VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
This provides a standardized benchmark for researchers working on SLAM with event cameras, addressing a gap in the field, though it is incremental as it focuses on dataset creation rather than algorithmic innovation.
The authors tackled the lack of benchmark datasets for event-inclusive multi-sensor SLAM by introducing VECtor, the first complete benchmark set captured with a synchronized multi-sensor setup including event-based and regular cameras, depth sensors, and an IMU, all sequences come with ground truth data from motion capture systems.
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.