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作 者:张子毅 孙焘 王琦 张宏博 程之恒 ZHANG Ziyi;SUN Tao;WANG Qi;ZHANG Hongbo;CHENG Zhiheng(School of Qilu Transportation,Shandong University,Jinan 250002,Shandong,China;Sinohydro Bureau 14 Co.,Ltd.,Kunming 650041,Yunnan,China)
机构地区:[1]山东大学齐鲁交通学院,山东济南250002 [2]中国水利水电第十四工程局有限公司,云南昆明650041
出 处:《山东大学学报(工学版)》2024年第5期155-162,共8页Journal of Shandong University(Engineering Science)
基 金:山东省重点研发计划资助项目(2020CXG010118)。
摘 要:提出一种基于激光雷达的水利点云建图方法。通过采集河段和闸口地形点云数据,选取适当的源点云和目标点云,利用坐标转换的粗配准和优化的迭代最近点的精配准算法,基于最小二乘法求解最优变换矩阵,实现数据转换和点云配准。经试验验证,本研究的配准方法能够完善点云信息,实现点云信息的互补,配准后的点云数量相对目标点云集增加39807个,探测距离相对目标点云集扩大20.87 m;在精配准方面,对比传统的迭代最近点和正态分布变换2种算法,配准时间分别降低31.63%和3.21%,配准精度误差较迭代最近点算法增加4.30%,较正态分布变换算法减小21.08%。This study proposed a LiDAR-based approach for mapping water-related structures through point cloud registration.Point cloud data was collected from river sections and sluices.Appropriate source and target point clouds were selected,and coarse registration was performed based on coordinate transformation.Precise registration was carried out using an optimized iterative closest point algorithm.The optimal transformation matrix was solved using the least squares method.These steps enabled the achievement of data transformation and point cloud registration.The experimental results showed that the registration method in this paper could improve the point cloud information and realize the complementarity of point cloud information.In the number of point clouds,the registered data increased by 39807 relatives to the target point cloud;in the detection distance and the relative target point cloud was expanded by 20.87 m.Compared with the traditional iterative closest point and normal distributions transform algorithms,the registration time was reduced by 31.63%and 3.21%,respectively.In terms of registration accuracy,the error was 4.30%higher than the iterative closest point algorithm and 21.08%lower than the normal distributions transform algorithm.
关 键 词:智慧水利 激光雷达 坐标转换 最小二乘法 迭代最近点
分 类 号:TN958.98[电子电信—信号与信息处理]
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