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LASER RADAR POINT CLOUD DATA CLASSIFICATION METHOD, APPARATUS AND DEVICE, AND STORAGE MEDIUM

机译:激光雷达点云数据分类方法,装置和装置以及存储介质

摘要

A laser radar point cloud data classification method, apparatus and device, and a storage medium. The method comprises: acquiring sample point cloud data, and laser radar point cloud data to be classified (101); establishing a point cloud classifier according to the sample point cloud data (102); and classifying the laser radar point cloud data to be classified by means of the point cloud classifier (103). According to the method, a cloud point classifier is trained by means of sample point cloud data, and laser radar point cloud data to be classified is automatically classified by means of the point cloud classifier, thereby greatly reducing a manual intervention component during a classification process, and achieving a high automation degree and a low cost. Sample point cloud data used for training the point cloud classifier covers pole tower data of various tower types and power line data of various line types, the data is comprehensive, and the point cloud classifier trained by means of the sample point cloud data has a high accuracy and is unlikely to make mistakes. In addition, after a classification result is obtained by means of automatic classification, speckle combination and optimization are carried out, and optimization is carried out according to a pole tower position file and a pre-set optimization rule, so as to further improve the classification accuracy.
机译:激光雷达点云数据分类方法,装置和装置以及存储介质。该方法包括:获取样本点云数据和要分类的激光雷达点云数据(101);根据样本点云数据建立点云分类器(102);通过点云分类器(103)对激光雷达点云数据进行分类。根据该方法,通过采样点云数据训练浊点分类器,并通过点云分类器对待分类的激光雷达点云数据进行自动分类,从而大大减少了分类过程中的人工干预成分,自动化程度高,成本低。用于训练点云分类器的样本点云数据涵盖了各种塔型的杆塔数据和各种线型的电力线数据,数据比较全面,并且通过样本点云数据训练的点云分类器具有较高的准确性,并且不太可能犯错误。另外,在通过自动分类获得分类结果之后,进行斑点组合和优化,并根据极塔位置文件和预设的优化规则进行优化,以进一步提高分类效果。准确性。

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