Datasets:

Voxel51
/

Elevator-LIO-FiftyOne

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 379 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 367 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
      child 2, tags: list<item: null>
          child 0, item: null
      child 3, _media_type: string
      child 4, _rand: double
      child 5, sequence: string
      child 6, scene_type: string
      child 7, has_camera: bool
      child 8, has_elevator_flag: bool
      child 9, is_community: bool
      child 10, note: string
      child 11, lidar_msgs: int64
      child 12, imu_msgs: int64
      child 13, camera_msgs: int64
      child 14, duration_s: double
      child 15, has_pointcloud: bool
      child 16, has_imu: bool
      child 17, has_image: bool
      child 18, _dataset_id: struct<$oid: string>
          child 0, $oid: string
      child 19, created_at: struct<$date: string>
          child 0, $date: string
      child 20, last_modified_at: struct<$date: string>
          child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targets: null
skeletons: null
camera_intrinsics: null
static_transforms: null
annotation_runs: null
brain_methods: null
evaluations: null
runs: null
label_schemas: null
frame_label_schemas: null
sampl
...
 string, ftype: string, embedded_doc_type: string, subfield: string, fields:  (... 318 chars omitted)
  child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
      child 0, name: string
      child 1, ftype: string
      child 2, embedded_doc_type: string
      child 3, subfield: string
      child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
              child 0, name: string
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                  child 0, item: null
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              child 8, read_only: bool
              child 9, created_at: struct<$date: string>
                  child 0, $date: string
      child 5, db_field: string
      child 6, description: null
      child 7, info: null
      child 8, read_only: bool
      child 9, created_at: struct<$date: string>
          child 0, $date: string
slug: string
last_modified_at: struct<$date: string>
  child 0, $date: string
workspaces: list<item: null>
  child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
              samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 379 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 367 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, sequence: string
                    child 6, scene_type: string
                    child 7, has_camera: bool
                    child 8, has_elevator_flag: bool
                    child 9, is_community: bool
                    child 10, note: string
                    child 11, lidar_msgs: int64
                    child 12, imu_msgs: int64
                    child 13, camera_msgs: int64
                    child 14, duration_s: double
                    child 15, has_pointcloud: bool
                    child 16, has_imu: bool
                    child 17, has_image: bool
                    child 18, _dataset_id: struct<$oid: string>
                        child 0, $oid: string
                    child 19, created_at: struct<$date: string>
                        child 0, $date: string
                    child 20, last_modified_at: struct<$date: string>
                        child 0, $date: string
              group_media_types: null
              info: null
              app_config: null
              classes: null
              mask_targets: null
              default_mask_targets: null
              skeletons: null
              camera_intrinsics: null
              static_transforms: null
              annotation_runs: null
              brain_methods: null
              evaluations: null
              runs: null
              label_schemas: null
              frame_label_schemas: null
              sampl
              ...
               string, ftype: string, embedded_doc_type: string, subfield: string, fields:  (... 318 chars omitted)
                child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
                    child 0, name: string
                    child 1, ftype: string
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                    child 3, subfield: string
                    child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
                            child 0, name: string
                            child 1, ftype: string
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                            child 4, fields: list<item: null>
                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$date: string>
                                child 0, $date: string
                    child 5, db_field: string
                    child 6, description: null
                    child 7, info: null
                    child 8, read_only: bool
                    child 9, created_at: struct<$date: string>
                        child 0, $date: string
              slug: string
              last_modified_at: struct<$date: string>
                child 0, $date: string
              workspaces: list<item: null>
                child 0, item: null
              to
              {'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
              because column names don't match

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Dataset Card for Elevator-LIO (FiftyOne Multimodal)

A FiftyOne multimodal dataset of 22 MCAP episodes derived from the Elevator-LIO Dataset (arXiv 2605.24495). Each episode is a synchronized recording of Livox MID-360 LiDAR point clouds, IMU data, and (in two sequences) camera imagery captured during multi-floor building navigation including 79 elevator rides across office, dormitory, campus, and mall environments.

Converted from ROS 1 bags to MCAP format for direct use in FiftyOne's MCAP viewer, with Livox CustomMsg transcoded to foxglove.PointCloud (10 Hz), IMU at 200 Hz, and calibrated static LiDAR-to-IMU transforms embedded in every episode.

Installation

pip install -U fiftyone

Usage

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

dataset = fouh.load_from_hub(
    "Voxel51/Elevator-LIO-FiftyOne",
    name="elevator-lio",
    persistent=True,
)

session = fo.launch_app(dataset)

Dataset Details

Dataset Sources

Repository: https://github.com/xiaofan4122/Elevator-LIO
Paper: https://arxiv.org/abs/2605.24495
Demo: https://xiaofan4122.github.io/Elevator_LIO_Page/

Uses

Direct Use

Benchmarking LiDAR-inertial odometry (LIO) systems on elevator and multi-floor navigation scenarios, where the standard assumption of an inertial reference frame breaks down. Standard LIO systems attribute elevator-induced IMU acceleration to robot ego-motion, causing localization to collapse. Use this dataset to:

Scrub through elevator transition events in the FiftyOne MCAP viewer (3D point cloud, IMU Plot, elevator flag channel)
Compare localization behavior at elevator entry and exit across 79 rides
Evaluate vertical drift suppression algorithms on sequences with known floor-return ground truth
Develop and test non-inertial state estimation methods in diverse indoor environments (office, dormitory, campus, mall)

Out-of-Scope Use

This dataset does not contain ground-truth trajectory files or pose annotations. It is not suitable as a direct supervised learning dataset for pose regression. The two community-contributed sequences (horizontal LiDAR mount) have different sensor geometry than the 20 official sequences and require elevator.strong_prior_enable: true in the Elevator-LIO YAML configuration.

Dataset Structure

Topology

Flat multimodal dataset: 22 samples, one per recorded episode. media_type = multimodal. Each sample's filepath points to a .mcap file containing all synchronized sensor streams for that episode.

No train/val/test splits. Episodes are differentiated by the scene_type field and can be filtered with dataset.match(F("scene_type") == "office").

Episodes

Category	Count	Sequences
Office	10	Office1–10
Campus	4	Campus1–4
Dormitory	4	Dormitory1–4
Mall	2	Mall1–2
Community	2	@编程猫小渐_2floors, @编程猫小渐_3floors

Total recording time: 75.4 minutes (95.6 s min – 705.7 s max per episode).

Sample fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to `.mcap` episode file
`sequence`	`StringField`	Sequence name matching the original bag (e.g. `"Office1"`, `"Campus3"`)
`scene_type`	`StringField`	Environment category: `"office"`, `"campus"`, `"dormitory"`, `"mall"`, `"community"`
`has_camera`	`BooleanField`	`True` for Mall2 and Office3 — the only episodes with a camera stream
`has_elevator_flag`	`BooleanField`	`True` when `/LIO/set_elevator_flag` events are present in the MCAP
`is_community`	`BooleanField`	`True` for community-contributed sequences (non-author data)
`note`	`StringField`	Usage notes (e.g. horizontal LiDAR mount warning for community sequences)
`lidar_msgs`	`IntField`	Number of `foxglove.PointCloud` messages (10 Hz, 5-scan accumulation)
`imu_msgs`	`IntField`	Number of IMU JSON messages (~200 Hz)
`camera_msgs`	`IntField`	Number of camera image messages (0 for non-camera episodes)
`duration_s`	`FloatField`	Episode duration in seconds
`has_pointcloud`	`BooleanField`	Capability flag: episode contains LiDAR data
`has_imu`	`BooleanField`	Capability flag: episode contains IMU data
`has_image`	`BooleanField`	Capability flag: episode contains camera images

MCAP channels (inside each episode)

Every .mcap file contains the following channels, viewable in FiftyOne's MCAP tile viewer:

Topic	Schema	Tile	Rate	Notes
`/livox/lidar`	`foxglove.PointCloud`	3D	10 Hz	XYZI float32; 5 consecutive Livox CustomMsg packets merged per message for full scene coverage
`/livox/imu`	`elevator_lio_imu` (JSON)	Plot	~200 Hz	Fields: `ax/ay/az` (linear accel), `gx/gy/gz` (angular vel), `qx/qy/qz/qw` (orientation)
`/LIO/set_elevator_flag`	`elevator_flag` (JSON)	Message/Plot	event	Boolean `active` field; present in subset of sequences
`/tf_static`	`foxglove.FrameTransform`	—	static	LiDAR → IMU transform from `calibration_offsets.yaml`; no timestamp field → static store for full episode
`/camera/image_raw/compressed`	`foxglove.CompressedImage`	Image	variable	JPEG; Mall2 and Office3 only
`/camera/calibration`	`foxglove.CameraCalibration`	3D (frustum)	static	From `calibration_offsets.yaml` K matrix and `plumb_bob` distortion; Mall2 and Office3 only

Coordinate frame

Points are in the lidar sensor frame (Z-up, X-forward, Y-negated from ROS convention for correct top-down display in the foxglove 3D viewer). The static /tf_static transform places the lidar frame as a child of the imu frame. No odometry or world-frame trajectory is included — each scan is local to the sensor.

Conversion decisions

Livox CustomMsg → foxglove.PointCloud: The Livox MID-360 uses a proprietary non-repetitive scan pattern. Each 20 ms accumulation window covers a different scene sector. Five consecutive packets (100 ms window) are merged into one MCAP message to provide full scene coverage at 10 Hz and eliminate per-scan flicker in the viewer.
IMU as JSON channel: foxglove.Imu is absent from foxglove-sdk 0.26.0; a declared JSON schema (elevator_lio_imu) is used instead so the Plot tile can chart individual numeric fields.
Camera topic rename: /camera/image/compressed is renamed to /camera/image_raw/compressed to satisfy the foxglove image calibration gate (topic last segment must contain image + raw/rect/rectified).
NaN/Inf filtering: Invalid Livox returns (NaN/Inf float32 values) and zero-return points (range < 0.1 m) are discarded before packing each PointCloud message.
Community bags: The two @编程猫小渐 sequences use a horizontal LiDAR mount with different sensor geometry. calibration_offsets.yaml from the official sequences does not apply; the static transform defaults to the same calibration but these sequences require elevator.strong_prior_enable: true when run through Elevator-LIO.

Sensor calibration (from `calibration_offsets.yaml`)

Parameter	Value
`imu_R_lidar`	Identity (LiDAR and IMU frames co-aligned)
`imu_t_lidar`	`[-0.011, -0.023, 0.044]` m
Camera K (fx, fy)	1344.65, 1341.78
Camera principal point (cx, cy)	801.83, 617.84
Distortion model	`plumb_bob` (k1, k2, p1, p2, k3)

Dataset Creation

Curation Rationale

Standard LiDAR-inertial odometry systems assume the sensor platform operates in an inertial reference frame. When a robot rides an elevator, the elevator cabin imposes a non-inertial motion: the IMU captures elevator-induced acceleration superimposed on robot ego-motion, while the LiDAR observes only the cabin interior. These two signals become physically inconsistent, causing conventional LIO pipelines to diverge or collapse. No prior public dataset specifically targeted this failure mode. The authors collected 20 sequences containing 79 elevator rides to enable development and evaluation of LIO systems for multi-floor indoor navigation.

Source Data

Data Collection and Processing

Data was collected handheld across four building types in Shanghai: office buildings, dormitories, university campus, and a shopping mall. The sensor suite — Livox MID-360 LiDAR with built-in IMU and an optional synchronized industrial camera — was mounted on a Jetson Orin Nano compute unit and carried through elevator rides, lobby traversals, and floor-level exploration. Sequences range from single-floor corridors to large-scale cross-floor mapping with long vertical travel (up to 705 seconds per episode).

Two additional sequences were contributed by community member @编程猫小渐 (小红书 ID 9556244270), covering 2-floor and 3-floor elevator rides with a horizontally mounted LiDAR.

Who are the source data producers?

Yifan Zhang, Yudong Huang, Yuchong Zhang, Changze Li, Haoran Liu, Ming Yang, and Tong Qin at Shanghai Jiao Tong University (SJTU), Shanghai, China. Community contributions by @编程猫小渐.

Annotations

Annotation process

No manual annotations. The /LIO/set_elevator_flag boolean topic, present in a subset of sequences, was set programmatically by the Elevator-LIO algorithm's elevator mode detector during data collection. No ground-truth trajectory or pose annotations are distributed; localization accuracy in the paper was evaluated by measuring terminal height error on return-to-start sequences.

Who are the annotators?

The elevator flag signal was generated by the Elevator-LIO algorithm during recording. No human annotators.

Personal and Sensitive Information

Data was collected in public and semi-public building environments. No face or personal data was intentionally captured. The camera is present in only 2 of 22 sequences (Mall2 and Office3); all other sequences are LiDAR+IMU only with no imaging capability.

Citation

BibTeX:

@article{zhang2026elevatorlio,
  title={Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion},
  author={Zhang, Yifan and Huang, Yudong and Zhang, Yuchong and Li, Changze and Liu, Haoran and Yang, Ming and Qin, Tong},
  journal={arXiv preprint arXiv:2605.24495},
  year={2026}
}

APA:

Zhang, Y., Huang, Y., Zhang, Y., Li, C., Liu, H., Yang, M., & Qin, T. (2026). Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion. arXiv preprint arXiv:2605.24495.