论文标题
在线初始化和外在时空校准，用于单眼视觉惯性探测器
Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry
论文作者
论文摘要
本文提出了一种在线初始化方法，用于引导基于优化的单眼视惯例（VIO）。该方法可以在线校准相机和IMU之间的相对转换（空间）和时间偏移（时间），并估计初始化阶段期间度量标准尺度，速度，重力，陀螺仪偏置和加速度计的初始值。为了补偿时间偏移的影响，我们的方法包括针对摄像机和IMU姿势估计的两种短期运动插值算法。此外，它还包括一个三步过程，以逐步估计参数从粗糙到细。首先，通过最大程度地减少相机和IMU之间的旋转差异来估算外部旋转，陀螺仪偏置和时间偏移。其次，通过使用补偿摄像头姿势并忽略加速度计偏置，大约估计了度量标准，重力和外部翻译。第三，通过考虑加速度计偏置和重力幅度来完善这些值。为了进一步优化系统状态，引入了全球和局部优化的非线性优化算法，该算法考虑了时间偏移。公共数据集的实验结果表明，可以通过提出的方法准确地估算初始值和外部参数以及传感器姿势。
This paper presents an online initialization method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO). The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias during the initialization stage. To compensate for the impact of time offset, our method includes two short-term motion interpolation algorithms for the camera and IMU pose estimation. Besides, it includes a three-step process to incrementally estimate the parameters from coarse to fine. First, the extrinsic rotation, gyroscope bias, and time offset are estimated by minimizing the rotation difference between the camera and IMU. Second, the metric scale, gravity, and extrinsic translation are approximately estimated by using the compensated camera poses and ignoring the accelerometer bias. Third, these values are refined by taking into account the accelerometer bias and the gravitational magnitude. For further optimizing the system states, a nonlinear optimization algorithm, which considers the time offset, is introduced for global and local optimization. Experimental results on public datasets show that the initial values and the extrinsic parameters, as well as the sensor poses, can be accurately estimated by the proposed method.
