Calibration of robots has become a research field of great importance over the last decades especially in the field industrial robotics. The main reason for this is that the field of application was significantly broadened due to an increasing number of fully automated or robot assisted tasks to be performed. Those applications require significantly higher level of accuracy due to more delicate tasks that need to be fulfilled (e.g. assembly in the semiconductor industry or robot assisted medical surgery). In the past, (industrial) robot calibration had to be performed manually for every single robot under lab conditions in a long and cost intensive process. Expensive and complex measurement systems had to be operated by highly trained personnel. The result of this process is a set of measurements representing the robot pose in the task space (i.e. world coordinate system) and as joint encoder values. To determine the deviation, the robot pose indicated by the internal joint encoder values has to be compared to the physical pose (i.e. external measurement data). Hence, the errors in the kinematic model of the robot can be computed and therefore later on compensated. These errors are inevitable and caused by varying manufacturing tolerances and other sources of error (e.g. friction and deflection). They have to be compensated in order to achieve sufficient accuracy for the given tasks. Furthermore for performance, maintenance, or quality assurance reasons the robots may have to undergo the calibration process in constant time intervals to monitor and compensate e.g. ageing effects such as wear and tear. In modern production processes old fashioned procedures like the one mentioned above are no longer suitable. Therefore a new method has to be found that is less time consuming, more cost effective, and involves less (or in the long term even no) human interaction in the calibration process.