3 research outputs found
Trust Your IMU: Consequences of Ignoring the IMU Drift
In this paper, we argue that modern pre-integration methods for inertial
measurement units (IMUs) are accurate enough to ignore the drift for short time
intervals. This allows us to consider a simplified camera model, which in turn
admits further intrinsic calibration. We develop the first-ever solver to
jointly solve the relative pose problem with unknown and equal focal length and
radial distortion profile while utilizing the IMU data. Furthermore, we show
significant speed-up compared to state-of-the-art algorithms, with small or
negligible loss in accuracy for partially calibrated setups. The proposed
algorithms are tested on both synthetic and real data, where the latter is
focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the
proposed solvers on different commercially available low-cost UAVs, and
demonstrate that the novel assumption on IMU drift is feasible in real-life
applications. The extended intrinsic auto-calibration enables us to use
distorted input images, making tedious calibration processes obsolete, compared
to current state-of-the-art methods
Camera Pose Estimation with Unknown Principal Point
To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is not always true, especially for asymmetrically cropped images. In this paper, we develop the first exactly minimal solver for the case of unknown principal point and focal length by using four and a half point correspondences (P4.5Pfuv). We also present an extremely fast solver for the case of unknown aspect ratio (P5Pfuva). The new solvers outperform the previous state-of-the-art in terms of stability and speed. Finally, we explore the extremely challenging case of both unknown principal point and radial distortion, and develop the first practical non-minimal solver by using seven point correspondences (P7Pfruv). Experimental results on both simulated data and real Internet images demonstrate the usefulness of our new solvers