论文标题
重新归一化，用于滚动百叶窗视觉惯性探射仪的初始化
Renormalization for Initialization of Rolling Shutter Visual-Inertial Odometry
论文作者
论文摘要
在本文中，我们处理了带有滚动快门摄像机的视觉惯性进程系统的初始化问题。初始化是使用惯性信号并将其与视觉数据融合的先决条件。我们通过将其施放到卡纳塔尼（Kanatani）的重新归一化方案中，同时提出了一种新的统计解决方案，以同时对视觉和惯性数据进行初始化问题。重新规定是一种优化方案，旨在减少普通线性系统的固有统计偏差。我们得出并提出了特定于初始化问题的必要步骤和方法。对地面真相的广泛评估表现出卓越的性能，准确性高达$ 20 \％$，而不是最初提出的最小二乘解决方案。尽管通过不同的方式达到了解决方案，但重新归一化的执行与最佳最大似然估计的性能相似。在本文中，我们将可以施放在重新归一化方案中的一组计算机视觉问题中。
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.
