  Both, robot and hand-eye calibration haven been object to research for
decades. While current approaches manage to precisely and robustly identify the
parameters of a robot's kinematic model, they still rely on external devices,
such as calibration objects, markers and/or external sensors. Instead of trying
to fit the recorded measurements to a model of a known object, this paper
treats robot calibration as an offline SLAM problem, where scanning poses are
linked to a fixed point in space by a moving kinematic chain. As such, the
presented framework allows robot calibration using nothing but an arbitrary
eye-in-hand depth sensor, thus enabling fully autonomous self-calibration
without any external tools. My new approach is utilizes a modified version of
the Iterative Closest Point algorithm to run bundle adjustment on multiple 3D
recordings estimating the optimal parameters of the kinematic model. A detailed
evaluation of the system is shown on a real robot with various attached 3D
sensors. The presented results show that the system reaches precision
comparable to a dedicated external tracking system at a fraction of its cost.
