Visual-inertial fusion is crucial for a large amount of intelligent and autonomous applications, such as robot navigation and augmented reality. To bootstrap and achieve optimal state estimation, the spatial-temporal displacements between IMU and cameras must be calibrated in advance. Most existing calibration methods adopt continuous-time state representation, more specifically the B-spline. Despite these methods achieve precise spatial-temporal calibration, they suffer from high computational cost caused by continuous-time state representation. To this end, we propose a novel and extremely efficient calibration method that unleashes the power of discrete-time state representation. Moreover, the weakness of discrete-time state representation in temporal calibration is tackled in this paper. With the increasing production of drones, cellphones and other visual-inertial platforms, if one million devices need calibration around the world, saving one minute for the calibration of each device means saving 2083 work days in total. To benefit both the research and industry communities, the open-source implementation is released at https://github.com/JunlinSong/DT-VI-Calib.

翻译：视觉-惯性融合对于机器人导航和增强现实等大量智能自主应用至关重要。为实现最优状态估计，必须预先标定IMU与相机之间的时空位移。现有标定方法大多采用连续时间状态表示，特别是B样条方法。尽管这些方法能够实现精确的时空标定，但其连续时间状态表示导致计算成本高昂。为此，我们提出了一种新颖且极其高效的标定方法，充分发挥离散时间状态表示的优势。此外，本文解决了离散时间状态表示在时间标定方面的固有缺陷。随着无人机、智能手机及其他视觉-惯性平台产量的持续增长，若全球有百万台设备需要标定，每台设备节省一分钟标定时间，总计可节约2083个工作日。为惠及学术界与工业界，开源实现已发布于https://github.com/JunlinSong/DT-VI-Calib。