A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Enebuse, Ikenna; Foo, Mathias; Ibrahim, Badaruddin; Ahmed, Hafiz; Supmak, Fhon; Eyobu, Odongo Steven

doi:10.1109/access.2021.3104514

Cited by 58 publications

(20 citation statements)

References 105 publications

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“…However, self-calibration of the system in the absence of an optic table is done with the aid of fiducial markers. Hand–eye calibration [ 17 ] and the pose calibration between the camera frame of reference and XY platform frame of reference are the two calibrations involved in our system configuration. Hand–eye calibration is carried out by attaching the fiducial marker to the end-effector (light source).…”

Section: Proposed Systemmentioning

confidence: 99%

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

et al. 2022

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Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.

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Section: Proposed Systemmentioning

confidence: 99%

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

et al. 2022

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“…Figure 4 shows a schematic diagram of the model. (11) The transformation relationship between the image frame coordinates (x, y) and the camera frame coordinates (X C , Y C , Z C ) is expressed by (11)(12)(13)(14) 0 0 0 1 0 0 0 , 1 0 0 1 0 1…”

Section: Derivation Of Modelmentioning

confidence: 99%

Machine-vision-based Spindle Positioning System of Grinding-wheel-saw Automatic Replacement System

Qiu¹,

Yang²,

Wu³

et al. 2022

Sensors and Materials

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To improve the working efficiency and safety of steel round bar sawing equipment, a grinding-wheel-saw automatic replacement system has been employed to replace grinding-wheel saws. In this paper, we report a machine-vision-based spindle positioning system for spindle localization in this replacement system. The hardware and software for the spindle positioning system were established, as well as an imaging model of the camera and the calibration of the vision system. The template matching approach applied to identify the shaft nut of the spindle is also described. Experimental results clearly demonstrate the effectiveness of the proposed machine-vision-based system.

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“…In order to estimate the kinematic parameters, several calibration methods have been introduced in the literature, examples include, circle point analysis method [2], arm signature identification method [3], screw axis identification method [4], etc. On the other hand, estimating matrix B with the help of camera calibration is a common practice and well documented in the computer vision community [5][6][7][8][9]. In the calibration process, the camera mounted on the robot flange captures images of a calibration board (checkerboard) from a number of different positions and orientations.…”

Section: Introductionmentioning

confidence: 99%

Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System

et al. 2022

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Although robotic vision systems offer a promising technology solution for rapid and reconfigurable in-process 3D inspection of complex and large parts in contemporary manufacturing, measurement accuracy poses a challenge for its wide deployment. One of the key issues in adopting a robotic vision system is to understand the extent of its measurement errors which are directly correlated with the calibration process. In this paper, a possible source of practical and inherent measurement uncertainties involved in the calibration process of a robotic vision system are discussed. The system considered in this work consists of an image sensor mounted on an industrial robot manipulator with six degrees of freedom. Based on a series of experimental tests and computer simulations, the paper gives a comprehensive performance comparison of different calibration approaches and shows the impact of measurement uncertainties on the calibration process. It has been found from the error sensitivity analysis that minor uncertainties in the calibration process can significantly affect the accuracy of the robotic vision system. Further investigations suggest that inducing errors in image calibration patterns can have an adverse effect on the hand–eye calibration process compared to the angular errors in the robot joints.

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A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Abstract: is funded through the Sêr Cymru programme by Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF).

Cited by 58 publications

References 105 publications

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

Machine-vision-based Spindle Positioning System of Grinding-wheel-saw Automatic Replacement System

Significance of Camera Pixel Error in the Calibration Process of a Robotic Vision System

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