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Pairwise registration for terrestrial laser scanner point clouds based on the covariance matrix
Remote Sensing Letters ( IF 2.3 ) Pub Date : 2021-06-13 , DOI: 10.1080/2150704x.2021.1938734
Yongjian Fu 1 , Zongchun Li 1 , Yong Deng 1 , Shihang Zhang 2 , Hua He 1 , Wenqi Wang 1 , Feng Xiong 1
Affiliation  

ABSTRACT

Automatic pairwise registration for unordered terrestrial laser scanner (TLS) point clouds is the pre-requisite for many applications, including 3D model reconstruction, cultural heritage management and landslide monitoring. However, most of the existing registration methods are still suffering from several limitations: (1) difficult to find the correct corresponding point pairs from the source and target point cloud, (2) low accuracy of pairwise registration and (3) the iterative process of registration is time-consuming. To overcome these challenges, based on the covariance matrix, this paper presents a robust and descriptive feature descriptor vector (FDV) to locally describe a point, by which the corresponding point pairs are obtained and the registration matrix is calculated. First, the FDVs of the original point cloud’s keypoints are calculated. Second, the corresponding point pairs are found via their FDVs. Third, the coarse transformation matrix is calculated by the corresponding point pairs using the singular value decomposition (SVD) algorithm, and which is further refined by the iterative closest point (ICP) algorithm to get a better result. Finally, the experiments are conducted on the Autonomous Systems Lab (ASL) and ISPRS datasets; our results show that the proposed algorithm can obtain good registration performance, and outperforms the compared methods.



中文翻译:

基于协方差矩阵的地面激光扫描仪点云成对配准

摘要

无序地面激光扫描仪 (TLS) 点云的自动成对配准是许多应用的先决条件,包括 3D 模型重建、文化遗产管理和滑坡监测。然而,大多数现有的配准方法仍然存在一些局限性:(1)难以从源点云和目标点云中找到正确的对应点对,(2)成对配准的准确性低以及(3)迭代过程注册很费时间。为了克服这些挑战,本文基于协方差矩阵,提出了一种鲁棒的描述性特征描述符向量(FDV)来局部描述一个点,通过它获得相应的点对并计算配准矩阵。第一的,计算原始点云关键点的 FDV。其次,通过它们的 FDV 找到相应的点对。第三,使用奇异值分解(SVD)算法通过对应点对计算粗变换矩阵,并通过迭代最近点(ICP)算法进一步细化以获得更好的结果。最后,在自治系统实验室 (ASL) 和 ISPRS 数据集上进行了实验;我们的结果表明,所提出的算法可以获得良好的配准性能,并且优于比较方法。并通过迭代最近点(ICP)算法进一步细化以获得更好的结果。最后,在自治系统实验室 (ASL) 和 ISPRS 数据集上进行了实验;我们的结果表明,所提出的算法可以获得良好的配准性能,并且优于比较方法。并通过迭代最近点(ICP)算法进一步细化以获得更好的结果。最后,在自治系统实验室 (ASL) 和 ISPRS 数据集上进行了实验;我们的结果表明,所提出的算法可以获得良好的配准性能,并且优于比较方法。

更新日期:2021-07-04
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