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

TL;DR

This paper introduces VECtor, a comprehensive multi-sensor SLAM benchmark dataset with synchronized event, stereo, depth sensors, and IMU, enabling progress in event-inclusive SLAM research.

Contribution

It provides the first complete, hardware-synchronized multi-sensor SLAM benchmark dataset with ground truth, covering diverse environments and challenges for event-based sensors.

Findings

First complete multi-sensor SLAM benchmark dataset with event cameras.

Includes synchronized data with ground truth in various environments.

Facilitates development and evaluation of event-inclusive SLAM algorithms.

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.