上海科技大学知识管理系统

提供了一种基于球谐变换(SHT)和迭代优化的全局配准方法。提供了两个假设：首先，预先定义输入点云中正确匹配对的最小百分比表示为点云中离群点数量的限制，其次，基于场景预设用于确定正确匹配对的距离阈值，并表示为点云中离群点的有限距离。在所提供的算法中，点云首先经过粗配准以获得多个搜索域，并且基于评估标准对搜索域进行排序。采用分支定界法排除错误的搜索域，得到最终的配准结果。

A global registration method based on spherical harmonic transformation (SHT) and iterative optimization is provided. Two assumptions are provided : firstly, it is predefined that a minimum percentage of a correct matching pair in an input point cloud is represented as a limit on a quantity of outliers in the point cloud, and secondly, a distance threshold used to determine the correct matching pair is preset based on a scenario and represented as a limited distance of an outlier in the point cloud. In the algorithm provided, the point cloud first undergoes coarse registration to obtain a plurality of search domains, and the search domains are sorted based on an evaluation criterion. A branch and bound method is used to exclude an incorrect search domain and obtain a final registration result.