Wang Biao, Zhou Jie, Huang Yan, Wang Yonghong, Huang Bin
School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel). 2022 Aug 30;22(17):6525. doi: 10.3390/s22176525.
Entire surface point clouds in complex objects cannot be captured in a single direction by using noncontact measurement methods, such as machine vision; therefore, different direction point clouds should be obtained and registered. However, high efficiency and precision are crucial for registration methods when dealing with huge number of point clouds. To solve this problem, an improved registration algorithm based on double threshold feature extraction and distance disparity matrix (DDM) is proposed in this study. Firstly, feature points are extracted with double thresholds using normal vectors and curvature to reduce the number of points. Secondly, a fast point feature histogram is established to describe the feature points and obtain the initial corresponding point pairs. Thirdly, obviously wrong corresponding point pairs are eliminated as much as possible by analysing the Euclidean invariant features of rigid body transformation combined with the DDM algorithm. Finally, the sample consensus initial alignment and the iterative closest point algorithms are used to complete the registration. Experimental results show that the proposed algorithm can quickly process large data point clouds and achieve efficient and precise matching of target objects. It can be used to improve the efficiency and precision of registration in distributed or mobile 3D measurement systems.
对于复杂物体，使用非接触测量方法（如机器视觉）无法在单一方向上捕获其整个表面点云；因此，需要获取不同方向的点云并进行配准。然而，在处理大量点云时，配准方法的高效性和精确性至关重要。为了解决这个问题，本研究提出了一种基于双阈值特征提取和距离差异矩阵（DDM）的改进配准算法。首先，利用法向量和曲率通过双阈值提取特征点，以减少点数。其次，建立快速点特征直方图来描述特征点并获得初始对应点对。第三，结合DDM算法，通过分析刚体变换的欧几里得不变特征，尽可能消除明显错误的对应点对。最后，使用样本一致性初始对齐和迭代最近点算法完成配准。实验结果表明，所提算法能够快速处理大数据点云，并实现目标物体的高效精确匹配。它可用于提高分布式或移动3D测量系统中配准的效率和精度。
