python点云配准
时间: 2023-06-21 08:21:55 浏览: 135
Python中有很多点云配准的库,常用的有Open3D、Pyntcloud、PyVista等。这些库都提供了点云配准的API,可以方便地实现点云的配准功能。
下面简单介绍一下Open3D库实现点云配准的方法:
1. 加载点云数据
```python
import open3d as o3d
source = o3d.io.read_point_cloud("source.ply")
target = o3d.io.read_point_cloud("target.ply")
```
2. 对点云进行下采样和法向量估计
```python
source_down = source.voxel_down_sample(voxel_size=0.05)
source_down.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
target_down = target.voxel_down_sample(voxel_size=0.05)
target_down.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
```
3. 计算FPFH特征
```python
source_fpfh = o3d.pipelines.registration.compute_fpfh_feature(source_down, o3d.geometry.KDTreeSearchParamHybrid(radius=0.25, max_nn=100))
target_fpfh = o3d.pipelines.registration.compute_fpfh_feature(target_down, o3d.geometry.KDTreeSearchParamHybrid(radius=0.25, max_nn=100))
```
4. 进行配准
```python
threshold = 0.02
trans_init = np.eye(4)
reg_p2p = o3d.pipelines.registration.registration_icp(source_down, target_down, threshold, trans_init, o3d.pipelines.registration.TransformationEstimationPointToPoint(), o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=200))
```
5. 可视化结果
```python
source.transform(reg_p2p.transformation)
o3d.visualization.draw_geometries([source, target])
```
这样就可以实现简单的点云配准了。需要注意的是,不同的点云库实现方法略有不同,但基本的配准流程是相似的。