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A Point Cloud Registration Algorithm Based on Feature Extraction and Matching

机译：基于特征提取与匹配的点云配准算法

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摘要

The existing registration algorithms suffer from low precision and slow speed when registering a large amount of point cloud data. In this paper, we propose a point cloud registration algorithm based on feature extraction and matching; the algorithm helps alleviate problems of precision and speed. In the rough registration stage, the algorithm extracts feature points based on the judgment of retention points and bumps, which improves the speed of feature point extraction. In the registration process, FPFH features and Hausdorff distance are used to search for corresponding point pairs, and the RANSAC algorithm is used to eliminate incorrect point pairs, thereby improving the accuracy of the corresponding relationship. In the precise registration phase, the algorithm uses an improved normal distribution transformation (INDT) algorithm. Experimental results show that given a large amount of point cloud data, this algorithm has advantages in both time and precision.
机译：现有的注册算法在注册大量点云数据时精度低，速度慢。本文提出了一种基于特征提取与匹配的点云配准算法。该算法有助于缓解精度和速度问题。在粗配准阶段，该算法基于对保留点和凹凸的判断，提取特征点，提高了特征点提取的速度。在注册过程中，使用FPFH特征和Hausdorff距离搜索对应的点对，并使用RANSAC算法消除不正确的点对，从而提高了对应关系的准确性。在精确配准阶段，该算法使用改进的正态分布变换（INDT）算法。实验结果表明，给定大量的点云数据，该算法在时间和精度上均具有优势。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第17期|7352691.1-7352691.9|共9页
  • 作者

    Liu Yongshan; Kong Dehan; Zhao Dandan; Gong Xiang; Han Guichun;

  • 作者单位

    Yanshan Univ, Dept Informat Sci & Engn, Hebei St 438, Qinhuangdao 066000, Hebei, Peoples R China;

    Hebei Univ Environm Engn, Dept Informat Engn, Jingang St 8, Qinhuangdao 066000, Hebei, Peoples R China;

    Yanshan Univ, Dept Informat Sci & Engn, Hebei St 438, Qinhuangdao 066000, Hebei, Peoples R China;

    Yanshan Univ, Dept Informat Sci & Engn, Hebei St 438, Qinhuangdao 066000, Hebei, Peoples R China;

    Yanshan Univ, Dept Informat Sci & Engn, Hebei St 438, Qinhuangdao 066000, Hebei, Peoples R China;

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