Visual-inertial navigation systems are powerful in their ability to accurately estimate localization of mobile systems within complex environments that preclude the use of global navigation satellite systems. However, these navigation systems are reliant on accurate and up-to-date temporospatial calibrations of the sensors being used. As such, online estimators for these parameters are useful in resilient systems. This paper presents an extension to existing Kalman Filter based frameworks for estimating and calibrating the extrinsic parameters of multi-camera IMU systems. In addition to extending the filter framework to include multiple camera sensors, the measurement model was reformulated to make use of measurement data that is typically made available in fiducial detection software. A secondary filter layer was used to estimate time translation parameters without closed-loop feedback of sensor data. Experimental calibration results, including the use of cameras with non-overlapping fields of view, were used to validate the stability and accuracy of the filter formulation when compared to offline methods. Finally the generalized filter code has been open-sourced and is available online.

翻译：视觉-惯性导航系统在复杂环境中（如无法使用全球导航卫星系统的场景）具有准确估计移动系统定位的强大能力。然而，这类导航系统依赖于所用传感器精确且实时更新的时空标定参数。因此，针对这些参数的在线估计器对于弹性系统至关重要。本文提出了对现有基于卡尔曼滤波框架的扩展，用于估计和标定多相机IMU系统的外参。除了将滤波框架扩展至包含多个相机传感器外，还重新设计了测量模型，以利用通常可在标记物检测软件中获取的测量数据。通过引入次级滤波器层，在无需传感器数据闭环反馈的情况下估计时间平移参数。实验标定结果（包括使用非重叠视场相机）验证了该滤波公式相较于离线方法的稳定性与精度。最后，通用滤波代码已开源并可在线获取。