Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

三维（3D）点云配准是三维（3D）模型重建或三维测绘中的重要步骤。目前，点云配准有很多方法，但这些方法无法同时解决效率和精度的问题。我们提出一种基于RGB（红、绿、蓝）值的快速全局配准方法，该方法使用四初始点对（FIPP）算法。首先，统计数据集中点的不同RGB值的数量，并使用颜色过滤器丢弃目标数据集中点数过少的颜色。然后通过比较两个数据集之间颜色的相似度（在颜色容差范围内）在源数据集中生成候选点集，并使用改进的FIPP算法从两个数据集中搜索四个点对。最后，使用总体最小二乘法（TLS）计算全局配准的刚性变换矩阵，并使用迭代最近点（ICP）算法进行局部配准。所提出的方法（RGB - FIPP）已通过两种类型的数据进行验证，结果表明它在保持高精度的同时能有效提高三维点云配准的速度。该方法适用于具有RGB值的点。