Efficient Relative Pose Estimation for Cameras and Generalized Cameras in Case of Known Relative Rotation Angle
This work addresses incremental improvements in computer vision for applications like robotics and augmented reality, where reliable pose estimation is crucial.
The paper tackles relative pose estimation for calibrated cameras and generalized cameras with known relative rotation angle, proposing minimal solvers that improve state-of-the-art for regular cameras and introduce a novel solver for generalized cameras, with experiments showing numerical stability, speed, and robustness.
We propose two minimal solutions to the problem of relative pose estimation of (i) a calibrated camera from four points in two views and (ii) a calibrated generalized camera from five points in two views. In both cases, the relative rotation angle between the views is assumed to be known. In practice, such angle can be derived from the readings of a 3d gyroscope. We represent the rotation part of the motion in terms of unit quaternions in order to construct polynomial equations encoding the epipolar constraints. The Gröbner basis technique is then used to efficiently derive the solutions. Our first solver for regular cameras significantly improves the existing state-of-the-art solution. The second solver for generalized cameras is novel. The presented minimal solvers can be used in a hypothesize-and-test architecture such as RANSAC for reliable pose estimation. Experiments on synthetic and real datasets confirm that our algorithms are numerically stable, fast and robust.