摘要
为解决海量激光点云数据定位受干扰影响较大,准确性较差等问题,提出一种海量激光点云数据的关键特征准确定位输出方法。采用FP-tree微蜂窝树结构模型进行海量激光点云数据的存储结构分布式部署,提取海量激光点云数据的关联尺度特征值,对提取的特征量进行模糊指向性聚类处理,采用循环迭代检测方法进行海量激光点云数据的定位检测,实现海量激光点云数据的关键特征准确定位输出。仿真结果表明,采用该方法进行海量激光点云数据的关键特征提取的准确性较高,精度较好。
In order to solve the problem that the location of massive laser point cloud data is greatly affected by interference and the accuracy is poor,a method of accurate location and output of the key features of massive laser point cloud data is proposed. The FP-tree microcellular tree model is used to distribute the storage structure of the massive laser point cloud data,and the associated scale eigenvalues of the massive laser point cloud data are extracted,and the extracted features are processed by fuzzy directivity clustering. The method of cyclic iterative detection is used to detect the location of massive laser point cloud data,and the key features of mass laser point cloud data can be accurately located. The simulation results show that this method is used to carry out massive laser points. The key feature extraction of cloud data is accurate and accurate.
引文
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