The increasing computing and sensing capabilities of modern mobile phones have spurred research interests in developing new visual–inertial odometry (VIO) techniques to turn a smartphone into a self-contained vision-aided inertial navigation system for various applications. Smartphones nowadays use cameras with optical image stabilization (OIS) technology to reduce image blurs. However, the mechanism may result in varying camera intrinsic parameters (CIP), which must be taken into account in VIO computation. In this article, we first develop a linear model to relate the CIP with the inertial measurement unit measured acceleration. Based on the model, we introduce a new VIO method, called CIP-VMobile, which treats CIP as state variables and tightly couples them with other state variables in a graph optimization process to estimate the optimal state. The method uses the linear model to construct a factor graph and uses the linear-model-computed values as initial CIP estimates to speed up the VIO computation and attain a better pose estimation result. Simulation and experimental results with an iPhone 7 validate the method's efficacy. Based on CIP-VMobile, we fabricated a robotic navigation aid (RNA) based on an iPhone 7 for assisted navigation. Experimental results with the RNA demonstrate CIP-VMobile's promise in real-world navigation applications.

中文翻译：

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视觉惯性里程法估算手机的相机固有参数及其在辅助导航中的应用

现代手机日益增长的计算和传感能力激发了人们对开发新的视觉惯性里程计（VIO）技术的兴趣，这些技术将智能手机转变为用于各种应用的独立的视觉辅助惯性导航系统。如今的智能手机使用具有光学图像稳定（OIS）技术的相机来减少图像模糊。但是，该机制可能会导致相机固有参数（CIP）发生变化，在VIO计算中必须考虑在内。在本文中，我们首先开发一个线性模型，以将CIP与惯性测量单元测得的加速度联系起来。基于该模型，我们引入了一种新的VIO方法，称为CIP-VMobile，该方法将CIP视为状态变量，并在图形优化过程中将其与其他状态变量紧密耦合以估计最佳状态。该方法使用线性模型构建因子图，并使用线性模型计算的值作为初始CIP估计，以加快VIO计算并获得更好的姿态估计结果。使用iPhone 7进行的仿真和实验结果验证了该方法的有效性。基于CIP-VMobile，我们制造了基于iPhone 7的机器人导航辅助系统（RNA），用于辅助导航。RNA的实验结果证明了CIP-VMobile在现实导航应用中的前景。我们制造了基于iPhone 7的机器人辅助导航（RNA），用于辅助导航。RNA的实验结果证明了CIP-VMobile在现实导航应用中的前景。我们制造了基于iPhone 7的机器人辅助导航（RNA），用于辅助导航。RNA的实验结果证明了CIP-VMobile在现实导航应用中的前景。