A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
The first SLAM benchmark datasets which simultaneously satisfy the following requirements:
Captured by a full hardware-synchronizedsensor 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;
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.
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.
