Automatic 3D point cloud registration algorithm based on triangle similarity ratio consistency. Issue 14 (29th October 2020)

• Record Type: Journal Article
• Authors: Zou, Xuyan; He, Hanwu; Wu, Yueming; Chen, Youbin; Xu, Mingxi
• Is Part Of: IET image processing. Volume 14:Issue 14(2020)
• Page Start: 3314
• Page End: 3323
• Publication Date: 2020-10-29
• Subjects: image reconstruction -- augmented reality -- object recognition -- feature extraction -- image representation -- image matching -- image registration; point clouds -- triangle similarity ratio consistency -- three‐dimensional point cloud registration -- 3D object recognition -- novel local feature descriptor -- local angle statistics histogram -- LASH -- local shape geometries -- local neighbourhood -- registration algorithm -- triangle matching points -- consistent similarity ratios -- triangular matching points; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1087
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Three‐dimensional (3D) point cloud registration is a fundamental key issue in 3D reconstruction, 3D object recognition and augmented reality. In this study, the authors propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, they propose a 3D point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. They can use these matching sets to calculate multiple transformations between two point clouds. Finally, they use the error function to identify the best transformation and to achieve coarse alignment of the two point clouds. Experiments and comparisons with other global algorithms demonstrate that the proposed approach can be applied to register point clouds with considerable or limited overlaps and is robust to noise.