Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration

Shikun Li,Ruodan Lu,Liang Guo,Jianya Liu

doi:10.3390/rs13163210

Shikun Li, Ruodan Lu + Show 2 more

Open Access

https://doi.org/10.3390/rs13163210

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Abstract

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.

Highlights

Point cloud registration is a fundamental task that aims to calculate the optimal full scene coverage between the multiple scans from limited viewpoints by estimating the transformation parameters, that is, rotation matrix and translation vector [1]
(2) We propose a method of cross-selection of registration bases in overlapping regions
We describe the process of local extraction as follows, which corresponding to the local overlapping regions extraction part of Algorithm 3: (1) This step starts by calculating the variance of the two point clouds Vxs, Vys, Vzs, Vxt, Vyt, Vzt on the x-axis, y-axis, and z-axis, respectively

Summary

Introduction

Point cloud registration is a fundamental task that aims to calculate the optimal full scene coverage between the multiple scans from limited viewpoints by estimating the transformation parameters, that is, rotation matrix and translation vector [1]. Coarse alignment represented by the 4-Points Congruent Sets(4PCS) algorithm [2]. Some tools such as Vercator Cloud and CloudCompare follow the same coarse-to-fine registration strategy. Limited to the initial approaching pose conditions of the ICP algorithm, it is essential to afford accurate initial values provided by the coarse alignment, which can accelerate the convergence of the fine alignment to a global minimum. The coarse registration intends to estimate an optimal rigid transformation matrix to align the source point cloud P to target point cloud Q.

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