Abstract
In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.
Highlights
In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer
To verify the efficiency of the proposed method, we first evaluated our optimized boundary extraction algorithm based on different large-scale planar point clouds, which were acquired with a profilometer
To verify the efficiency of the proposed method, we first evaluated our optimized boundary extraction algorithm based on different large-scale planar point clouds, which boundary extraction algorithm based on different large-scale planar point clouds, which were acquired with a profilometer
Summary
An optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a largescale planar point cloud to ensure that the flatness measurement is precise. Among various machine vision schemes, a laser profilometer [1,2] is suitable for fast and high-precision flatness measurement because of its excellent point cloud acquisition ability. To solve the above problem, the rigid registration technology of a three-dimensional (3D) point cloud is used to achieve six degrees-of-freedom (6-DoF) [3,4] pose estimation of the object to be measured. The main problems in point cloud registration for flatness measurement are as follows: Because of its high computational complexity, the ICP algorithm cannot efficiently process largescale point clouds obtained by a laser profilometer. In the case of point clouds with curvature-invariant surfaces [13], the coarse registration precision is low, which adversely affects subsequent fine registration
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