VECtor Benchmark

sensor

The first SLAM benchmark datasets which simultaneously satisfy the following requirements:

Captured by a full hardware-synchronized sensor suite that includes an event stereo camera, a regular stereo camera, an RGB-D sensor, a LiDAR, and an IMU;
Covering the full spectrum of motion dynamics, environment complexities, and illumination conditions;
Complete six degrees of freedom ground truth signals for both small and large-scale scenarios, and highly-accurate intrinsic and extrinsic calibration.

News

Sept 20, 2022	Watch our video presentation for IROS'22 on Youtube or on Bilibili.
Jun 30, 2022	All data sequences have been uploaded. Enjoy!
May 18, 2022	A document containing evaluations for all data sequences can be found at here.
Apr 16, 2022	Dataset toolbox and data samples are now available.
Mar 24, 2022	Calibration toolbox, along with data sequences and calibration results, is now available.
Feb 24, 2022	VECtor Benchmark goes live!

Citation

Please cite the following publication when using this benchmark in an academic context:

RA-L

VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM

L. Gao, Y. Liang, J. Yang, S. Wu, C. Wang, J. Chen, and L. Kneip

IEEE Robotics and Automation Letters 2022

Abs arXiv PDF Supp

Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination. An important problem that may benefit from the addition of an event camera is given by Simultaneous Localization And Mapping (SLAM). However, in order to ensure progress on event-inclusive multi-sensor SLAM, novel benchmark sequences are needed. Our contribution is the first complete set of benchmark datasets captured with a multi-sensor setup containing an event-based stereo camera, a regular stereo camera, multiple depth sensors, and an inertial measurement unit. The setup is fully hardware-synchronized and underwent accurate extrinsic calibration. All sequences come with ground truth data captured by highly accurate external reference devices such as a motion capture system. Individual sequences include both small and large-scale environments, and cover the specific challenges targeted by dynamic vision sensors.

License

This datasets, along with documentation and toolbox, are released under the BSD 3-Clause Clear License.

Acknowledgement

This work was supported by the Natural Science Foundation of Shanghai (grant number 22ZR1441300), as well as the generous support provided by our industry partner Stereye Intelligent Technology.