Rapid and Simultaneous Visual-based Estimation of Kinematic and Hand-eye Parameters of Industrial Mobile Manipulators

Abstract

Manufacturing applications increasingly integrate visually aided robotic systems. Such systems must rely on excellent kinematic parameter calibration and a hand – eye matrix estimation to perform according to standards. The latter is as precise as the camera pose estimation capability and the robotic forward kinematic precision. To enhance the overall system’s precision, one must simultaneously act and improve the robot’s kinematic parameters and hand – eye transformation due to mutual inference. This work exploits standard 2D camera systems to simultaneously estimate the kinematic parameters and the hand – eye transformation matrix through a method based on the Unscented Kalman Filter (UKF) and the parameters uncertainty transportation through the robot’s kinematic. The method employs data gathered during the robot movements and camera readings and iteratively improves the system parameters’ estimate. The method is applied to industrial mobile manipulators and tested on both synthetic data and real experiment data, showing a great improvement in the kinematic parameters estimation.