Handheld, UGV, Other

VECtor

Event-centric multi-sensor SLAM

Indoor event/stereo/depth/lidar/IMU collection with two recording scales, hardware synchronization and MoCap or FARO-map pose references.

Metadata: Complete Metadata curator reviewed Released 2022

Quick facts

Vehicle type
Handheld, UGV, Other
Environment
Indoor
Data origin
Real-world
Sensor count
8
Ground truth
Available
Calibration
Full Raw Calibration Data
Annotations
Not reported

Sensor overview

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

Ground truth

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

Ground-truth references for VECtor
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 · Small-Scale Sequences 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
MoCap readings are described as sub-millimeter accurate; no independently validated uniform delivered-pose error is supplied after hand-eye transformation.
Reference notes
Hand-eye transformed external MoCap reference for small-scale sequences, including those with deliberately restricted motion. Camera/marker component counts are not reported.
6DoF Other FARO survey-grade laser scanner Not reported Not reported Partial · Large-Scale Sequences 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.
Hybrid
Accuracy notes
No measured trajectory accuracy or explicit ground-truth output cadence is supplied; scanner quality and lidar range precision are not pose accuracy.
Reference notes
ICP scan-to-map localization in CloudCompare: motion-compensated dataset lidar is fitted to an independently scanned FARO map. Manual first-pose initialization and previous-pose seeds; output starts in lidar frame and can be transformed to other sensor frames. This is not an independent moving tracker.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Full Raw Calibration Data
Processed parameters
Available
Raw calibration data
Available
Calibration targets
Static/blinking 9×6 virtual checkerboard, 6×6 Aprilgrid and 7×6 checkerboard; FARO room with checkerboard for camera/lidar calibration.

Reproducible calibration data

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

Notes

Camera intrinsics/extrinsics, camera/IMU, camera/MoCap and camera/lidar parameters plus CAD measurements and raw calibration sequences are released. Kinect uses factory intrinsics/internal depth-color relation; its color data assist calibration only. Five-hour stationary IMU recording supports Allan-variance parameters.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Depth and lidar streams are mutually split by recording scale.

Regular cameras use 2×2 binning because of bandwidth limits.

Event biases/aperture change with lighting; mean event rates are not fixed camera frequencies.

Kinect color is used for calibration but is not a released recording stream.

Lidar synchronization prose conflicts with the table/timing diagram.

Large-scale truth fits dataset lidar to surveyed FARO maps and lacks a measured trajectory error bound.

Secret submission-benchmark sequences and live service are planned work.

Cameras

Camera 1

Model
Prophesee Gen3 CD
Setup
Stereo
Count
1
Effective cameras
2
Modality
Event
Resolution
640 x 480
Rate
Not reported
Shutter
Not Reported
Lens type
Not Reported
HFOV
67 deg
VFOV
82 deg
Notes
One stereo event-camera system (two devices), ~17 cm baseline, Kowa LM5JCM lenses. Asynchronous events; no fixed frame rate. MoCap IR filtering and sequence-dependent bias/aperture settings affect event statistics.

Camera 2

Model
FLIR Grasshopper3
Setup
Stereo
Count
1
Effective cameras
2
Modality
Grayscale
Resolution
1224 x 1024
Rate
30 Hz
Shutter
Global
Lens type
Not Reported
HFOV
61 deg
VFOV
82 deg
Notes
One stereo system (two cameras), ~17 cm baseline, Kowa LM6JC lenses; 2×2 binning due to bandwidth. This regular stereo pair is physically separate from the event pair.

Camera 3

Model
Azure Kinect
Setup
Mono
Count
1
Effective cameras
1
Modality
Depth
Resolution
640 x 576
Rate
30 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
75 deg
VFOV
65 deg
Notes
Only depth is recorded, narrow unbinned mode, up to 3.86 m. Present in small-scale sequences, omitted from large-scale sequences. Color is used only for calibration; its rolling shutter is not assigned to the depth channel.

IMUs

Xsens AHRS inertial stream

Model
XSens MTi-30 AHRS
Count
1
Accel / gyro
200 / 200 Hz

GNSS

Not reported

LiDAR

Ouster OS0-128

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

Radar

Not reported

Additional sensors

Magnetometer

Name
Xsens AHRS magnetic-field output
Count
1
Notes
Three-axis magnetometer belongs to the recorded XSens MTi-30 AHRS device; no separate physical device count or component model is inferred, and magnetic-channel cadence is not separately specified.

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
gao2022vector

BibTeX

@misc{gao2022vector,
  title={VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM},
  author={Gao, Ling and Liang, Yuxuan and Yang, Jiaqi and Wu, Shaoxun and Wang, Chenyu and Chen, Jiaben and Kneip, Laurent},
  year={2022},
  eprint={2207.01404},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2207.01404v1}
}

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