用离散时间表示法实现超快视觉惯性同步校准
Unleashing the Power of Discrete-Time State Representation: Ultrafast Target-based IMU-Camera Spatial-Temporal Calibration
- 采用离散时间状态表示，显著降低计算开销
- 解决离散法在时间校准上的缺陷，精度媲美连续方法
- 适合大规模设备校准，如无人机、手机等
视觉-惯性融合在机器人导航、增强现实等智能自主应用中至关重要。为实现最优状态估计，必须预先校准惯性测量单元（IMU）与相机之间的时空位移。现有方法多采用连续时间状态表示（如B样条），虽精度高，但计算成本巨大。为此，本文提出一种新型高效校准方法，充分发挥离散时间状态表示的潜力，并攻克其在时间校准中的不足。随着无人机、手机等视觉-惯性平台产量上升，若全球一百万设备每台节省1分钟校准时间，总计可节约2083个工作日。为推动研究与产业应用，代码已开源至https://github.com/JunlinSong/DT-VI-Calib。
原文摘要 · Abstract (English)
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.
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