PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios

• Published in: Machines (Basel) Vol. 11; no. 2; p. 254
• Main Authors: Wang, Wenxin, Zhao, Changming, Zhang, Haiyang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Adaptive algorithms; adaptive registration algorithm; Algorithms; Color imagery; Computer graphics; Computer vision; Data acquisition systems; Datasets; Deep learning; Dimensional analysis; geometric information; Graphical representations; Graphics software; Image reconstruction; Lasers; Machine vision; Matching; Mathematical analysis; Mobile computing; photometric information; Photometry; point cloud; Public relations; Registration; Reverse engineering; Robotics; Teaching methods; Three dimensional models; transformation matrix
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines11020254

In the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. However, the mainstream method of point cloud registration is subject to the problems of a long registration time as well as a poor modeling effect, and these two factors cannot be balanced. To address this issue, we propose an adaptive registration mechanism based on a multi-dimensional analysis of practical application scenarios. Through the use of laser point clouds and RGB images, we are able to obtain geometric and photometric information, thus improving the data dimension. By adding target scene classification information to the RANSAC algorithm, combined with geometric matching and photometric matching, we are able to complete the adaptive estimation of the transformation matrix. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in terms of point cloud registration accuracy and time compared with other mainstream algorithms, striking a balance between expected performance and time cost.