An Improved ICP Point Cloud Registration Algorithm Based on Three-Points Congruent Sets

Pengwei Yu, Yongqian Yang, Aizhong Tian, Changqing Du, Xiaofan Liu, Biying Pei, Kaixin Gu, Yimu Guo, Songyang Che
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引用次数: 1

Abstract

ICP (Iterative Closest Point) is the most widely used point cloud registration algorithm. However, some shortcomings still exist in this algorithm, such as (1) the need to manually determine the initial value of the registration; (2) the low efficiency for large-scale point cloud registration. Therefore, this paper proposes an improved ICP point cloud registration algorithm based on the three-points congruent sets. Firstly, the algorithm narrows the search of corresponding points by extracting 3D-SIFT key points. Then, possible corresponding points are confirmed by the position relationship between the centroid and key points. The optimal transformation matrix can also be determined based on the error function. Finally, the two point clouds are accurately aligned according to the resulted optimal transformation matrix and ICP algorithm. Experimentally, the algorithm is proved to be efficient without manual intervention.
基于三点同余集的改进ICP点云配准算法
ICP (Iterative nearest Point)是目前应用最广泛的点云配准算法。但该算法也存在一些不足,如(1)需要人工确定配准的初始值;(2)大规模点云配准效率低。为此,本文提出了一种改进的基于三点同余集的ICP点云配准算法。首先,该算法通过提取3D-SIFT关键点来缩小对应点的搜索范围;然后根据质心与关键点的位置关系确定可能的对应点。最优变换矩阵也可以根据误差函数确定。最后,根据得到的最优变换矩阵和ICP算法对两个点云进行精确对齐。实验证明,该算法在不需要人工干预的情况下是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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