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

Bahadir, O., Siebert, J. P. and Aragon-Camarasa, G. (2023) A Deep Learning-Based Hand-eye Calibration Approach using a Single Reference Point on a Robot Manipulator. In: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO2022), Xishuangbanna, China, 5-9 December 2022, pp. 1109-1114. ISBN 9781665481090 (doi: 10.1109/ROBIO55434.2022.10011774)

Abstract

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Siebert, Dr Paul and Aragon Camarasa, Dr Gerardo and Bahadir, Dr Ozan
• Authors: Bahadir, O., Siebert, J. P., and Aragon-Camarasa, G.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781665481090
• Copyright Holders: Copyright © 2022 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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