基于二维正态分布的无人机激光雷达点云匹配研究
Research on UAV lidar point cloud matching based on two-dimensional normal distribution
任娜 1张玉 2王洪江 1张楠1
作者信息
- 1. 沈阳工程学院信息学院,沈阳 110136
- 2. 东北林业大学信息学院,哈尔滨 150006
- 折叠
摘要
无人机激光雷达点云特征多,一次匹配花时间长,难以进行二次匹配,因此研究基于二维正态分布的无人机激光雷达点云匹配方法.采集无人机激光雷达点云图像,通过旋转平移方法、双边滤波方法对图像预处理,利用二维正态分布算法和动态时间规整算法完成点云特征提取,使用初始变换矩阵估计算法对点云进行粗匹配,再使用近点迭代算法进行点云快速精匹配,通过两次匹配实现无人机激光雷达点云快速匹配.实验结果表明,所提方法的无人机激光雷达点云图像去噪效果好,点云匹配时间短,匹配偏差仅在0.04 m-0.15 m之间,匹配精度达到了相关预期.
Abstract
UAV lidar point cloud has many features,and the first matching takes a long time,so it is difficult to carry out the second matching.Therefore,the UAV lidar point cloud matching method based on two-dimensional nor-mal distribution is studied.Collect the UAV lidar point cloud image,preprocess the image through the rotation transla-tion method and Bilateral filter method,use the two-dimensional normal distribution algorithm and the dynamic time warping algorithm to complete the point cloud feature extraction,use the initial transformation matrix estimation algo-rithm to rough match the point cloud,and then use the near point iteration algorithm to fast and fine match the point cloud,and realize the UAV lidar point cloud fast matching through two matching.The experimental results show that the proposed method has good denoising effect on unmanned aerial vehicle LiDAR point cloud images,short point cloud matching time,and a matching deviation of only 0.04 m-0.15 m.The matching accuracy has met the relevant expectations.
关键词
二维正态分布/无人机激光雷达/点云匹配/旋转平移方法/点云特征/粗匹配/精匹配Key words
two-dimensional normal distribution/drone LiDAR/point cloud matching/rotation translation meth-od/point cloud features/rough matching/precise matching引用本文复制引用
基金项目
辽宁省教育厅科学研究经费项目(面上项目)(LJKZ1086)
辽宁省应用基础研究计划(2022JH2/101300134)
辽宁省应用基础研究计划(2023JH2/101300065)
出版年
2024
NETL
NSTL
万方数据