A Deep Learning-Based Hand-eye Calibration Approach using a Single Reference Point on a Robot Manipulator.

We present a hand-eye calibration approach based on a deep learning-based regression architecture to find the transformation between the robot end-effector and an external camera. For this, we hypothesise that it is possible to track a single reference point in the robot's end-effector to estimate the hand-eye geometric transformation using a deep neural network and a 3D vision system. To explore this hypothesis, we design three experiments to study the different components of our proposed network architecture while solving isolated cases of the hand-eye calibration problem. Our experimental results using a simulated environment show that our proposed approach has less than 1 mm error for translation and less than 2.31 degrees error for orientation. We also carried out experiments for our third approach in two real robotic testbeds (a Universal Robot 3 and the Rethink Baxter robot). Our approach achieves 2 mm and 5.9 degrees, 4.53 mm and 9.2 degrees of errors for the Universal Robot UR3 and the Rethink Baxter robot.