Laser stripe extraction method in industrial environments utilizing self-adaptive convolution technique

Abstract

A line-structured laser scanner is widely applied for 3D reconstruction in industrial environments with ubiquitous various luminance, complicated background, diverse objects, and instable lasers. These elements will show up as noise in the obtained laser stripe images. Therefore, the basic and key point for a line-structured laser scanner is to accurately extract the laser stripe from noise. This paper proposes an effective laser stripe extraction procedure with two steps. First, a novel laser stripe center extraction method based on the geometry information and correlation in the laser stripe, is designed to significantly eliminate noise and accurately extract the laser stripe centers. In addition, the robustness, speed, and accuracy of this method are respectively analyzed in detail. Second, piecewise fitting is adopted to acquire a smooth and continuous laser stripe centerline. In order to select the optimal fitting method, the characteristics of two spline methods, Akima spline and cubic Hermite spline, are deeply analyzed and compared. Finally, an experiment is carried out by using a rough metal step and a line-structured laser scanning system. The experiment results demonstrate that the proposed self-adaptive convolution-mass method can significantly eliminate noise in industrial environments. In addition, the cubic Hermite spline is a better choice for 3D reconstruction, rather than the Akima spline.

Robust Analysis and Laser Stripe Center Extraction for Rail Images

“…Then, a laser peak linking process is applied to fill in the gaps generated in the previous step. In the method proposed by Yin et al [14], preprocessing, including self-adaptive convolution and threshold segmentation, is adopted to reduce the influence of noise before center of mass is determined. Piecewise fitting is then applied to acquire the optimized laser stripe centerline.…”

“…Piecewise fitting is then applied to acquire the optimized laser stripe centerline. However, while there is always a gap in rail images, these methods [13,14] cannot recognize the gaps between surfaces caused by the shooting angle, and cannot always link the gaps; moreover, a considerable amount of time is spent on centerline optimization. In the work by Du et al [15], a robust laser stripe extraction method that uses ridge segmentation and region ranking was proposed; however, ridge direction cannot be computed for saturated regions of the stripe, which may occur in rail images.…”

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing

“…To date, none of the methods proposed is perfect and far from being ready to be applied to complicated environments. What caused the noises and bring difficulty to this detection process is that the intensity of the laser stripe that the camera captured is modulated by the interreflections between different surfaces in the environment, the saturation of the laser stripes, some materials like polished metal has extreme reflection capabilities, the incident angles between different surfaces and the uneven surfaces and the discontinuity of line caused by the randomly placed objects in the environment [17]. In other cases, the laser will scatter due to the haze, resulting in the irregular shape of the laser stripe in the image acquired by the camera, as shown in Figure 1.…”

A Laser-Based On-Machine Measuring System for Profile Accuracy of Double-Headed Screw Rotor

“…Wu, Selami and Yin et al used the Hessian matrix method, an improved Gaussian fitting method, and the cubic Hermite spline interpolation method to perform grayscale centroid extraction, respectively. Although the above methods have improved the extraction accuracy of spot centroids to a certain extent, they cannot improve the robustness of the algorithm yet [19,20,21]. To solve this problem, Li et al improved the poor robustness by Steger method.…”