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3D映射中的重要步骤。目前，点云配准的方法很多，但这些方法都不能同时解决效率和精度的问题。提出了一种基于RGB（Red，绿色，Blue）值的快速全局配准方法，该方法采用四初始点对（FIPP）算法。首先，对数据集中的点的不同RGB值的数量进行计数，并且通过使用滤色器来丢弃目标数据集中具有太少点的颜色。然后通过比较两个数据集之间的颜色相似度和颜色容差来生成源数据集中的候选点集，并使用改进的FIPP算法从两个数据集中搜索四个点对。最后，用总体最小二乘（TLS）算法计算全局配准的刚性变换矩阵，用迭代最近点（ICP）算法计算局部配准的刚性变换矩阵。采用两类数据对所提出的方法（RGB-FIPP）进行了验证，结果表明，该方法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。