摘要
提取建筑物线性特征时,由于未对采集到的建筑物点云数据预处理,导致最终提取的建筑物线性特征存在完整度差、提取正确率低的问题,对此,提出装配式建筑平面激光点云数据线性特征提取方法。通过内设激光测距系统获取建筑物在三维向量的有效数据,采用非接触式几何支撑系统建立基于有效数据的空间向量模型,将该模型输入三维激光扫描仪的目标物体表面校正系统中,获取到建筑物的整体点云数据。利用Otsu目标检测法对获取的整体点云数据进行优化处理,采用改进IFFT算法建立基于优化数据的物像空间位姿表征模型,利用连续投影算法提取网格内部特征点并连接成线,完成建筑物点云数据线性特征的提取。实验结果表明,所提方法的装配式建筑平面激光点云数据线性特征提取清晰度高、完整度好,且建筑物线性特征的提取正确率最高可达100%,说明该方法具有实用性。
When building linear features are extracted,because the collected building point cloud data is not preprocessed,the final extracted building linear features have problems of poor integrity and low extraction accuracy.Therefore,a linear feature extraction method for assembly building plane laser point cloud data is proposed.The effective data of the building in the three-dimensional vector is obtained through the built-in laser ranging system.The non-contact geometric support system is used to establish the spatial vector model based on the effective data.The model is input into the target object surface correction system of the three-dimensional laser scanner to obtain the overall point cloud data of the building.The Otsu target detection method is used to optimize the overall point cloud data obtained.The improved IFFT algorithm is used to establish the object image spatial pose representation model based on the optimized data.The continuous projection algorithm is used to extract the internal feature points of the grid and connect them into lines,completing the extraction of the linear features of the building point cloud data.The experimental results show that the proposed method has high definition,good integrity,and the highest accuracy of building linear feature extraction can reach 100%,which shows that the proposed method is practical.
作者
陆艳侠
LU Yan-xia(Urban Construction College,Lu'an Vocational and Technical College,Anhui Lu'an 237158,China)
出处
《齐齐哈尔大学学报（自然科学版）》
2023年第5期57-62,共6页
Journal of Qiqihar University(Natural Science Edition)
基金
安徽省教育厅资助项目“2019年高校优秀青年骨干人才国内访学研修”(gxgnfx2019119)
安徽省质量工程项目“《建筑施工技术》高水平高职教材建设项目”(2021gspjc042)。
关键词
三维激光扫描技术
点云数据
粗差剔除
IFFT算法
线性特征提取
3D laser scanning technology
point cloud data
gross error elimination
IFFT algorithm
linear feature extraction
