  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.
