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作 者:石培杰 孟荣 赵智龙 张东坡 李焱 SHI Peijie;MENG Rong;ZHAO Zhilong;ZHANG Dongpo;LI Yan(State Grid Hebei Extra High Voltage Company,Shijiazhuang 050070,China)
机构地区:[1]国网河北省电力有限公司超高压分公司,河北石家庄050070
出 处:《河北电力技术》2025年第1期77-84,共8页Hebei Electric Power
基 金:国家电网有限公司科技项目(kj2023-014)。
摘 要:针对传统点云配准中存在精度差、计算效率低、易受噪声干扰等问题,提出了基于改进PointDSC和KD-ICP的变电站三维点云配准方法。首先,设计了变电站高精度三维点云数据采集系统,通过无人机和无人车搭载激光雷达系统获取变电站的点云数据,同时利用基于密度的空间聚类算法进行数据去噪处理。然后,采用快速点特征直方图进行数据的特征描述,并将其输入改进的PointDSC网络进行粗配准。最后,使用KD树优化迭代最近点算法,将其用于处理粗配准后的点云数据,从而实现精配准,得到一个准确拼接的变电站三维点云。基于采集到的变电站点云数据对所提方法进行试验验证,结果表明:配准结果与场景点云几乎重合,配准准确率均值和耗时分别为98.22%和2.49 s,能够满足变电站三维实时建模的需求。A three-dimensional(3D) point cloud registration method for substations based on improved PointDSC and KD-ICP is proposed to address the challenges of low accuracy,low computational efficiency,and susceptibility to noise interference in traditional point cloud registration.Firstly,a high-precision 3D point cloud data acquisition system for substations is designed.The point cloud data of substations is obtained by carrying a LiDAR system on unmanned aerial vehicles(UAVs) and unmanned vehicles.Meanwhile, a density based spatial clustering algorithm is utilized for data denoising.Then,the fast point feature histogram is used to describe the data features and input into the improved PointDSC network for coarse registration.Finally,the KD tree is used to optimize the Iterative Closest Point(ICP) algorithm,which is used to process the point cloud data after coarse registration,in order to achieve precise registration and obtain an accurately stitched 3D point cloud of the substation.Based on the collected substation point cloud data,the proposed method is verified by experiments.The results show that the registration results obtained almost overlapped with the scene point cloud and the average registration accuracy and time consumption are 98.22% and 2.49 s,respectively,which can meet the requirements of 3D real-time modeling of substations.
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