Nowadays, color-coded patterns are widely used to make real-time 3D shape measurement possible based on fringe projection profilometry (FPP). However, color crosstalk between the color camera remains a primary limitation of this method. In order to reduce the influence of color crosstalk on the phase retrieval accuracy, an accurate color crosstalk coefficient calibration method is proposed for color decoupling in this work. Firstly, based on the orthogonal fringe projection mode, the influence of color crosstalk coefficients on wrapped phase error is theoretically derived. Meanwhile, the truth phase values are generated based on smooth surface polynomial fitting to eliminate phase artifacts. Finally, by projecting the designed color orthogonal fringe patterns onto a standard plate, separate images from R, G and B channel could be obtained to extract the phase error, which can be further used for color channel crosstalk coefficients calibration. Multiple experiments with a standard white plate and sphere have verified that the method effectively improves phase quality, making it suitable for fast and accurate 3D shape measurement.
High-accuracy spatial distance measurement is essential to scientific research and equipment manufacturing, and industrial production control. To meet the demand of high-performance measurement, a distance measurement approach based on microwave photonic technology is proposed, in which two electro-optic modulators are connected in series. The first one is employed for performing transmission signal and the second is used for modulating echo signal, forming a vector superposition of microwave signal. By microwave frequency sweeping, the measured distance can be resolved from the microwave amplitude spectrum. To verify the performance of the proposed approach, a proof-of-concept experiment is carried out. The measurement results show that an accuracy of ±0.5 mm is obtained.
A photoelectric reference ruler is designed based on position sensitive detector (PSD) for providing the reference length. A calibration method of the photoelectric reference ruler is proposed, which introduces the common points for transition between the PSD coordinate systems. The common points are confirmed based on the special workpiece with positioning holes. Through placing the 1.5-in. ball target on the positioning holes, the coordinate systems and the common points for transformations are structured by high-precision measurement instruments. After the calibration, the double-theodolites system is oriented by the photoelectric reference ruler and operated to measure the spatial points and distances in the experiment. The mean error is <0.18 mm in points measurement and <0.14 mm in distance measurement, and the feasibility of the proposed calibration method is validated. Compared with the traditional reference ruler, the photoelectric reference ruler is proved to be applicable for measurement system orientation and spatial large-scale measurement.
Image feature extraction of target is the premise of parameter calibration of the cameras and sensors in vision
measurement. Because the spatial coordinates of features on the target are already known, the calibration mainly depends
on precise extraction of target image features and accurate matching to their corresponding spatial features. This paper,
which mainly discussed the planar target (circular feature target and square feature target) image, researched a new method
to achieve the feature extraction based on homography.
Firstly, the target plane can be defined as XY plane of the world coordinate system. According to the image
coordinates and their corresponding spatial coordinates of at least four corner positions on the target, homography
between the target plane and the image plane was solved
Secondly, the rough image coordinates of feature points on the image plane can be obtained by their corresponding
spatial coordinates. So the accurate corresponding relationship between the image plane and the space position was
established.
Finally, the accurate image coordinates of feature points were solved, via the least squares ellipse fitting (circular
feature target) or the corner detection algorithm (square feature target).
The experiments showed that this method can achieve precise extraction of planar target image feature points and
accurate matching to spatial feature points.
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