A two-step calibration method of a large FOV binocular stereovision sensor for onsite measurement

Wang, Zhenxing; Wu, Zhuoqi; Zhen, Xijin; Yang, Ruijie; Xi, Juntong; Chen, Xiaobo

doi:10.1016/j.measurement.2014.10.037

Cited by 40 publications

(16 citation statements)

References 24 publications

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“…The baseline length is about 1000 mm, and the working distance is about 1500 mm. The intrinsic and structure parameters of these two cameras are calibrated by the method proposed in Reference [ 25 ]. The projector does not need to be calibrated, because it is not involved in the triangulation process.…”

Section: Methodsmentioning

confidence: 99%

A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light

Mei

Yang

Chen

et al. 2021

Sensors

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In this paper, a topology-based stereo matching method for 3D measurement using a single pattern of coded spot-array structured light is proposed. The pattern of spot array is designed with a central reference ring spot, and each spot in the pattern can be uniquely coded with the row and column indexes according to the predefined topological search path. A method using rectangle templates to find the encoded spots in the captured images is proposed in the case where coding spots are missing, and an interpolation method is also proposed for rebuilding the missing spots. Experimental results demonstrate that the proposed technique could exactly and uniquely decode each spot and establish the stereo matching relation successfully, which can be used to obtain three-dimensional (3D) reconstruction with a single-shot method.

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Section: Methodsmentioning

confidence: 99%

A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light

Mei

Yang

Chen

et al. 2021

Sensors

Self Cite

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“…There is a vast literature about 3D reconstruction for static camera systems, i.e. system with fixed cameras orientation, [33], [34], [35], [36], [38], [39], [41], [42], [44], [45]. Here we move a step forward to generalize the 3D reconstruction theory to our dynamic system.…”

Section: Dynamic 3d Reconstructionmentioning

confidence: 99%

“…The rotation of the cameras makes the external parameters (orientation and position of the cameras in the world reference frame) time-dependent quantities that have then to be carefully calibrated frame by frame to guarantee high accuracy in the reconstruction of the scene. The literature suggests two different calibration approaches [31]: i) 3D methods, which reconstruct key points of calibrated 3D targets and estimate the external parameters as the ones that minimize the 3D reconstruction error [32], [33], [34], [35], [36]; ii) 2D methods, which match features across the cameras, reconstruct the correspondent 3D points that are then projected back on the cameras, and estimate the external parameters as the ones that minimize the reprojection error [37], [38], [39], [40], [41], [42], [43], [44], [45].…”

Section: Introductionmentioning

confidence: 99%

CoMo: A Novel Comoving 3D Camera System

Cavagna

Xiao

Melillo

et al. 2021

IEEE Trans. Instrum. Meas.

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Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system home configuration (rotational stage at an angle equal to 0 • ) and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

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“…is process of solving parameters is called system calibration. e accuracy of system calibration directly determines the accuracy of subsequent measurement and calculation [3][4][5]. erefore, it is very important to study the high-precision and high-efficiency system calibration method for the 3D detection system.…”

Section: Introductionmentioning

confidence: 99%

Research on Calibration Method of Binocular Vision System Based on Neural Network

Zhu

Wang

2021

Security and Communication Networks

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In binocular vision inspection system, the calibration of detection equipment is the basis to ensure the subsequent detection accuracy. The current calibration methods have the disadvantages of complex calculation, low precision, and poor operability. In order to solve the above problems, the calibration method of binocular camera, the correction method of lens distortion, and the calibration method of projector in the binocular vision system based on surface structured light are studied in this paper. For lens distortion correction, on the basis of analyzing the traditional correction methods, a distortion correction method based on radial basis function neural network is proposed. Using the excellent nonlinear mapping ability of RBF neural network, the distortion correction models of different lenses can be obtained quickly. It overcomes the defect that the traditional correction model cannot adjust adaptively with the type of lens. The experimental results show that the accuracy of the method can meet the requirements of system calibration.

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A two-step calibration method of a large FOV binocular stereovision sensor for onsite measurement

Cited by 40 publications

References 24 publications

A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light

A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light

CoMo: A Novel Comoving 3D Camera System

Research on Calibration Method of Binocular Vision System Based on Neural Network

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