代许松,花向红,田朋举,陶武勇,邹禄杰

• 1:武汉大学测绘学院
• 2:武汉大学灾害监测与防治研究中心
• 3:中南大学地球科学与信息物理学院
• 4:南昌大学信息工程学院

摘要(Abstract)：

针对迭代最近点算法(ICP)及其改进算法在精配准中使用时要求目标点云与源云间有良好初始姿态的问题,该文提出了一种利用主成分分析(PCA)提取特征点并求取轴向偏离比进行点云粗配准的方法。该方法首先利用KD-Tree创建每个点的K邻域;然后应用主成分分析法计算每个点邻域的轴向偏离比和邻域中心至切平面的距离筛选出点云的特征点,并建立目标点云与源点云间的对应关系,实现粗配准;最后应用迭代最近点算法对两片点云进行精配准。使用仿真数据以及实测建筑物数据进行了实验,结果表明,该文算法在效率和适用性上均优于SAC-IA算法和4PCS算法。

关键词(KeyWords)： 激光点云;轴向偏离比;迭代最近点算法;粗配准;精配准

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41674005,41871373)

作者(Author): 代许松,花向红,田朋举,陶武勇,邹禄杰

DOI: 10.16251/j.cnki.1009-2307.2021.12.014

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