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KevinWong216
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MirrorSentinel-Elevator

Modalities:
Image
3D
Tags:
robotics
slam
ros2
lidar
imu
image
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dataset: string
public_sequence_ids: list<item: string>
  child 0, item: string
required_modalities: list<item: string>
  child 0, item: string
required_topics: list<item: string>
  child 0, item: string
schema_version: int64
sequence_id_migration: struct<excluded_legacy_sequence: struct<database_bytes: int64, reason: string, sequence: string>, ma (... 182 chars omitted)
  child 0, excluded_legacy_sequence: struct<database_bytes: int64, reason: string, sequence: string>
      child 0, database_bytes: int64
      child 1, reason: string
      child 2, sequence: string
  child 1, mapping: struct<elevator_02: string, elevator_03: string, elevator_04: string, elevator_05: string, elevator_ (... 74 chars omitted)
      child 0, elevator_02: string
      child 1, elevator_03: string
      child 2, elevator_04: string
      child 3, elevator_05: string
      child 4, elevator_06: string
      child 5, elevator_07: string
      child 6, elevator_08: string
      child 7, elevator_09: string
sequences: struct<elevator_01: struct<id: string, legacy_id: string, ros1_source: struct<available: bool, bytes (... 6728 chars omitted)
  child 0, elevator_01: struct<id: string, legacy_id: string, ros1_source: struct<available: bool, bytes: int64, duration_ns (... 742 chars omitted)
      child 0, id: string
      child 1, legacy_id: string
      child 2, ros1_source: struct<available: bool, bytes: int64, duration_ns: int64, filename: string, message_count: int64, sh (... 13 chars omitted)
     
...
   child 3, sqlite_integrity_check: string
          child 1, duration_ns: int64
          child 2, end_ns: int64
          child 3, message_count: int64
          child 4, metadata_version: int64
          child 5, start_ns: int64
          child 6, topics: struct</ouster/imu|sensor_msgs/msg/Imu: int64, /ouster/points|sensor_msgs/msg/PointCloud2: int64, /t (... 208 chars omitted)
              child 0, /ouster/imu|sensor_msgs/msg/Imu: int64
              child 1, /ouster/points|sensor_msgs/msg/PointCloud2: int64
              child 2, /tf_static|tf2_msgs/msg/TFMessage: int64
              child 3, /tf|tf2_msgs/msg/TFMessage: int64
              child 4, /zed2/zed_node/left/camera_info|sensor_msgs/msg/CameraInfo: int64
              child 5, /zed2/zed_node/left/image_rect_color|sensor_msgs/msg/Image: int64
      child 4, validation: struct<has_imu: bool, has_lidar: bool, has_rgb: bool, ros1_ros2_exact_metadata_match: bool, ros2_hum (... 21 chars omitted)
          child 0, has_imu: bool
          child 1, has_lidar: bool
          child 2, has_rgb: bool
          child 3, ros1_ros2_exact_metadata_match: bool
          child 4, ros2_humble_bag_info: string
code_repository: string
database_install_modes: list<item: string>
  child 0, item: string
database_bytes: int64
release_date: timestamp[s]
sequence_count: int64
source_manifest_sha256: string
physical_footprint_count: int64
whitelist_only: bool
release_version: string
license: string
code_revision: string
hf_repo_id: string
to
{'code_repository': Value('string'), 'code_revision': Value('string'), 'database_bytes': Value('int64'), 'database_install_modes': List(Value('string')), 'dataset': Value('string'), 'hf_repo_id': Value('string'), 'license': Value('string'), 'physical_footprint_count': Value('int64'), 'release_date': Value('timestamp[s]'), 'release_version': Value('string'), 'schema_version': Value('int64'), 'sequence_count': Value('int64'), 'source_manifest_sha256': Value('string'), 'whitelist_only': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              dataset: string
              public_sequence_ids: list<item: string>
                child 0, item: string
              required_modalities: list<item: string>
                child 0, item: string
              required_topics: list<item: string>
                child 0, item: string
              schema_version: int64
              sequence_id_migration: struct<excluded_legacy_sequence: struct<database_bytes: int64, reason: string, sequence: string>, ma (... 182 chars omitted)
                child 0, excluded_legacy_sequence: struct<database_bytes: int64, reason: string, sequence: string>
                    child 0, database_bytes: int64
                    child 1, reason: string
                    child 2, sequence: string
                child 1, mapping: struct<elevator_02: string, elevator_03: string, elevator_04: string, elevator_05: string, elevator_ (... 74 chars omitted)
                    child 0, elevator_02: string
                    child 1, elevator_03: string
                    child 2, elevator_04: string
                    child 3, elevator_05: string
                    child 4, elevator_06: string
                    child 5, elevator_07: string
                    child 6, elevator_08: string
                    child 7, elevator_09: string
              sequences: struct<elevator_01: struct<id: string, legacy_id: string, ros1_source: struct<available: bool, bytes (... 6728 chars omitted)
                child 0, elevator_01: struct<id: string, legacy_id: string, ros1_source: struct<available: bool, bytes: int64, duration_ns (... 742 chars omitted)
                    child 0, id: string
                    child 1, legacy_id: string
                    child 2, ros1_source: struct<available: bool, bytes: int64, duration_ns: int64, filename: string, message_count: int64, sh (... 13 chars omitted)
                   
              ...
                 child 3, sqlite_integrity_check: string
                        child 1, duration_ns: int64
                        child 2, end_ns: int64
                        child 3, message_count: int64
                        child 4, metadata_version: int64
                        child 5, start_ns: int64
                        child 6, topics: struct</ouster/imu|sensor_msgs/msg/Imu: int64, /ouster/points|sensor_msgs/msg/PointCloud2: int64, /t (... 208 chars omitted)
                            child 0, /ouster/imu|sensor_msgs/msg/Imu: int64
                            child 1, /ouster/points|sensor_msgs/msg/PointCloud2: int64
                            child 2, /tf_static|tf2_msgs/msg/TFMessage: int64
                            child 3, /tf|tf2_msgs/msg/TFMessage: int64
                            child 4, /zed2/zed_node/left/camera_info|sensor_msgs/msg/CameraInfo: int64
                            child 5, /zed2/zed_node/left/image_rect_color|sensor_msgs/msg/Image: int64
                    child 4, validation: struct<has_imu: bool, has_lidar: bool, has_rgb: bool, ros1_ros2_exact_metadata_match: bool, ros2_hum (... 21 chars omitted)
                        child 0, has_imu: bool
                        child 1, has_lidar: bool
                        child 2, has_rgb: bool
                        child 3, ros1_ros2_exact_metadata_match: bool
                        child 4, ros2_humble_bag_info: string
              code_repository: string
              database_install_modes: list<item: string>
                child 0, item: string
              database_bytes: int64
              release_date: timestamp[s]
              sequence_count: int64
              source_manifest_sha256: string
              physical_footprint_count: int64
              whitelist_only: bool
              release_version: string
              license: string
              code_revision: string
              hf_repo_id: string
              to
              {'code_repository': Value('string'), 'code_revision': Value('string'), 'database_bytes': Value('int64'), 'database_install_modes': List(Value('string')), 'dataset': Value('string'), 'hf_repo_id': Value('string'), 'license': Value('string'), 'physical_footprint_count': Value('int64'), 'release_date': Value('timestamp[s]'), 'release_version': Value('string'), 'schema_version': Value('int64'), 'sequence_count': Value('int64'), 'source_manifest_sha256': Value('string'), 'whitelist_only': Value('bool')}
              because column names don't match

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MirrorSentinel Reflective Elevator Dataset

MirrorSentinel-Elevator is a synchronized RGB-LiDAR-IMU ROS 2 dataset for studying severe mirror and glass interference in compact elevator environments. It contains eight short traversals, E01-E08, collected across seven measured physical elevator footprints. E02 and E03 repeat the same physical cabin and are grouped as one footprint for inferential statistics.

The release contains the exact raw bags used by the accompanying paper. It does not contain generated maps, monocular-depth priors, reflection masks, learned-model weights, map-cleaning outputs, or a cabin ROI. The geometric annotations are evaluation-only and are never read by the mapping or cleaning pipeline.

Dataset Summary

Property Value
Sequences 8 synchronized ROS 2 bags
Physical footprints 7; E02 and E03 share one cabin
Total database size 30.61 GiB (32870649856 bytes)
Total duration 265.227 s
Modalities Rectified left RGB, 3D LiDAR, IMU, camera calibration, TF
Storage ROS 2 SQLite3 (.db3), metadata version 8
Audio None
Splits Evaluation collection; no prescribed train/validation/test split
Ground reference Manually measured rectangular cabin geometry

Sequence Inventory

Sequence Duration [s] DB3 [GiB] RGB LiDAR IMU SHA-256 prefix
E01 39.266 4.550 1159 393 3927 3a24e137af3065d4
E02 35.254 4.081 1041 352 3526 2a245ca2fe3260a0
E03 31.002 3.601 920 310 3100 584dfceaaf3b5529
E04 26.777 3.091 787 267 2678 5bac233c9ff1e6eb
E05 22.537 2.608 665 225 2254 13d44f4f31e672b5
E06 40.026 4.558 1142 400 4003 1051dcc1d5bfe2ad
E07 23.167 2.686 684 232 2317 f3efb53a5f36ca46
E08 47.198 5.438 1378 472 4720 741f550197df0ce5

Full database hashes, exact nanosecond durations, topic types, and validation records are stored in manifest.json. SHA256SUMS covers every distributed file except itself.

Directory Layout 

MirrorSentinel-Elevator/
  .gitattributes
  README.md
  LICENSE.md
  CITATION.cff
  RELEASE.json
  SHA256SUMS
  manifest.json
  data/
    elevator_01/
      elevator_01.db3
      metadata.yaml
    ...
    elevator_08/
      elevator_08.db3
      metadata.yaml
  annotations/
    elevator_01/room_gt_annotation.yaml
    ...
    elevator_08/room_gt_annotation.yaml

Each sequence contains the following topics:

Topic ROS 2 type Role
/ouster/points sensor_msgs/msg/PointCloud2 3D LiDAR returns
/ouster/imu sensor_msgs/msg/Imu Inertial measurements
/zed2/zed_node/left/image_rect_color sensor_msgs/msg/Image Rectified left RGB
/zed2/zed_node/left/camera_info sensor_msgs/msg/CameraInfo Camera calibration
/tf tf2_msgs/msg/TFMessage Dynamic transforms
/tf_static tf2_msgs/msg/TFMessage Static sensor transforms

Camera calibration and sensor transforms required to interpret the streams are therefore embedded in every bag.

Download and Playback

Install a current Hugging Face client:

python3 -m pip install -U "huggingface_hub>=0.32"
hf download KevinWong216/MirrorSentinel-Elevator \
  --repo-type dataset \
  --local-dir MirrorSentinel-Elevator

Verify all released bytes:

cd MirrorSentinel-Elevator
sha256sum --check SHA256SUMS

Inspect or replay one sequence with ROS 2 Humble:

ros2 bag info data/elevator_01
ros2 bag play data/elevator_01 --clock

The source-code repository includes the frozen mapper integration, visual prior adapters, map cleaner, evaluation implementation, and paper-facing experiment records. Follow its full-data reproduction instructions after placing this dataset at the documented data root.

Annotations and Evaluation Protocol

Each room_gt_annotation.yaml records one manually selected cabin corner, in-plane orientation, measured length and width, and a declared vertical interval. The file expands this compact rectangle into wall, floor, and ceiling geometry used by the released evaluator.

These annotations are not laser-scan ground truth and are not trajectory ground truth. They support cabin-boundary map-quality evaluation only. The informational bag: field retains a pre-release data-root alias for byte-level provenance; evaluation does not open that field.

The collection does not prescribe machine-learning splits. All E01-E08 sequences form the paper's target-domain evaluation collection. Researchers creating train/test partitions should report them explicitly and keep E02 and E03 together when independence at the physical-footprint level matters.

Intended Use

Appropriate uses include:

  • reflective-surface robustness evaluation for LiDAR, LIO, and LIVO systems;
  • multipath and behind-surface endpoint analysis;
  • visibility-based map cleaning;
  • RGB-LiDAR-IMU synchronization and fusion research;
  • reproducibility studies of the accompanying MirrorSentinel paper.

The dataset was not designed or validated for face recognition, identity inference, surveillance, access-control analysis, or profiling of people or facilities. Incidental visual appearances do not constitute labels for those tasks.

Limitations

  • The collection is deliberately focused: eight short elevator traversals over seven physical footprints.
  • Cabin geometry is manually measured rather than obtained from a survey-grade 3D scanner.
  • No globally referenced trajectory ground truth is distributed.
  • Reflective layouts, materials, lighting, and sensor placement do not cover every elevator or glass environment.
  • ROS 2 bags are domain-native research artifacts and are not rendered by the Hugging Face tabular dataset viewer.

Claims about new methods should therefore remain specific to the evaluated conditions or be supported by additional independent data.

Release, Privacy, and Rights

The maintainers reviewed the recordings and cleared this release for public distribution. The bags contain no audio. RGB frames can show the sensor operator, reflections, and institutional indoor spaces. CC BY 4.0 does not waive privacy, publicity, personality, trademark, or other third-party rights that may apply; downstream users remain responsible for their use.

The dataset is distributed under CC BY 4.0. Attribution is required. The license does not imply endorsement by the authors or their institution, and it does not grant rights to use names, logos, or trademarks for endorsement.

Citation 

@misc{wong2026mirrorsentinel_dataset,
  author       = {Kvn Wong},
  title        = {MirrorSentinel Reflective Elevator RGB-LiDAR-IMU Dataset},
  year         = {2026},
  version      = {1.0.0},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/KevinWong216/MirrorSentinel-Elevator}},
  note         = {Eight ROS 2 elevator traversals over seven physical footprints}
}

For questions and reproducibility reports, use the MirrorSentinel issue tracker.
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