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.
翻译:活动摄像机最近越来越受欢迎,因为它们具有强大的潜力,在动态性高或具有挑战性照明的情况下可以补充正常摄像机,同时定位和绘图系统(SLAM)提出一个重要问题,可能因增加事件摄像机而受益。然而,为了确保事件包容性多传感器系统的进展,需要新的基准序列。我们的贡献是第一个完整的基准数据集,通过一个多传感器装置,包括一个以事件为基础的立体摄像机、一个定期立体摄像机、多个深度传感器和一个惯性测量装置,收集了一套基准数据集,其中含有一个以事件为基础的立体摄像机、一个定期立体摄像机、一个多深度传感器和一个惯性测量装置。该装置是完全硬件同步的,并进行了精确的外向校准。所有序列都有非常精确的外部参考设备,如运动抓捕系统,所采集的地面真实数据。单个序列包括小型和大型环境,并覆盖动态视觉传感器所针对的具体挑战。