)

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.