Datasets:

MichalPelka
/

UndegroundParkingDataset

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'69967968780 -0.00985013 -0.00195826 -0.000385422 -0.419999 0.0335665 0.906133 0'}) and 1 missing columns ({'59957951180 -0.0141112 -0.00834984 0.000679843 -0.425614 0.0408907 0.903203 0'}).

This happened while the csv dataset builder was generating data using

hf://datasets/MichalPelka/UndegroundParkingDataset/mandeye/imu0001.csv (at revision 3b5976b93664d9d1a8bf55c8e418e7d21127a859), ['hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0000.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0001.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0002.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0003.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0004.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0005.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0006.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0007.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0008.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0009.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0010.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0011.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0012.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0013.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0014.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0015.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0016.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0017.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0018.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0019.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0020.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0021.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0022.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0023.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0024.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              69967968780 -0.00985013 -0.00195826 -0.000385422 -0.419999 0.0335665 0.906133 0: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 538
              to
              {'59957951180 -0.0141112 -0.00834984 0.000679843 -0.425614 0.0408907 0.903203 0': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'69967968780 -0.00985013 -0.00195826 -0.000385422 -0.419999 0.0335665 0.906133 0'}) and 1 missing columns ({'59957951180 -0.0141112 -0.00834984 0.000679843 -0.425614 0.0408907 0.903203 0'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/MichalPelka/UndegroundParkingDataset/mandeye/imu0001.csv (at revision 3b5976b93664d9d1a8bf55c8e418e7d21127a859), ['hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0000.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0001.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0002.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0003.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0004.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0005.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0006.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0007.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0008.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0009.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0010.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0011.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0012.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0013.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0014.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0015.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0016.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0017.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0018.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0019.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0020.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0021.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0022.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0023.csv', 'hf://datasets/MichalPelka/UndegroundParkingDataset@3b5976b93664d9d1a8bf55c8e418e7d21127a859/mandeye/imu0024.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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60437804060 -0.00558907 0.0140207 0.000679843 -0.412918 0.0213594 0.911504 0
60442107860 -0.00132801 0.015086 -0.000385422 -0.411454 0.0228243 0.89832 0
60447949740 0.00506357 0.0129554 0.00281037 -0.40828 0.0230684 0.895391 0
60452371360 0.0071941 0.0140207 0.0049409 -0.409012 0.0186739 0.899297 0
60457949070 0.0071941 0.00869439 0.0049409 -0.414627 0.0167208 0.912725 0

End of preview.

Underground Parking Dataset

Mobile-mapping recording of an underground parking garage in Skierniewice, Poland, captured with a Mandeye handheld LiDAR scanner, accompanied by a terrestrial laser scanner (TLS) reference point cloud that can be used as ground truth for evaluating SLAM / LiDAR odometry and point-cloud quality.

Top view of the ground-truth TLS point cloud (Z+F IMAGER 5010), shaded by intensity.

.
├── mandeye/                 # raw Mandeye recording (25 chunks, 0000–0024)
│   ├── lidarNNNN.laz        # LiDAR points of chunk NNNN (LAS 1.2, point format 1)
│   ├── lidarNNNN.sn         # LiDAR sensor serial number(s) used in the chunk
│   ├── imuNNNN.csv          # IMU measurements of chunk NNNN
│   └── gnssNNNN.gnss        # GNSS log of chunk NNNN (empty – no GNSS fix underground)
└── ground-truth/
    └── skierniewice_ground_truth_ZFIMAGER5010.laz   # TLS reference cloud

Part	Files	Size
`mandeye/`	25 × (`.laz`, `.sn`, `.csv`, `.gnss`)	~240 MB
`ground-truth/`	1 × `.laz` (~5.2 M points)	~37 MB

Mandeye recording

LiDAR – Livox sensor (serial 47MDL9T0020017). Each lidarNNNN.laz holds roughly 10–20 s of raw scans in the sensor frame, with per-point GPS time.
IMU – imuNNNN.csv, space-separated, one sample per line:

column meaning

1 timestamp [ns]

2–4 angular velocity x, y, z [rad/s]

5–7 linear acceleration x, y, z [g]

8 IMU id
GNSS – files are present for format compatibility but are empty, as there is no satellite reception underground.

column	meaning
1	timestamp [ns]
2–4	angular velocity x, y, z [rad/s]
5–7	linear acceleration x, y, z [g]
8	IMU id

The chunk layout is the standard Mandeye output and can be processed directly with HDMapping (e.g. lidar_odometry_step_1, followed by multi-view registration).

Ground truth

skierniewice_ground_truth_ZFIMAGER5010.laz is a static scan of the same garage acquired with a Z+F IMAGER 5010 terrestrial laser scanner (LAS 1.2, point format 2, ~5.2 M points; the RGB fields hold grayscale intensity). It is expressed in its own local frame, so a trajectory/map produced from the Mandeye data has to be registered to it (e.g. ICP) before computing cloud-to-cloud distances.

Usage

git lfs install
git clone https://huggingface.co/datasets/MichalPelka/UndegroundParkingDataset

or with the Hugging Face CLI:

huggingface-cli download MichalPelka/UndegroundParkingDataset --repo-type dataset --local-dir UndegroundParkingDataset

License

Released under the Apache 2.0 license.

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Total file size:

296 MB

Underground Parking Dataset

Contents

Mandeye recording

Ground truth

Usage

License