Investigating deep-learning-based solutions for flexible and robust hand-eye calibration in robotics

Bahadir, Ozan (2023) Investigating deep-learning-based solutions for flexible and robust hand-eye calibration in robotics. PhD thesis, University of Glasgow.

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The cameras are the main sensor for robots to perceive their environments because they provide high-quality information and their low-cost. However, transforming the information obtained from cameras into robotic actions can be challenging. To manipulate objects in camera scenes, robots need to establish a transformation between the camera and the robot base, which is known as hand-eye calibration. Achieving accurate hand-eye calibration is critical for precise robotic manipulation, yet traditional approaches can be time-consuming, error-prone, and fail to account for changes in the camera or robot base over time.

This thesis proposes a novel approach that leverages the power of deep learning to automatically learn the mapping between the robot’s joint angles and the camera’s images, enabling real-time calibration updates. The approach samples the robot and camera spaces discretely and represents them continuously, enabling efficient and accurate computation of calibration parameters. By automating the calibration process and using deep learning algorithms, a more robust and efficient solution for hand-eye calibration in robotics is offered.

To develop a robust and flexible hand-eye calibration approach, three main studies were conducted. In the first study, a deep learning-based regression architecture was developed that processes RGB and depth images, as well as the poses of a single reference point selected on the robot end-effector with respect to the robot base acquired through the robot kinematic chain. The success of this architecture was tested in a simulated environment and two real robotic environments, evaluating the metric error and precision. In the second approach, the success of the developed approach was evaluated by transferring from metric error to task error by performing a real robotic manipulation task, specifically a pick-and-place. Additionally, the performance of the developed approach was compared with a classic hand-eye calibration approach, using three evaluation criteria: real robotic manipulation task, computational complexity, and repeatability. Finally, the learned calibration space of the developed deep learning-based hand-eye calibration approach was extended with new observations over time using Continual learning, making the approach more robust and flexible in handling environmental changes. Two buffer-based approaches were developed to eliminate the catastrophic forgetting problem, which is forgetting learned information over time by considering new observations. The performance and comparison of these approaches with the training of the developed approach in the first study using all datasets from scratch were tested on a simulated and a real-world environment.

Experimental results of this thesis reveal that: 1) a deep learning-based hand-eye calibration approach has competitive results with the classical approaches in terms of metric error (positional and rotational error deviation from the ground-truth) while eliminating data re-collection and re-training camera pose changes over time, and has 96 times better repeatability (precision) than the classic approach as well as it has the state-of-the-art result for it in comparison to the other deep learning-based hand-eye calibration approaches; 2) it also has competitive results with the classic approaches for performing a real-robotic manipulation task and reduces the computational complexity; 3) the leveraging deep-learning based hand-eye calibration approach with Continual Learning, it is possible to extend the learned calibration space over new observations without training the network from scratch with a lower accuracy gap (less than 1.5 mm and 2.5 degrees in the simulations and real-world environments for the translation and orientation components).

Overall, the proposed approach offers a more efficient and robust solution for hand-eye calibration in robotics, providing greater accuracy and flexibility to adapt to environments where the poses of the robot and camera base change according to each other over time. These changes may come from either robot or camera movement. The results of the studies demonstrate the effectiveness of the approach in achieving precise and reliable robotic manipulation, making it a promising solution for robotics applications.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Colleges/Schools: College of Science and Engineering > School of Computing Science
Supervisor's Name: Siebert, Dr. Paul and Gerardo, Dr. Aragon Camarasa
Date of Award: 2023
Depositing User: Theses Team
Unique ID: glathesis:2023-83779
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 25 Aug 2023 08:08
Last Modified: 29 Aug 2023 07:40
Thesis DOI: 10.5525/gla.thesis.83779
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