论文标题
室内无人机定位的最小求解器
Minimal Solvers for Indoor UAV Positioning
论文作者
论文摘要
在本文中，我们考虑了一系列相对姿势问题的集合，这些问题自然出现在视觉室内无人机导航的应用中。我们专注于可用板上IMU的其他信息，从而通过重力向量提供部分外部校准。求解器是为部分校准的相机设计的，用于各种逼真的室内场景，这使得使用底楼的图像可以导航。当前的最新求解器使用更一般的假设，例如使用任意平面结构；但是，这些求解器并不能为真实场景产生足够的重建，也不能足够快地进行实时系统。 我们表明，与最先进的求解器相比，所提出的求解器具有更好的数值稳定性，更快，需要更少的点对应关系。这些属性是实时系统中强大导航的重要组成部分，我们在合成和真实数据上证明了我们的方法优于其他方法，并且可以产生出色的运动估计。
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art solvers. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other methods, and yields superior motion estimation.
