Sequences Images Based Camera Self-calibration Method

Jiang, Zetao; Guo, Shutao; Jia, Lianggang

doi:10.1007/978-3-642-31919-8_69

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional camera calibration method is represented by Zhang method 2 , which has high robustness and accuracy. The self-calibration methods mainly based on Kruppa equation 3 and active vision 4 . The former has low requirements for the experimental environment, but its robustness and accuracy are not very good.…”

Section: Calibration Theorymentioning

confidence: 99%

“…Hand-eye calibration: Move the robotic arm to capture the origin calibration board from different positions and angles, calculate the position of the same reference point in the coordinate system of the robotic arm base, and use Root Mean Square Error (RMSE) to verify the accuracy of hand-eye calibration. The calculation method of RMSE is shown in Equation (3).…”

Section: System Verificationmentioning

confidence: 99%

See 1 more Smart Citation

Calibration of refueling robot based on binocular vision

Guo,

Zhang,

Zhang

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

View full text Add to dashboard Cite

This paper presents a comprehensive calibration system, including intrinsic and extrinsic parameters of the camera, handeye, and Tool Center Point (TCP) calibration. To improve efficiency and simplify the process, the system only requires two steps to calibrate four parameters and provides errors for each parameter. The entire process does not require any other devices except for one calibration board. This paper suggests using 3D distance as the error for stereo camera calibration and provides suggestions for capturing 15 different angles of images to cover the entire image. In terms of hand-eye calibration, this paper effectively utilizes the stereo camera's 3D reconstruction results and provides intuitive error feedback for TCP calibration. Through practical engineering practice, the calibration system presented in this paper has good application value. The system can meet the application needs of refueling robots and effectively reduce the calibration time to less than 10 minutes.

show abstract

Section: Calibration Theorymentioning

confidence: 99%

Section: System Verificationmentioning

confidence: 99%

Calibration of refueling robot based on binocular vision

Guo,

Zhang,

Zhang

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

View full text Add to dashboard Cite

show abstract

“…This method may lead to a considerable improvement of the stability and robustness of the results. [46] This article describes a new method for the self-calibration of a sequence of images with the varying intrinsic parameters of the camera. This article is based on the Kruppa equations.…”

Section: Survey Of the Previous Workmentioning

confidence: 99%

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

Satouri¹,

EL²,

TAIRI³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

show abstract

“…The second method is camera self-calibration, a simple and fast calibration method for unknown scenes. The self-calibration method was originally introduced into computer vision by Faugeras et al [10] and Luong et al [11], and improved by Wang et al [12], Jiang et al [13], Merras et al [14] and Akkad et al [15,16] to calculate the camera intrinsic parameters, based on Kruppa equations in terms of the fundamental matrix. In these studies, the accuracy of intrinsic parameter estimation was limited by the fundamental matrix, which is estimated by matching pairs of images from different views.…”

Section: Introductionmentioning

confidence: 99%

A LiDAR data-based camera self-calibration method

Feng

et al. 2018

Meas. Sci. Technol.

View full text Add to dashboard Cite

To find the intrinsic parameters of a camera, a LiDAR data-based camera self-calibration method is presented here. Parameters have been estimated using particle swarm optimization (PSO), enhancing the optimal solution of a multivariate cost function. The main procedure of camera intrinsic parameter estimation has three parts, which include extraction and fine matching of interest points in the images, establishment of cost function, based on Kruppa equations and optimization of PSO using LiDAR data as the initialization input. To improve the precision of matching pairs, a new method of maximal information coefficient (MIC) and maximum asymmetry score (MAS) was used to remove false matching pairs based on the RANSAC algorithm. Highly precise matching pairs were used to calculate the fundamental matrix so that the new cost function (deduced from Kruppa equations in terms of the fundamental matrix) was more accurate. The cost function involving four intrinsic parameters was minimized by PSO for the optimal solution. To overcome the issue of optimization pushed to a local optimum, LiDAR data was used to determine the scope of initialization, based on the solution to the P4P problem for camera focal length. To verify the accuracy and robustness of the proposed method, simulations and experiments were implemented and compared with two typical methods. Simulation results indicated that the intrinsic parameters estimated by the proposed method had absolute errors less than 1.0 pixel and relative errors smaller than 0.01%. Based on ground truth obtained from a meter ruler, the distance inversion accuracy in the experiments was smaller than 1.0 cm. Experimental and simulated results demonstrated that the proposed method was highly accurate and robust.

show abstract

Sequences Images Based Camera Self-calibration Method

Cited by 5 publications

References 4 publications

Calibration of refueling robot based on binocular vision

Calibration of refueling robot based on binocular vision

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

A LiDAR data-based camera self-calibration method

Contact Info

Product

Resources

About