Nontarget-Based Global Registration for Unorganized Point Clouds Obtained in the Dynamic Shipyard Environment

Abstract

In this paper, we propose a method for registering unorganized point clouds without using targets or markers. Motivated by the 4-points congruent sets (4PCS) algorithm, which is a nontarget-based registration method commonly used in the related fields, we develop a feature-based 4PCS algorithm (F-4PCS). The method combines the basic approach of the 4PCS algorithm with geometric feature information to produce consistent global registration results efficiently. We use the features from the point feature histogram descriptor and the ones that capture the surface curvature. The experimental results show that the proposed method successfully registers point clouds of both the outdoor and indoor scenes and demonstrates better performance than the existing 4PCS-based registration methods.

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model

“…For coarse alignment, constraints such as point, line, and surface can be used. As a representative example, the authors in [6] developed the feature-based 4-point congruent sets (F-4PCS) method and successfully performed non-target feature-based registration. The authors in [7] proposed a local feature statistics histogram (LFSH) local feature descriptor for point cloud registration that rapidly produced robust results.…”