Self-calibration with Partially Known Rotations

Abstract

Abstract. Self-calibration methods allow estimating the intrinsic camera parameters without using a known calibration object. However, such methods are very sensitive to noise, even in the simple special case of a purely rotating camera. Suitable pan-tilt-units can be used to perform pure camera rotations. In this case, we can get partial knowledge of the rotations, e.g. by rotating twice about the same axis. We present extended self-calibration algorithms which use such knowledge. In systematic simulations, we show that our new algorithms are less sensitive to noise. Experiments on real data result in a systematic error caused by non-ideal hardware. However, our algorithms can reduce the systematic error. In the case of complete rotation knowledge, it can even be greatly reduced.

Intrinsic and extrinsic active self-calibration of multi-camera systems

“…Based on Eq. (4), Bajramovic and Denzler [2] formulate a non-linear optimization problem which uses (partial) rotation knowledge to estimate K. We extend this approach. In addition to the pinhole parameters K, we also estimate the radial distortion parameters d def = (k 1 , k 2 , t 1 , t 2 ) and the hand-eye rotation R H .…”

“…Frahm and Koch [12] and Bajramovic and Denzler [2] use different extents of rotation knowledge to improve the intrinsic self-calibration.…”