  The choice of poses for camera calibration with planar patterns is only
rarely considered - yet the calibration precision heavily depends on it. This
work presents a pose selection method that finds a compact and robust set of
calibration poses and is suitable for interactive calibration. Consequently,
singular poses that would lead to an unreliable solution are avoided
explicitly, while poses reducing the uncertainty of the calibration are
favoured. For this, we use uncertainty propagation.
  Our method takes advantage of a self-identifying calibration pattern to track
the camera pose in real-time. This allows to iteratively guide the user to the
target poses, until the desired quality level is reached. Therefore, only a
sparse set of key-frames is needed for calibration.
  The method is evaluated on separate training and testing sets, as well as on
synthetic data. Our approach performs better than comparable solutions while
requiring 30% less calibration frames.
