点对特征PPF算法详解
时间: 2023-05-22 07:01:06 浏览: 99
点对特征PPF算法是一种用于三维点云匹配的算法,它可以计算两个点云之间的相对姿态。该算法通过计算点对之间的特征描述符来实现匹配。具体来说,该算法首先对每个点计算其法向量和特征描述符,然后将每个点与其它点进行匹配,计算它们之间的相对姿态。最后,通过对所有匹配结果进行加权平均,得到两个点云之间的最终相对姿态。
相关问题
ppf算法的demo代码
PPF算法是一种模式匹配算法,常用于点云数据的匹配。以下是PPF算法的Python示例代码:
```python
import numpy as np
def compute_ppf(p1, n1, p2, n2):
d = p2 - p1
d /= np.linalg.norm(d)
normal_distance = np.dot(n1, n2)
rotation_angle = np.arccos(normal_distance)
rotation_axis = np.cross(n1, n2)
rotation_axis /= np.linalg.norm(rotation_axis)
ppf = np.concatenate([d, rotation_angle * rotation_axis])
return ppf
def ppf_matching(scene_points, model_points, scene_normals, model_normals, ppf_thresh=0.05):
model_size = len(model_points)
scene_size = len(scene_points)
model_ppfs = []
for i in range(model_size):
for j in range(i + 1, model_size):
model_ppfs.append(compute_ppf(model_points[i],model_normals[i],model_points[j],model_normals[j]))
scene_ppfs = []
for i in range(scene_size):
for j in range(i + 1, scene_size):
scene_ppfs.append(compute_ppf(scene_points[i],scene_normals[i],scene_points[j],scene_normals[j]))
matches = []
for i, model_ppf in enumerate(model_ppfs):
distances = [np.linalg.norm(model_ppf - scene_ppf) for scene_ppf in scene_ppfs]
min_distance_idx = np.argmin(distances)
if distances[min_distance_idx] < ppf_thresh:
matches.append((i, min_distance_idx))
return matches
```
ppf算法的测试代码
以下是一个使用ppf算法进行测试的示例代码:
```python
import numpy as np
from sklearn.neighbors import NearestNeighbors
def ppf_algorithm(points, normals):
# 计算点对特征
features = np.zeros((len(points), 4))
for i in range(len(points)):
p1 = points[i]
n1 = normals[i]
for j in range(len(points)):
if i != j:
p2 = points[j]
n2 = normals[j]
delta_p = p2 - p1
delta_n = n2 - n1
alpha = np.arctan2(delta_p[1], delta_p[0])
beta = np.arccos(delta_n.dot(delta_p) / (np.linalg.norm(delta_n) * np.linalg.norm(delta_p)))
features[i] += np.array([alpha, beta, delta_p[0], delta_p[1]])
features[i] /= len(points) - 1
# 使用KNN查找最近邻点对
nbrs = NearestNeighbors(n_neighbors=2, algorithm='kd_tree').fit(features)
distances, indices = nbrs.kneighbors(features)
return distances, indices
# 测试数据
points = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
normals = np.array([[1, 0], [0, 1], [-1, 0], [0, -1]])
distances, indices = ppf_algorithm(points, normals)
print("距离:", distances)
print("索引:", indices)
```