Liu Haibin, Tang Yanglei, Wang Huanjie
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Sensors (Basel). 2024 Dec 10;24(24):7889. doi: 10.3390/s24247889.
This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning. Firstly, a judgment mechanism is incorporated into the DBSCAN algorithm. This mechanism is based on the standard deviation and correlation coefficient of point cloud clusters. It improves the algorithm's adaptive clustering capabilities. Secondly, the point cloud is partitioned into straight-line point cloud clusters, with each cluster generating adaptive grid cells. These adaptive cells extend the range of point cloud registration. This boosts the algorithm's robustness and provides an initial value for subsequent optimization. Lastly, cell segmentation is performed, where the number of segments is determined by the lengths of the adaptively generated cells, thereby improving registration accuracy. The proposed CSNDT algorithm demonstrates superior robustness, precision, and matching efficiency compared to classical point cloud registration methods such as the Iterative Closest Point (ICP) algorithm and the NDT algorithm.
本文提出了一种基于点云聚类和分割的配准方法，称为聚类与分割正态分布变换（CSNDT），旨在提高点云配准的范围和效率。传统的正态分布变换（NDT）算法在初始化阶段面临挑战，导致局部特征信息丢失和错误映射。为了解决这些限制，本文提出了一种自适应单元划分方法。首先，将一种基于点云簇的标准差和相关系数的判断机制纳入DBSCAN算法，提高了算法的自适应聚类能力。其次，将点云划分为直线点云簇，每个簇生成自适应网格单元，这些自适应单元扩展了点云配准的范围，增强了算法的鲁棒性，并为后续优化提供了初始值。最后，进行单元分割，分割数量由自适应生成单元的长度确定，从而提高配准精度。与迭代最近点（ICP）算法和NDT算法等经典点云配准方法相比，所提出的CSNDT算法具有更强的鲁棒性、更高的精度和匹配效率。
