Research of Online Hand–Eye Calibration Method Based on ChArUco Board

Lin, Wen‐Wei; Liang, Peidong; Luo, Guantai; Zhao, Ziyang; Zhang, Chentao

doi:10.3390/s22103805

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…The general procedure includes capturing images of a board with a specific pattern, such as a checkerboard, and optimizing the model using the captured image set. Several mathematical models and calibration boards [20] have been proposed to achieve more accurate and faster calibration. To optimize the mathematical model, there are two main approaches, which are the separation and simultaneous methods.…”

Section: Related Workmentioning

confidence: 99%

Hand-Eye Calibration via Linear and Nonlinear Regressions

Sato

2023

Automation

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For a robot to pick up an object viewed by a camera, the object’s position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object’s position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot’s posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object’s position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm.

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Section: Related Workmentioning

confidence: 99%

Hand-Eye Calibration via Linear and Nonlinear Regressions

Sato

2023

Automation

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“…On the other hand, increasing flexibility in practice is directly related to minimizing the dependency of the calibration methods on calibration objects or even eliminating the necessity for their usage at all. Another trend is the development of algorithms to automatically detect the need for recalibration, which can allow for continuous calibration adjustments based on detected changes in the environment [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional calibration methods require placing physical markers or objects with predefined dimensions in the robot’s workspace [ 6 , 8 , 9 , 10 ]. These methods are proven to enable a highly accurate calibration, but various aspects, such as inaccuracies in the manufacturing of calibration markers, the non-planarity of calibration boards, the sensitivity of feature detection algorithms to ambient conditions, and the complexity and inaccuracy of algorithms needed for their detection and decoding, reduce their flexibility and efficiency [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Submillimeter-Accurate Markerless Hand–Eye Calibration Based on a Robot’s Flange Features

Đalić,

Jovanović,

Marić

2024

Sensors

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An accurate and reliable estimation of the transformation matrix between an optical sensor and a robot is a key aspect of the hand–eye system calibration process in vision-guided robotic applications. This paper presents a novel approach to markerless hand–eye calibration that achieves streamlined, flexible, and highly accurate results, even without error compensation. The calibration procedure is mainly based on using the robot’s tool center point (TCP) as the reference point. The TCP coordinate estimation is based on the robot’s flange point cloud, considering its geometrical features. A mathematical model streamlining the conventional marker-based hand–eye calibration is derived. Furthermore, a novel algorithm for the automatic estimation of the flange’s geometric features from its point cloud, based on a 3D circle fitting, the least square method, and a nearest neighbor (NN) approach, is proposed. The accuracy of the proposed algorithm is validated using a calibration setting ring as the ground truth. Furthermore, to establish the minimal required number and configuration of calibration points, the impact of the number and the selection of the unique robot’s flange positions on the calibration accuracy is investigated and validated by real-world experiments. Our experimental findings strongly indicate that our hand–eye system, employing the proposed algorithm, enables the estimation of the transformation between the robot and the 3D scanner with submillimeter accuracy, even when using the minimum of four non-coplanar points for calibration. Our approach improves the calibration accuracy by approximately four times compared to the state of the art, while eliminating the need for error compensation. Moreover, our calibration approach reduces the required number of the robot’s flange positions by approximately 40%, and even more if the calibration procedure utilizes just four properly selected flange positions. The presented findings introduce a more efficient hand–eye calibration procedure, offering a superior simplicity of implementation and increased precision in various robotic applications.

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“…With the development of robotics and sensor technology [ 1 , 2 , 3 , 4 ], robotic grasping has gradually become a significant function for robots. It involves identifying and locating a target object through a visual sensor.…”

Section: Introductionmentioning

confidence: 99%

Reprojection Error Analysis and Algorithm Optimization of Hand–Eye Calibration for Manipulator System

Peng,

Ren,

Gao

et al. 2023

Sensors

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The Euclidean distance error of calibration results cannot be calculated during the hand–eye calibration process of a manipulator because the true values of the hand–eye conversion matrix cannot be obtained. In this study, a new method for error analysis and algorithm optimization is presented. An error analysis of the method is carried out using a priori knowledge that the location of the augmented reality markers is fixed during the calibration process. The coordinates of the AR marker center point are reprojected onto the pixel coordinate system and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling to obtain the Euclidean distance between the two coordinates as the basis for the error analysis. We then fine-tune the results of the hand–eye calibration algorithm to obtain the smallest reprojection error, thereby obtaining higher-precision calibration results. The experimental results show that, compared with the Tsai–Lenz algorithm, the optimized algorithm in this study reduces the average reprojection error by 44.43% and the average visual positioning error by 50.63%. Therefore, the proposed optimization method can significantly improve the accuracy of hand–eye calibration results.

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Research of Online Hand–Eye Calibration Method Based on ChArUco Board

Cited by 8 publications

References 22 publications

Hand-Eye Calibration via Linear and Nonlinear Regressions

Hand-Eye Calibration via Linear and Nonlinear Regressions

Submillimeter-Accurate Markerless Hand–Eye Calibration Based on a Robot’s Flange Features

Reprojection Error Analysis and Algorithm Optimization of Hand–Eye Calibration for Manipulator System

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