Minimal Solvers for Indoor UAV Positioning
This work addresses real-time navigation challenges for UAVs in indoor environments, offering incremental improvements over existing solvers.
The paper tackles the problem of visual indoor UAV navigation by developing minimal solvers for relative pose estimation that incorporate IMU data for partial calibration, resulting in solvers that are faster, more numerically stable, and require fewer point correspondences than state-of-the-art methods.
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.