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Hugging Face

Dataset Card for Elevator-LIO (FiftyOne Multimodal)#

preview

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 Description#

  • Curated by: Yifan Zhang, Yudong Huang, Yuchong Zhang, Changze Li, Haoran Liu, Ming Yang, Tong Qin (Shanghai Jiao Tong University); FiftyOne packaging by Harpreet Sahota

  • Funded by: [More Information Needed]

  • Shared by: Harpreet Sahota (@DataScienceHarp)

  • Language(s): English

  • License: CC BY 4.0

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

@_2floors, @_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 @ 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 @ ( 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 @.

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.

More Information#

  • Original ROS 1 bag dataset: https://huggingface.co/datasets/xiaofan0100/Elevator-LIO-Dataset

  • Elevator-LIO source code: https://github.com/xiaofan4122/Elevator-LIO

  • Project page: https://xiaofan4122.github.io/Elevator_LIO_Page/

  • FiftyOne MCAP viewer documentation: https://docs.voxel51.com/user_guide/app.html

Dataset Card Authors#

Harpreet Sahota

Dataset Card Contact#

@DataScienceHarp