| 基于特征空间匹配的激光雷达点云配准算法 |
| |
| 引用本文： | 陈 强,岳东杰,陈 健.基于特征空间匹配的激光雷达点云配准算法[J].大地测量与地球动力学,2020,40(12):1303-1307. |
| |
| 作者姓名： | 陈 强 岳东杰 陈 健 |
| |
| 摘 要： | 针对传统基于特征的粗配准效率低、误匹配较多的不足，提出一种基于特征空间匹配的配准方法。利用简化的PointNet模型实现特征空间的提取，以优化的点云PPF信息作为输入，根据提取的特征空间向量计算欧氏距离以筛选匹配点，通过RANSAC剔除误匹配点对完成粗配准，利用ICP实现精配准。实验结果表明，本文算法相比FPFH和SHOT算法与ICP结合可有效提升配准效率，且配准结果的均方根误差较小。
|
| 关 键 词： | 三维扫描 点云配准 PointNet模型 随机采样一致性 迭代最近点算法 |
Laser LiDAR Point Cloud Registration Algorithm Based on Feature Space Matching |
| |
| CHEN Qiang,YUE Dongjie,CHEN Jian.Laser LiDAR Point Cloud Registration Algorithm Based on Feature Space Matching[J].Journal of Geodesy and Geodynamics,2020,40(12):1303-1307. |
| |
| Authors: | CHEN Qiang YUE Dongjie CHEN Jian |
| |
| Abstract: | Aiming at the shortcomings of traditional feature-based coarse registration with low efficiency and many mismatches, we propose a registration method based on feature space matching. We extract the feature space using a simplified PointNet model. We take optimized point cloud PPF information as input and calculate the Euclidean distance according to the extracted feature space vector to filter out matching points. We eliminate the mismatched points to complete the coarse registration through RANSAC, and use ICP to realize fine registration. The results show that the proposed algorithm combined with ICP greatly improves the registration efficiency compared with FPFH and SHOT algorithm, and RMSE of the registration result is smaller. |
| |
| Keywords: | 3D scanning point cloud registration PointNet model random sample consensus iterative closest point algorithm |
|
| 点击此处可从《大地测量与地球动力学》浏览原始摘要信息 |
| 点击此处可从《大地测量与地球动力学》下载免费的PDF全文 |
