List of Datasets

UGV, Handheld

TIERS Multi-Modal LiDAR

Sensor-agnostic multi-modal LiDAR localization and mapping

Ten forest, indoor and road sequences comparing five lidar types, L515 RGB/depth and four integrated IMUs, with subset motion capture and SLAM-derived references.

Metadata: Complete Metadata curator reviewed Released 2022

Primary places to inspect, download, read about, or reproduce the dataset.

Supplementary

Not reported

Quick facts

Vehicle type
UGV, Handheld
Environment
Indoor, Outdoor
Data origin
Real-world
Sensor count
11
Ground truth
Available
Calibration
Partial Raw Data
Annotations
Not reported

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

2 reported

IMUs

4 reported

GNSS

Not reported

LiDAR

5 reported

Radar

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
6DoF Motion capture OptiTrack Not reported 120 Hz Partial · Forest01, Forest02, Indoor01, Indoor02, Indoor03. Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
External
Accuracy notes
Abstract advertises submillimeter accuracy but this paper does not give an independent dynamic pose uncertainty study.
Reference notes
External optical pose reference from OptiTrack; cadence comes from driver diagram, not a separately verified release manifest.
6DoF LiDAR SLAM Not reported Not reported Not reported Partial · Indoor04, Indoor05, Road02 Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Derived
Accuracy notes
SLAM reference error not independently quantified.
Reference notes
Official original-release README provides FAST-LIO SLAM results for Indoor04, Indoor05 and Road02, but explicitly none for Forest03 or Road01. These are derived trajectories without independent accuracy guarantees.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Partial Raw Data
Processed parameters
Available
Raw calibration data
Available
Calibration targets
Not reported

Reproducible calibration data

Raw calibration data is reported, so users can reproduce or inspect the calibration process.

Notes

Release provides lidar extrinsic tooling, calibration parameters and a raw LidarsCali bag. Complete raw calibration coverage for the full lidar/camera/IMU rig is not established.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Forest03 uses only VLP-16 and Horizon.

L515 cadence and 128-channel Ouster model conflict within the source.

Limited-view solid-state data can fail in close-obstacle interiors.

Motion capture covers five small-area sequences. Derived SLAM references are supplied for Indoor04, Indoor05 and Road02; Forest03 and Road01 have no released SLAM reference.

Original ten-sequence TIERS collection is separate from the enhanced extension, which remains unpublished pending its own review.

Cameras

Camera 1

Model
Intel RealSense L515
Setup
Rgbd
Count
1
Effective cameras
2
Modality
Rgbd
Resolution
1920 x 1080
Rate
Not reported
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
RGB output 1920×1080; depth output 1024×768. Detailed format states 10 Hz whereas Table I and Fig. 5 say 30 Hz; cadence unresolved. Camera-derived depth is an input, not ground-truth depth. Forest03 uses a reduced lidar-only handheld rig.

IMUs

Ouster OS1 integrated IMU

Model
ICM-20948
Count
1
Accel / gyro
100 / 100 Hz

Ouster OS0 integrated IMU

Model
ICM-20948
Count
1
Accel / gyro
100 / 100 Hz

Livox Horizon integrated IMU

Model
Bosch BMI088
Count
1
Accel / gyro
200 / 200 Hz

Livox Avia integrated IMU

Model
Bosch BMI088
Count
1
Accel / gyro
200 / 200 Hz

GNSS

Not reported

LiDAR

Velodyne VLP-16

Model or name
Velodyne VLP-16
Count
1
Dimensions
3
Rate
10 Hz
Channels
16
Range
100 m

Ouster OS1-64

Model or name
Ouster OS1-64
Count
1
Dimensions
3
Rate
10 Hz
Channels
64
Range
120 m

Ouster OS0-128

Model or name
Ouster OS0-128
Count
1
Dimensions
3
Rate
10 Hz
Channels
128
Range
50 m

Livox Horizon

Model or name
Livox Horizon
Count
1
Dimensions
3
Rate
10 Hz
Channels
Not reported
Range
260 m

Livox Avia

Model or name
Livox Avia
Count
1
Dimensions
3
Rate
10 Hz
Channels
Not reported
Range
450 m

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Not reported
Format
Not reported
Class count
Not reported
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Not reported
Optical flow Not reported
Depth ground truth Not reported

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
li2022tierslidars

BibTeX

@misc{li2022tierslidars,
  title = {Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms},
  author = {Li Qingqing and Yu Xianjia and Jorge Peña Queralta and Tomi Westerlund},
  year = {2022},
  eprint = {2203.03454v1},
  archivePrefix = {arXiv}
}

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