In this paper we deal with the initialization problem of a visual-inertial odometry system with rolling shutter cameras. Initialization is a prerequisite for using inertial signals and fusing them with visual data. We propose a novel statistical solution to the initialization problem on visual and inertial data simultaneously, by casting it into the renormalization scheme of Kanatani. The renormalization is an optimization scheme which intends to reduce the inherent statistical bias of common linear systems. We derive and present the necessary steps and methodology specific to the initialization problem. Extensive evaluations on ground truth exhibit superior performance and a gain in accuracy of up to \(20\%\) over the originally proposed Least Squares solution. The renormalization performs similarly to the optimal Maximum Likelihood estimate, despite arriving at the solution by different means. With this paper we are adding to the set of Computer Vision problems which can be cast into the renormalization scheme.

在本文中，我们处理带有卷帘快门相机的视觉惯性测距系统的初始化问题。初始化是使用惯性信号并将其与可视数据融合的先决条件。通过将其转化为Kanatani的重归一化方案，我们针对视觉和惯性数据的初始化问题提出了一种新颖的统计解决方案。重归一化是一种优化方案，旨在减少常见线性系统的固有统计偏差。我们得出并提出了针对初始化问题的必要步骤和方法。对地面真实性的广泛评估显示出卓越的性能，并且精度高达\（20 \％\）最初提出的最小二乘解决方案。尽管通过不同方式得出了解决方案，但重归一化的执行方式与最佳最大似然估计相似。通过本文，我们将添加到计算机视觉问题集中，这些问题可以纳入重归一化方案中。