A new methodology in fast and accurate matching of the 2D and 3D point clouds extracted by laser scanner systems. (March 2015)

• Record Type: Journal Article
• Authors: Torabi, M.; Mousavi G., S.M.; Younesian, D.
• Is Part Of: Optics & laser technology. Volume 66(2015)
• Page Start: 28
• Page End: 34
• Publication Date: 2015-03
• Subjects: Fine registration -- Optimization problem -- Train wheel profile; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2014.07.004
• Languages: English
• ISSNs: 0030-3992

Abstract

Abstract: Registration of the point clouds is a conventional challenge in computer vision related applications. As an application, matching of train wheel profiles extracted from two viewpoints is studied in this paper. The registration problem is formulated into an optimization problem. An error minimization function for registration of the two partially overlapping point clouds is presented. The error function is defined as the sum of the squared distance between the source points and their corresponding pairs which should be minimized. The corresponding pairs are obtained thorough Iterative Closest Point (ICP) variants. Here, a point-to-plane ICP variant is employed. Principal Component Analysis (PCA) is used to obtain tangent planes. Thus it is shown that minimization of the proposed objective function diminishes point-to-plane ICP variant. We utilized this algorithm to register point clouds of two partially overlapping profiles of wheel train extracted from two viewpoints in 2D. Also, a number of synthetic point clouds and a number of real point clouds in 3D are studied to evaluate the reliability and rate of convergence in our method compared with other registration methods. Highlights: Converting registration issue to an optimization problem. Employing PCA to speed up fit tangent plane. Easy operation of the proposed algorithm with high-accurate fine registration. Robust, fast, high precision and adjustable for real-time application. Desiring to avoid a local minimum results and high-accurate fine registration.