Abstract

Line laser 3D reconstruction technology is widely used in industrial applications. As a key step of this technology, line laser midline extraction directly affects the accuracy of the 3D reconstructed model. In reconstructing the shoe outsole, the traditional algorithm based on the threshold method to determine the laser position may result in a large amount of information loss and miscellaneous point misjudgment owing to the irregularity of the shoe outsole surface, which critically affects the laser imaging quality. To address this problem, an algorithm based on the QQ plot inspection of the laser has been proposed. The QQ plot is a scatter plot, the abscissa is usually the quantile of the standard normal distribution, and the ordinate is the quantile of the data to be tested. If the points on the scatter plot tend to be straight lines, the data to be tested is in a normal distribution. Based on this property, the proposed algorithm aims to check whether the pixels of the image column tend to be normally distributed, rather than using traditional thresholding methods to locate the laser. The objective is to examine whether the image column pixel distribution is normal, instead of using the traditional threshold method to locate the laser. However, the calculation speed of this method is extremely low. To enhance the efficiency of testing the normality of the QQ plot, a quantile-repetition (Q-R) test method is proposed. In this approach, the degree of repetition of quantiles and the position of Q-R values are used to replace the QQ plot based evaluation of the points being on a straight line, and the exact center position is determined by the GGM. The experimental results show that the proposed algorithm can extract more effective points and fewer invalid points of the laser compared to those obtained using the traditional approach, in a rapid, stable, and accurate manner.

© 2023 Optica Publishing Group

Laser stripe segmentation and centerline extraction based on 3D scanning imaging

Chuan Ye, Wenrong Feng, Qiyan Wang, Chao Wang, Bo Pan, Youchun Xie, Yuanyao Hu, and Jian Chen
Appl. Opt. 61(18) 5409-5418 (2022)

LaserNet: a method of laser stripe center extraction under non-ideal conditions

Jiawei Shang, Yuzhou Chen, and Jianhui Nie
Appl. Opt. 62(13) 3387-3397 (2023)

Robust and accurate sub-pixel extraction method of laser stripes in complex circumstances

Maosen Wan, Shuaidong Wang, Huining Zhao, Huakun Jia, and Liandong Yu
Appl. Opt. 60(36) 11196-11204 (2021)

Robust laser stripe extraction for three-dimensional reconstruction based on a cross-structured light sensor

Leiying He, Shanshan Wu, and Chuanyu Wu
Appl. Opt. 56(4) 823-832 (2017)

Extraction method of a nonuniform auxiliary laser stripe feature for three-dimensional reconstruction of large components

Yang Zhang, Wei Liu, Wenqi Wang, Peng Gao, Hongwen Xing, and Jianwei Ma
Appl. Opt. 59(22) 6573-6583 (2020)