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

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)...

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Published in:	IEEE robotics and automation letters Vol. 7; no. 3; pp. 1 - 8
Main Authors:	Gao, Ling, Liang, Yuxuan, Yang, Jiaqi, Wu, Shaoxun, Wang, Chenyu, Chen, Jiaben, Kneip, Laurent
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Benchmark testing Benchmarks Cameras Data Sets for Robot Learning Data Sets for Robotic Vision Data Sets for SLAM Inertial platforms Inertial sensing devices Laser radar Motion capture Robot vision systems Sensor Fusion Sensors Sequences Simultaneous localization and mapping Synchronization
ISSN:	2377-3766, 2377-3766
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Abstract	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.
AbstractList	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.
Author	Liang, Yuxuan Gao, Ling Chen, Jiaben Wu, Shaoxun Wang, Chenyu Kneip, Laurent Yang, Jiaqi
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SubjectTerms	Benchmark testing Benchmarks Cameras Data Sets for Robot Learning Data Sets for Robotic Vision Data Sets for SLAM Inertial platforms Inertial sensing devices Laser radar Motion capture Robot vision systems Sensor Fusion Sensors Sequences Simultaneous localization and mapping Synchronization
Title	VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
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