Laser imaging and approximation networks for calibration of three-dimensional vision

In this paper, we propose method to easily conduct the wheel alignment measurement by using Camera Calibration and Laser Triangulation. By taking advantage of laser projection which is ubiquitous in structured environments, our method makes it convenient for a mobile operation to collect the data needed for calibration. The proposed method estimates the relative position of the wheel by first determining the pose of each plane of the wheel and by comparing to standard data; we can easily find the misalignment of the wheel. The algorithm is validated first by performing experiments on simulated data and investigating its sensitivity for measurement to noise.Index Terms-camera calibration, laser triangulation, alignment, angle measurement.

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“…It is almost a calibration problem of structured light vision which can be found in related References [8,9]. Here gives solution:…”

Section: B Laser Line 3d Reconstructionmentioning

confidence: 99%

“…An image with the laser line pattern is taken, the system parameters L, d and f/a can be calculated as intrinsic parameter as shown in [8].…”

Section: B Measure Angle Of Wheel Rim and Shaft Jointmentioning

confidence: 99%

Measurement of wheel alignment using Camera Calibration and Laser Triangulation

Senjalia¹,

Pandya²,

Kapadia

2013

2013 Nirma University International Conference on Engineering (NUiCONE)

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“…The second kind of profile measurement methods is active vision, which generates the third dimensional information by the structured light. The light pattern in the active vision includes points 21 , lines 22 and gratings 23,24 . Xu G. 25 outlines a model to reconstruct the 3D laser projection point on the measured object.…”

Section: Introductionmentioning

confidence: 99%

“…Rodríguez J. A. M. 22 presents a 3D vision method using the mobile camera and the laser line. An approximation network is constructed to calibrate the scanning system.…”

Section: Introductionmentioning

confidence: 99%

Profile measurement adopting binocular active vision with normalization object of vector orthogonality

Chen

et al. 2019

Sci Rep

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Active-vision-based measurement plays an important role in the profile inspection study. The binocular vision, a passive vision, is employed in the active vision system to contribute the benefits of them. The laser plane is calibrated by two 2D targets without texture initially. Then, an L target with feature points is designed to construct the orthogonality object of two vectors. In order to accurately model the binocular-active-vision system, the feature points on the L target are built by two cameras and parameterized by the laser plane. Different from the optimization methods on the basis of the distance object, the laser plane is further refined by the distance-angle object. Thus, an optimization function is created considering both the norms and angles of the vectors. However, the scale of the distance is diverse from the scale of the angle. Therefore, the optimization function is enhanced by the normalization process to balance the different scales. The comparison experiments show that the binocular active vision with the normalization object of vector orthogonality achieves the decreasing distance errors of 25%, 22%, 13% and 4%, as well as the decreasing angle errors of 23%, 20%, 14% and 4%, which indicates an accurate measurement to reconstruct the object profile.

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A global solution of laser plane using the reconstructed distance from coordinate axis to calibration points

Yuan

et al. 2016

Results in Physics

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Laser imaging and approximation networks for calibration of three-dimensional vision

Cited by 6 publications

References 18 publications

Measurement of wheel alignment using Camera Calibration and Laser Triangulation

Measurement of wheel alignment using Camera Calibration and Laser Triangulation

Profile measurement adopting binocular active vision with normalization object of vector orthogonality

A global solution of laser plane using the reconstructed distance from coordinate axis to calibration points

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