论文标题
矢量：多传感器大满贯的多功能性事件为中心的基准
VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
论文作者
论文摘要
事件摄像机最近在高动力或具有挑战性的照明情况下具有强大的常规摄像头的潜力，最近越来越受欢迎。通过同时定位和映射（SLAM）给出了可能受益于事件摄像机的重要问题。但是，为了确保在包含事件的多传感器大满贯上进展，需要新颖的基准序列。我们的贡献是使用包含基于事件的立体声相机，常规立体声摄像机，多个深度传感器和惯性测量单元的多传感器设置捕获的第一组基准数据集。该设置是完全硬件同步的，并且进行了准确的外部校准。所有序列都均均均均伴随着由高度准确的外部参考设备（例如运动捕获系统）捕获的地面真实数据。各个序列包括小型和大型环境，并涵盖动态视觉传感器针对的特定挑战。
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.
