提出了一种基于几何信息的点云自动拼接方法，该方法可以在没有强度、影像等附加信息的情况下实现点云测站间的自动拼接。通过特征距离直方图提取待拼接站的特征点，然后在参考站的特征空间中进行K 临近查找得到特征点的初始匹配集合。提出相对高度和法向量的相似性度量，结合均方根距离剔除误匹配，计算得到初始转换参数，使用香农熵筛选复杂性较低的点参与迭代最临近点(ICP)精拼接。真实点云数据的实验证明，该方法可以剔除初始匹配集合中的误匹配，得到较好的初始转换参数，并提高ICP算法的效率和精度。

An automatic point clouds registration method is proposed based on geometric information. This method can realize point cloud automatic registration without additional intensity and images information. Feature points of registration scans are extracted by feature distance histogram, and the initial matching sets of matching points are searched by K closet neighbor search in feature space. Relative height and the normal vector similarity measure are put forward, false match is eliminated combining with the root mean square distance and the initial conversion parameters are calculated. Points of low complexity are selected by Shannon entropy to participate in iterative closet point (ICP) accurate registration. Experimental results of real point cloud data show that, the proposed method can eliminate false matching of initial matching set, get better initial transform parameters, and improve the efficiency and precision of ICP algorithm.