Abstract: To overcome the shortcoming that the Iterative Closest Point (ICP) algorithm is easy to fall into local optimum, an auto-registration method of ground based building point clouds based on line features and ICP algorithm was proposed. Firstly, the plane segmentation was performed on point clouds based on normal consistency. Secondly, the outlines of point clusters were extracted by alpha-shape algorithm, and the feature line segments were obtained by the splitting and fitting process. Then, the feature line pairs were taken as the registration primitives, and the angle and distance between line pairs were used as similarity measures for same-name feature matching in order to achieve the coarse registration of building cloud points. Finally, with the coarse registration result as the initial value, the ICP algorithm was used to realize the fine registration of building point clouds. Two sets of partially overlapping building point clouds were used to carry out the experiments. The experimental results show that the proposed coarse-to-fine registration method can effectively improve the dependency of ICP algorithm on initial value and realize the effective registration of partially overlapping building point clouds.

Key words: point cloud registration, building, plane segmentation, line feature, Iterative Closest Point (ICP) algorithm

摘要： 为克服迭代最近点(ICP)算法易陷入局部最优的缺陷，提出一种基于线特征及ICP算法的地基建筑物点云自动配准方法。首先，基于法向一致性进行建筑物点云平面分割；接着，采用alpha-shape算法进行点簇轮廓线提取，并拆分和拟合处理得到特征线段；然后，以线对作为配准基元，以线对夹角和距离作为相似性测度进行同名特征匹配，实现建筑物点云的粗配准；最后，以粗配准结果为初值，进一步采用ICP算法完成点云精确配准。利用两组部分重叠的建筑物点云进行配准实验，实验结果表明，采用由粗到精的配准方法能有效改善ICP算法对初值依赖的问题，实现具有部分重叠的建筑物点云的有效配准。

关键词: 点云配准, 建筑物, 平面分割, 特征线, 迭代最近点算法