  In this work, we propose an approach for extrinsic sensor calibration from
per-sensor ego-motion estimates. Our problem formulation is based on dual
quaternions, enabling two different online capable solving approaches. We
provide a certifiable globally optimal and a fast local approach along with a
method to verify the globality of the local approach. Additionally, means for
integrating previous knowledge, for example, a common ground plane for planar
sensor motion, are described. Our algorithms are evaluated on simulated data
and on a publicly available dataset containing RGB-D camera images. Further,
our online calibration approach is tested on the KITTI odometry dataset, which
provides data of a lidar and two stereo camera systems mounted on a vehicle.
Our evaluation confirms the short run time, state-of-the-art accuracy, as well
as online capability of our approach while retaining the global optimality of
the solution at any time.
