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.