Abstract — We present a Bayesian approach to calibrating the hand-eye kinematics of an anthropomorphic robot. In our approach, the robot perceives the pose of its end-effector with its head-mounted camera through visual markers attached to its end-effector. It collects training observations at several configurations of its 7-DoF arm and 2-DoF neck which are subsequently used for an optimization in a batch process. We tune Denavit-Hartenberg parameters and joint gear reductions as a minimal representation of the rigid kinematic chain. In order to handle the uncertainties of marker pose estimates and joint position measurements, we use a maximum a posteriori formulation that allows for incorporating prior model knowledge. This way, a multitude of parameters can be optimized from only few observations. We demonstrate our approach in simulation experiments and with a real robot and provide indepth experimental analysis of our optimization approach. I.

Abstract. In this paper, we present an automatic approach for the kine-matic calibration of the humanoid robot NAO. The kinematic calibra-tion has a deep impact on the performance of a robot playing soccer, which is walking and kicking, and therefore it is a crucial step prior to a match. So far, the existing calibration methods are time-consuming and error-prone, since they rely on the assistance of humans. The auto-matic calibration procedure instead consists of a self-acting measurement phase, in which two checkerboards, that are attached to the robot’s feet, are visually observed by a camera under several different kinematic con-figurations, and a final optimization phase, in which the calibration is formulated as a non-linear least squares problem, that is finally solved utilizing the Levenberg-Marquardt algorithm. 1