A General Approach to Hand–Eye Calibration Through the Optimization of Atomic Transformations

Pedrosa, Eurico; Oliveira, Miguel; Lau, Nuno; Santos, Vítor

doi:10.1109/tro.2021.3062306

Cited by 21 publications

(12 citation statements)

References 31 publications

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“…In this case, we are dealing with a sensor-to-sensor calibration problem. The Iris UR10e [ 14 ] ( Figure 15 ) is a system composed of a robotic arm containing two RGB cameras, one fixed to the end effector of the robotic manipulator and the other mounted on a tripod. This system is a hand–eye calibration system, which is simultaneously a sensor in motion and sensor to sensor calibration problem.…”

Section: Resultsmentioning

confidence: 99%

“…ATOM is a methodology that was proven to accurately calibrate several robotic systems. Additional details on the methodology itself are provided in [ 1 ], and usage cases of varied robotic systems can be read in [ 14 , 15 , 16 , 17 ]. This paper is focused not on the calibration methodology of ATOM, but rather on the support functionalities that create an integrated solution for the calibration of robotic systems.…”

Section: Related Workmentioning

confidence: 99%

“…Our previous work focused on the development of the Atomic Transformations Optimization Method’s (ATOM) calibration methodology. In particular, we worked on expanding the methodology for tackling hand–eye calibration problems [ 14 ], agricultural robots [ 15 ], autonomous vehicles [ 16 ] and collaborative cells [ 17 ]. In addition, we have shown several examples of successful calibrations [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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ATOM Calibration Framework: Interaction and Visualization Functionalities

Gomes

Oliveira

Santos

2023

Sensors

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Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ATOM Calibration Framework: Interaction and Visualization Functionalities

Gomes

Oliveira

Santos

2023

Sensors

Self Cite

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“…Only few methods have extended the hand-eye/robot-world constraints towards multi-camera calibration. [28] can only handle the case of multiple cameras moving in front of one and the same calibration target, while [29] presents a general approach to handle multi-agent (non-overlapping) hand-eye calibration. However, it is an optimization-based method that-owing to the non-linear, non-convex nature of the problem-highly depends on an accurate starting point.…”

Section: F X R V E S R C T + D P E 5 W + P D Y + H < / L a T E X I T >mentioning

confidence: 99%

Accurate calibration of multi-perspective cameras from a generalization of the hand-eye constraint

Wang¹,

Jiang²,

Huang³

et al. 2022

Preprint

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Multi-perspective cameras are quickly gaining importance in many applications such as smart vehicles and virtual or augmented reality. However, a large system size or absence of overlap in neighbouring fields-of-view often complicate their calibration. We present a novel solution which relies on the availability of an external motion capture system. Our core contribution consists of an extension to the handeye calibration problem which jointly solves multi-eye-to-base problems in closed form. We furthermore demonstrate its equivalence to the multi-eye-in-hand problem. The practical validity of our approach is supported by our experiments, indicating that the method is highly efficient and accurate, and outperforms existing closed-form alternatives.

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“…( Tsai and Lenz, 1989 ; Chen, 1991 ; Daniilidis, 1999 ; Malm and Heyden, 2000 ) proposed a classic hand-eye calibration method for the Eye-in-Hand vision and robot correlation method, which are mainly divided into “two-step method” and “one-step method”, and its mathematical model boils down to solving the AX=XB matrix equation. After that, various more efficient hand-eye calibration algorithms and mathematical models have been proposed one after another ( Malti, 2013 ; Nobre and Heckman, 2019 ; Koide and Menegatti, 2019 ; Deng et al., 2021 ; Fei et al., 2021 ; Liu et al., 2021 ; Pedrosa et al., 2021 ; Wang and Min, 2021 ; Wu et al., 2021 ). In China, there have also been some researches on the hand-eye calibration method of picking robots.…”

Section: Introductionmentioning

confidence: 99%

A novel hand-eye calibration method of picking robot based on TOF camera

Zhang

Cheng

et al. 2023

Front. Plant Sci.

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Aiming at the stability of hand-eye calibration in fruit picking scene, a simple hand-eye calibration method for picking robot based on optimization combined with TOF (Time of Flight) camera is proposed. This method needs to fix the TOF depth camera at actual and calculated coordinates of the peach the end of the robot, operate the robot to take pictures of the calibration board from different poses, and record the current photographing poses to ensure that each group of pictures is clear and complete, so as to use the TOF depth camera to image the calibration board. Obtain multiple sets of calibration board depth maps and corresponding point cloud data, that is, “eye” data. Through the circle center extraction and positioning algorithm, the circle center points on each group of calibration plates are extracted, and a circle center sorting method based on the vector angle and the center of mass coordinates is designed to solve the circle center caused by factors such as mirror distortion, uneven illumination and different photographing poses. And through the tool center point of the actuator, the coordinate value of the circle center point on the four corners of each group of calibration plates in the robot end coordinate system is located in turn, and the “hand” data is obtained. Combined with the SVD method, And according to the obtained point residuals, the weight coefficients of the marker points are redistributed, and the hand-eye parameters are iteratively optimized, which improves the accuracy and stability of the hand-eye calibration. the method proposed in this paper has a better ability to locate the gross error under the environment of large gross errors. In order to verify the feasibility of the hand-eye calibration method, the indoor picking experiment was simulated, and the peaches were identified and positioned by combining deep learning and 3D vision to verify the proposed hand-eye calibration method. The JAKA six-axis robot and TuYang depth camera are used to build the experimental platform. The experimental results show that the method is simple to operate, has good stability, and the calibration plate is easy to manufacture and low in cost. work accuracy requirements.

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A General Approach to Hand–Eye Calibration Through the Optimization of Atomic Transformations

Cited by 21 publications

References 31 publications

ATOM Calibration Framework: Interaction and Visualization Functionalities

ATOM Calibration Framework: Interaction and Visualization Functionalities

Accurate calibration of multi-perspective cameras from a generalization of the hand-eye constraint

A novel hand-eye calibration method of picking robot based on TOF camera

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