一种区域层次上的自动点云配准算法

摘要

We present a region-based algorithm for the automatic registration of 3D point clouds. Most ex-isted algorithms align the two scans globally, making them unsuitable when the overlapping ratio is low or the input shapes do not have strong features. We notice that rigid transform is low-dimensional and two overlapped regions of the point clouds are enough to recover it. Thus, we align each pair of regions directly, and then solve an energy optimization to obtain the global transform from a series of region registrations by introducing confidence term and consistency term. Finally, sparse ICP algorithm is used for refinement. Ex-periments show that under premise of robustness to noise and outliers, our algorithm can align scans with lower overlapping ratio and more general shapes.%针对目前已有的三维点云配准算法直接在全局上进行配准，不能有效地处理重叠比例较低和重叠区域特征不明显的三维点云数据的问题，提出一种区域层次上的自动点云配准算法。首先利用刚体变换的低维性质，把区域作为基本的配准对象，将全局配准分解为多个规模更小的区域配准，通过重叠的区域恢复区域间局部的刚体变换；其次引入可信性和一致性的概念，通过求解一个优化问题从一系列区域配准中得到全局配准；最后用稀疏 ICP 算法进行精确配准。实验结果表明，该算法在保持对噪声和离群点鲁棒的前提下可以正确配准重叠比例更低的点云，适用范围更广泛。