Voxel51/GrandTour-Sample

GrandTour sample → FiftyOne (Native Multimodal MCAP)

3 missions from GrandTour, the legged robotics dataset from the Robotic Systems Lab at ETH Zurich, converted to native multimodal MCAP episodes.

An ANYmal D quadruped carries the Boxi sensor payload through cities, buildings, forests, mountains and ice: HDR and depth cameras, a Hesai LiDAR, a tactical-grade inertial unit, a GNSS/INS receiver and the robot's own joint sensing, with a total station tracking a prism on the payload for reference. The full release holds 49 missions; this sample carries 3, chosen to contrast: ALB-3, Albisgütli - Forest 3; ETH-2, ETH - Mainbuilding Indoor; SNOW-1, Jungfraujoch - Snow 1.

13m 33s of walking, 24,390 camera frames, 12,701 depth frames and 8,363 LiDAR scans holding 433.5 million points.

Installation

pip install fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "Voxel51/GrandTour-Sample",
    name="GrandTour-Sample",
    persistent=True,
)
fo.launch_app(dataset)

The longest walk:

view = dataset.sort_by("lidar_odometry_path_m", reverse=True)
fo.launch_app(dataset, view=view)

What you get

Each episode contains:

• /hdr-front, /hdr-left and /hdr-right, the three HDR cameras at 1920x1280, as foxglove.CompressedImage (JPEG)
• /depth-front, the upper front depth camera at 848x480, as foxglove.CompressedImage (16-bit PNG, millimetres)
• a -calibration topic beside each camera, as foxglove.CameraCalibration
• /lidar-points, the motion-compensated Hesai scans, as foxglove.PointCloud with x, y, z, intensity, ring and time_offset
• /imu.plot, the STIM320 inertial unit
• /joint-positions.plot, /joint-velocities.plot and /joint-torques.plot, the twelve leg joints
• /legged-odometry, /lidar-odometry and /gnss-ins-odometry, the robot's own estimate, the LiDAR-inertial estimate and the GNSS/INS solution, as foxglove.PoseInFrame, each with its position on a .plot channel
• /gnss, the GNSS/INS fixes, as foxglove.LocationFix
• /prism.plot, the prism position the total station measured
• /command.plot, the operator's velocity command
• /battery.plot, the robot's battery
• /tf, the static transforms of the robot and its payload, each sensor's pose in its parent frame, as foxglove.FrameTransform
• /mission, naming the mission

Mission	Where	Duration	Camera frames	Depth frames	LiDAR scans	GNSS fixes	Path
ALB-3	Uetliberg, Albisguetli	3m 33s	6,390	3,374	2,217	44,601	78 m
ETH-2	ETH Zurich Main Building	7m 02s	12,678	6,508	4,306	0	211 m
SNOW-1	Jungfraujoch - Moenchsjoch Fenced	2m 57s	5,322	2,819	1,840	37,201	36 m

Episodes carry the fields mission, name, location, description, labels, split, gnss, recorded, duration, num_camera_frames, num_depth_frames, num_lidar_scans, num_lidar_points, num_imu_samples, num_gnss_fixes, num_prism_positions, lidar_odometry_path_m and release_notes, taken from the release's own mission index where it describes them.

Notes on the conversion

The release ships each mission as one zarr group per sensor topic, tarred, with each camera's frames in a tar of their own. The camera frames are carried as the release ships them, JPEG for colour and 16-bit PNG for depth, on the timestamps the zarr groups record. Every camera frame the release indexes is carried, and every LiDAR scan that holds points.

The release stores each static transform as the map from its base frame into the sensor's frame. /tf carries the inverse, each sensor's pose in its parent frame, which is what foxglove.FrameTransform describes. The upper front depth camera is mounted upside down, so its frames arrive turned half a turn, as its transform records.

The LiDAR scans are the release's motion-compensated ones, with the padding of its fixed-size arrays dropped. The per-point time is published as time_offset, in seconds from the scan's own stamp, since the release records an absolute time that a 32-bit float would quantize into steps of minutes.

This sample carries the three HDR cameras and the upper front depth camera. The release's five Alphasense cameras, its ZED 2i stereo pair and depth, its five other depth cameras, its Livox and Velodyne LiDARs, its other inertial units, the real-time GNSS solution, the raw GNSS observations, the point cloud maps and the Gaussian splats are not reproduced, and are available from the release itself.

License & attribution

The source release declares the MIT License on its Hugging Face card without naming a copyright holder. This conversion is distributed under the same license, with the work credited to the GrandTour authors at the Robotic Systems Lab, ETH Zurich:

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

@article{frey2026grandtour,
  title={GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation},
  author={Frey, Jonas and Tuna, Turcan and Fu, Frank and Patterson, Katharine and Xu, Tianao and Fallon, Maurice and Cadena, Cesar and Hutter, Marco},
  journal={arXiv preprint arXiv:2602.18164},
  year={2026}
}

@INPROCEEDINGS{Tuna-Frey-Fu-RSS-25,
    AUTHOR    = {Jonas Frey AND Turcan Tuna AND Lanke Frank Tarimo Fu AND Cedric Weibel AND Katharine Patterson AND Benjamin Krummenacher AND Matthias Müller AND Julian Nubert AND Maurice Fallon AND Cesar Cadena AND Marco Hutter},
    TITLE     = {{Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics}},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2025},
    ADDRESS   = {Los Angeles, United States},
    MONTH     = {June}
}

Changes from the source: 3 of the release's missions, converted from its zarr and image archives to the FiftyOne MCAP flavor, with the LiDAR padding dropped, the per-point time rebased to each scan, the release's camera intrinsics carried as calibration streams, and its static transforms inverted into each sensor's pose in its parent frame.