A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.
Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.
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