基于平移域估计的点云全局配准算法

针对迭代最近点(ICP)算法需要两幅点云具有良好的初始位置,否则易陷入局部最优的问题,提出了一种基于平移域估计的点云全局配准算法。首先分别计算数据点云和模型点云的去模糊主方向点云,利用两者平行于坐标轴的包围盒估计平移域范围;其次利用改进的全局ICP算法在估计出的平移域和-π,π]³的旋转域中进行全局搜索配准。该算法可以根据待配准点云自适应地估计平移域的大小,进行全局自动配准,配准过程中不需要计算点云的特征信息,所需设置的参数少,对点云的初始位置没有要求。实验结果表明,所提算法能够获取全局优化的精确的配准结果,同时提高了全局配准的效率。

Global point cloud registration algorithm based on translation domain estimating

The Iterative Closest Point (ICP) algorithm requires two point clouds to have a good initialization to start, otherwise the algorithm may easily get trapped into local optimum. In order to solve the problem, a novel translation domain estimating based global point cloud registration algorithm was proposed. The translation domain was estimated according to axis-aligned bounding box of calculating the defuzzification principal point clouds of data and model point clouds. With the estimated translation domain and -π, π]³ rotation domain, an improved globally optimal ICP was used to register for global searching. The proposed algorithm could estimate translation domain adaptively and register globally according to the point clouds for registration. The process of registration did not need to calculate the feature information of point clouds and was efficient for any initialization with less setting parameters. The experimental results show that the proposed algorithm can get accurate registration results of global optimization automatically, and also improve the efficiency of global registration.