Light plane calibration and accuracy analysis for multi-line structured light vision measurement system

Multi-line structured light binocular vision stereo matching method via coarse-to-fine spatial geometric constraints

3D measurement system based on divergent multi-line structured light projection, its accuracy analysis

Line-structured light 3D measurement is often used for 3D contour reconstruction of objects in complex industrial environments, where light plane calibration is a key step. In this paper, we propose a calibration method for a line-structured optical system based on a hinge-connected double-checkerboards stereo target. First, the target is moved randomly in multiple positions at any angle within the camera measurement space. Then, by acquiring any one image of the target with line-structured light, the 3D coordinates of the light stripes feature points are solved with the help of the external parameter matrix of the target plane and the camera coordinate system. Finally, the coordinate point cloud is denoised and used to quadratically fit the light plane. Compared with the traditional line-structured measurement system, the proposed method can acquire two calibration images at once; thus, only one image of line-structured light is needed to complete the light plane calibration. There is no strict requirement for the target pinch angle and placement, which improve system calibration speed with high accuracy. The experimental results show that the maximum RMS error of this method is 0.075mm, and the operation is simpler and more effective to meet the technical requirements of industrial 3D measurement.

A calibration method for line-structured light system by using sinusoidal fringes and homography matrix

Line structured light systems have been widely applied in the measurement of various fields. Calibration has been a hot research topic as a vitally important process of the line structured light system. The accurate calibration directly affects the measurement result of the line structured light system. However, the external environment factors, such as uneven illumination and uncertain light stripe width, can easily lead to an inaccurate extraction of light stripe center, which will affect the accuracy of the calibration. An image analysis-based framework in the calibration process was proposed for the line structure light system in this paper. A three-dimensional (3D) vision model of line structure light system was constructed. An image filtering model was established to equalize the uneven illumination of light stripe image. After segmenting the stripe image, an adaptive window was developed, and the width of the light stripe was estimated by sliding the window over the light stripe image. The light stripe center was calculated using the gray centroid method. The light plane was fitted based on the calibration points coordinates acquired by the camera system. In the measurement experiment of standard gauge block width, the maximum and minimum average deviations of 0.021 pixels and 0.008 pixels and the maximum and minimum absolute deviations of 0.023 pixels and 0.009 pixels could be obtained by using the proposed method, which implies the accuracy of the proposed method.

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

We introduce a structured light system that enables full-frame 3D scanning at speeds of 1000 fps, four times faster than the previous fastest systems. Our key innovation is the use of a custom acousto-optic light scanning device capable of projecting two million light planes per second. Coupling this device with an event camera allows our system to overcome the key bottleneck preventing previous structured light systems based on event cameras from achieving higher scanning speeds-the limited rate of illumination steering. Unlike these previous systems, ours uses the event camera's full-frame bandwidth, shifting the speed bottleneck from the illumination side to the imaging side. To mitigate this new bottleneck and further increase scanning speed, we introduce adaptive scanning strategies that leverage the event camera's asynchronous operation by selectively illuminating regions of interest, thereby achieving effective scanning speeds an order of magnitude beyond the camera's theoretical limit.

It is important to get reliable measurement data in Electrical impedance tomography (EIT) for getting satisfactory reconstruction result. Because EIT inverse problem is non-linear and ill-posed, incorrect data such as measured data through fault electrode can effect the estimation of resistivity distribution. So, EIT measurement system with methods for checking reliability has been developed. However, most existing methods require extra cost to evaluate reliability of EIT system. This paper presents modified Gauss-Newton (GN) method based on random sample consensus (RANSAC) algorithm for finding fault electrodes and getting good reconstructed image with faulty data for two-phase flow application. Also, suitable residual equation is proposed for determining the threshold to use RANSAC algorithm. Numerical simulations are performed to validate the performance of the proposed method. The results show that the proposed method has a good reconstruction performance compared to the conventional GN method.

Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtual structured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtual structured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a “Holoimage” by using the parameter optimized virtual structured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy.

Nowadays, bending workpiece angle measurement methods require touching the surface, which makes them prone to scratching the workpieces and inefficient. To solve the above problems, we propose a non-contact angle measurement system based on multi-line structured light. First, a bilinear laser is projected onto the surface of the workpiece and the machine tool lower die. However, reflections on the smooth workpiece will greatly destroy measurement accuracy. In this work, a UNet-based multi-exposure image fusion strategy is proposed to clip the reflection and increase the workpiece feature. Then, the Steger algorithm is used to achieve high-precision localization of the laser centerline. Finally, the angle of the workpiece is calculated indirectly according to fitting the laser centerline plane. The experimental results demonstrate that the measurement time of each workpiece is less than 100 ms and the measurement accuracy is ±0.3 deg in extreme installation locations and shadow. The proposed system can be extended to real industrial scenarios because of its high efficiency, accuracy, and robustness.

A calibration method of line-structured light system for measuring outer circle dimension during machining

Calibration of a 3D vision system using pattern projection

This paper describes the results from a feasibility analysis performed on two different structured light system designs and the image processing algorithms they require for dent detection and localization. The impact of each structured light system is analyzed in terms of their mechanical realization and the complexity of the image processing algorithms required for robust dent detection. The two design alternatives considered consist of projecting vertical or horizontal laser stripes on the drum surface. The first alternative produces straight lines in the image plane and requires scanning the drum surface horizontally, whereas the second alternative produces conic curves on the camera plane and requires scanning the drum surface vertically. That is, the first alternative favors image processing against mechanical realization while the second alternative favors mechanical realization against image processing. The results from simulated and real structured light systems are presented and their major advantages and disadvantages for dent detection are presented. The paper concludes with the lessons learned from experiments with real and simulated structured light system prototypes.

Metal surface 3D imaging based on convolutional autoencoder and light plane calibration

In the fields of industrial device detection, military product manufacturing, and medical imaging, it is necessary to acquire the 3D contour information of target objects. The method of reconstructing the 3D topography of objects based on laser scanning objects has been widely developed and applied. The laser scanning methods can be divided into point laser scanning, line laser scanning, and surface laser scanning. This paper carries out left and right monocular camera calibration, binocular stereo calibration, and laser light plane calibration based on machine vision. Line laser was used to scan the target scene to reconstruct the target scene. In order to surmount the problem that the speed of extracting the laser stripe center of the whole image with the traditional Steger algorithm in the process of 3D reconstruction is slow, we segment the region of laser stripe in the image with Otsu method, and the laser stripe center will be extracted by the Steger algorithm in the laser stripe region. Under the condition of ensuring the accuracy of extracting the laser stripe center, the speed of extracting image laser stripe center can be increased nearly about 3 times.KeywordsLine laserCamera calibrationThree-dimensional reconstructionOtsu