Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 133, in _split_generators
analyze(archives, downloaded_dirs, split_name)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 107, in analyze
for downloaded_dir_file in dl_manager.iter_files(downloaded_dir):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1350, in _iter_from_urlpaths
if xisfile(urlpath, download_config=download_config):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 733, in xisfile
fs, *_ = url_to_fs(path, **storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 395, in url_to_fs
fs = filesystem(protocol, **inkwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 293, in filesystem
return cls(**storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 80, in __call__
obj = super().__call__(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 62, in __init__
self.zip = zipfile.ZipFile(
File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
return _EndRecData64(fpin, -sizeEndCentDir, endrec)
File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supported
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📄 arXiv – 🎥 video – ⬇️ get – 📊 results – 💬 contact
Monado SLAM Datasets
The Monado SLAM datasets (MSD), are challenging egocentric visual-inertial (VI) datasets recorded from VR headsets to improve state-of-the-art VI-SLAM systems. They were originally developed to improve the open-source inside-out tracking component of the Monado project but its applicability is not limited to the XR field. The dataset has a permissive license CC-BY 4.0, meaning you can use them for any purpose you want, and only a mention of the original project is required. The creation of the dataset was supported by Collabora. Further work for publication was done in the Computer Vision Group from the Technical University of Munich.
Monado is an open-source OpenXR runtime that you can use to make devices OpenXR compatible. It also provides drivers for different existing hardware thanks to contributors in the community creating drivers for it. Monado provides useful modules that these drivers can use, including an inside-out head tracking component. While it allows to use different tracking systems, the preferred system is a fork of Basalt. Creating a good tracking solution requires a solid measurement pipeline to understand how changes in the system affect tracking quality. This dataset is published to support this purpose, and in the process, highlights problems with state-of-the-art real-time VI tracking systems that extend beyond the XR use case, especially in humanoid robotics.
For questions or comments, the preferred way is to use the Hugging Face
community discussions
tab. You might also join Monado's Discord server
and ask in the #slam channel.
For additional documentation see:
- Valve Index documentation
- HP Reverb G2 documentation
- Samsung Odyssey+ documentation
- Post-processing walkthrough video
Results
To replicate the results from the paper "The Monado SLAM Dataset for Egocentric Visual-Inertial Tracking", you can download all the relevant runs, evaluations, and plotting scripts from here.
Sequences
Categories
- MI_valve_index: Valve Index datasets (2x960x960 @54fps, IMU@1000Hz)
- MIC_calibration: Recordings of target with 3cm sides for camera and IMU calibration.
- MIO_others: Miscelaneous datasets including specific challenges.
- MIP_playing: Datasets in which the user is playing a game.
- MIPB_beat_saber: Music game in which the player hits song notes with two swords.
- MIPP_pistol_whip: Music game in which the player uses a pistol to target enemies in sync with the song rythm.
- MIPT_thrill_of_the_fight: Boxing simulator.
- MG_reverb_g2: (4x640x480 @30fps, IMU@1000Hz)
- MGC_calibration: Same as
MIC, includes pairwise recordings for each camera pair and magnetometer calibrations. - MGO_others: Miscelaneous recordings with different challenges.
- MGC_calibration: Same as
- MO_odyssey_plus: (2x640x480 @30fps, IMU@1000Hz)
- MOC_calibration: Same as previous calibration datasets. Includes a long MOC13 dataset for measuring IMU allan variance.
- MOO_others: Miscelaneous recordings with different challenges.
Table of sequences
You can preview and download individual sequences from these tables. If you want
to get the full dataset, you can use the hf
CLI
or clone with git lfs. Calibration information can be found in the extras
directory of each (e.g., for the
Index)
Table of calibration sequences
Click to show calibration sequences
Contact
If you have any additional question, you can reach out by:
- Opening a new discussion thread in hugging face.
- Join Monado's discord server and tag me (
@mateosss) in the #SLAM channel for more flexible interaction. - Contact me directly at [email protected].
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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