文章目录
下列中，先随机创建了z=1.0的随机点，然后改变其中3个点的z值。最后，使用SACMODEL_PLANE平面模型对它进行拟合。
#include
#include
#include
#include
#include
#include
#include
int main(int argc, char **argv)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
cloud->width = 15;
cloud->height = 1;
cloud->points.resize(cloud->width * cloud->height);
for (auto &point : *cloud)
{
point.x = 1024 * rand() / (RAND_MAX + 1.0f);
point.y = 1024 * rand() / (RAND_MAX + 1.0f);
point.z = 1.0;
}
(*cloud)[0].z = 2.0;
(*cloud)[3].z = -2.0;
(*cloud)[6].z = 4.0;
std::cerr << "Point cloud data: " << cloud->size() << " points" << std::endl;
for (const auto &point : *cloud)
std::cerr << " " << point.x << " "
<< point.y << " "
<< point.z << std::endl;
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
pcl::SACSegmentation<pcl::PointXYZ> seg;
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_PLANE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setDistanceThreshold(0.01);
seg.setInputCloud(cloud);
seg.segment(*inliers, *coefficients);
if (inliers->indices.size() == 0)
{
PCL_ERROR("Could not estimate a planar model for the given dataset.");
return (-1);
}
std::cerr << "Model coefficients: " << coefficients->values[0] << " "
<< coefficients->values[1] << " "
<< coefficients->values[2] << " "
<< coefficients->values[3] << std::endl;
std::cerr << "Model inliers: " << inliers->indices.size() << std::endl;
for (size_t i = 0; i < inliers->indices.size (); ++i)
std::cerr << inliers->indices[i] << " " << cloud->points[inliers->indices[i]].x << " "
<< cloud->points[inliers->indices[i]].y << " "
<< cloud->points[inliers->indices[i]].z << std::endl;
return (0);
}
输出：
Point cloud data: 15 points
0.352222 -0.151883 2
-0.106395 -0.397406 1
-0.473106 0.292602 1
-0.731898 0.667105 -2
0.441304 -0.734766 1
0.854581 -0.0361733 1
-0.4607 -0.277468 4
-0.916762 0.183749 1
0.968809 0.512055 1
-0.998983 -0.463871 1
0.691785 0.716053 1
0.525135 -0.523004 1
0.439387 0.56706 1
0.905417 -0.579787 1
0.898706 -0.504929 1
Model coefficients: 0 0 1 -1
Model inliers: 12
1 -0.106395 -0.397406 1
2 -0.473106 0.292602 1
4 0.441304 -0.734766 1
5 0.854581 -0.0361733 1
7 -0.916762 0.183749 1
8 0.968809 0.512055 1
9 -0.998983 -0.463871 1
10 0.691785 0.716053 1
11 0.525135 -0.523004 1
12 0.439387 0.56706 1
13 0.905417 -0.579787 1
14 0.898706 -0.504929 1
- 圆柱分割
下例先使用平面分割出平面，使用的是SACMODEL_NORMAL_PLANE，模型约束平面的法向方向，针对复杂的平面，可以更准确分割出平面点云，与SACMODEL_PLANE不同。最后针对剩余的点云进行圆柱分割。
下列中保存了两份分割点云： 平面点云和圆柱点云。
#include
#include
#include
#include
#include
#include
#include
#include
#include
typedef pcl::PointXYZ PointT;
int main(int argc, char **argv)
{
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>());
pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2(new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2(new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane(new pcl::ModelCoefficients), coefficients_cylinder(new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane(new pcl::PointIndices), inliers_cylinder(new pcl::PointIndices);
reader.read("../../pcd/table_scene_mug_stereo_textured.pcd", *cloud);
std::cerr << "PointCloud has: " << cloud->size() << " data points." << std::endl;
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(0, 1.5);
pass.filter(*cloud_filtered);
std::cerr << "PointCloud after filtering has: " << cloud_filtered->size() << " data points." << std::endl;
ne.setSearchMethod(tree);
ne.setInputCloud(cloud_filtered);
ne.setKSearch(50);
ne.compute(*cloud_normals);
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_NORMAL_PLANE);
seg.setNormalDistanceWeight(0.1);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setMaxIterations(100);
seg.setDistanceThreshold(0.03);
seg.setInputCloud(cloud_filtered);
seg.setInputNormals(cloud_normals);
seg.segment(*inliers_plane, *coefficients_plane);
std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
extract.setInputCloud(cloud_filtered);
extract.setIndices(inliers_plane);
extract.setNegative(false);
pcl::PointCloud<PointT>::Ptr cloud_plane(new pcl::PointCloud<PointT>());
extract.filter(*cloud_plane);
std::cerr << "PointCloud representing the planar component: " << cloud_plane->size() << " data points." << std::endl;
writer.write("../../pcd/table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);
extract.setNegative(true);
extract.filter(*cloud_filtered2);
extract_normals.setNegative(true);
extract_normals.setInputCloud(cloud_normals);
extract_normals.setIndices(inliers_plane);
extract_normals.filter(*cloud_normals2);
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_CYLINDER);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setNormalDistanceWeight(0.1);
seg.setMaxIterations(10000);
seg.setDistanceThreshold(0.05);
seg.setRadiusLimits(0, 0.1);
seg.setInputCloud(cloud_filtered2);
seg.setInputNormals(cloud_normals2);
seg.segment(*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
extract.setInputCloud(cloud_filtered2);
extract.setIndices(inliers_cylinder);
extract.setNegative(false);
pcl::PointCloud<PointT>::Ptr cloud_cylinder(new pcl::PointCloud<PointT>());
extract.filter(*cloud_cylinder);
if (cloud_cylinder->points.empty())
std::cerr << "Can't find the cylindrical component." << std::endl;
else
{
std::cerr << "PointCloud representing the cylindrical component: " << cloud_cylinder->size() << " data points." << std::endl;
writer.write("../../pcd/table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
}
return (0);
}
原始点云为：
分割出来的圆柱为：
分割出来的平面为：
-
欧式聚类分割
-
欧式聚类原理：
- 找到空间中某点p 0 p_0 p 0 ，有kdTree找到离它
最近的n个点，判断这n个点到p 0 p_0 p 0 的距离。将距离小于阈值r的点p 1 , p 2 , p 3 , p 4 . . . . p_1,p_2,p_3,p_4….p 1 ,p 2 ,p 3 ,p 4 ….放在类Q里。 - 对Q中的剩余点
重复以上步骤，并将满足条件的点放入Q。 - 当Q中再也不能有新的点加入时，则完成搜索。
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:21f0ada3-1a84-49a2-90e6-56836b659bb9
[En]
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:d333cff9-acb3-4abd-83a8-094d76ad3c2d
-
首先对原始点云做下采样，体素大小为0.01.
-
然后采样点云做平面滤波，使用的是SAC_RANSAC平面滤波，直到总量小于原始的0.3倍之后才不做平面滤波
- 对平面滤波剩余的点云进行欧式距离分割。
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
int main(int argc, char **argv)
{
pcl::PCDReader reader;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);
reader.read("../../pcd/table_scene_lms400.pcd", *cloud);
std::cout << "PointCloud before filtering has: " << cloud->size() << " data points." << std::endl;
pcl::VoxelGrid<pcl::PointXYZ> vg;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
vg.setInputCloud(cloud);
vg.setLeafSize(0.01f, 0.01f, 0.01f);
vg.filter(*cloud_filtered);
std::cout << "PointCloud after filtering has: " << cloud_filtered->size() << " data points." << std::endl;
pcl::SACSegmentation<pcl::PointXYZ> seg;
pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane(new pcl::PointCloud<pcl::PointXYZ>());
pcl::PCDWriter writer;
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_PLANE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setMaxIterations(100);
seg.setDistanceThreshold(0.02);
int i = 0, nr_points = (int)cloud_filtered->size();
while (cloud_filtered->size() > 0.3 * nr_points)
{
seg.setInputCloud(cloud_filtered);
seg.segment(*inliers, *coefficients);
if (inliers->indices.size() == 0)
{
std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
break;
}
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud(cloud_filtered);
extract.setIndices(inliers);
extract.setNegative(false);
extract.filter(*cloud_plane);
std::cout << "PointCloud representing the planar component: " << cloud_plane->size() << " data points." << std::endl;
extract.setNegative(true);
extract.filter(*cloud_f);
*cloud_filtered = *cloud_f;
}
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud(cloud_filtered);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance(0.02);
ec.setMinClusterSize(100);
ec.setMaxClusterSize(25000);
ec.setSearchMethod(tree);
ec.setInputCloud(cloud_filtered);
ec.extract(cluster_indices);
int j = 0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin(); it != cluster_indices.end(); ++it)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(new pcl::PointCloud<pcl::PointXYZ>);
for (std::vector<int>::const_iterator pit = it->indices.begin(); pit != it->indices.end(); ++pit)
cloud_cluster->push_back((*cloud_filtered)[*pit]);
cloud_cluster->width = cloud_cluster->size();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
std::cout << "PointCloud representing the Cluster: " << cloud_cluster->size() << " data points." << std::endl;
std::stringstream ss;
ss << "cloud_cluster_" << j << ".pcd";
writer.write<pcl::PointXYZ>(ss.str(), *cloud_cluster, false);
j++;
}
return (0);
}
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:b35073d2-c990-442f-acea-5863dd8dc6a6
[En]
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:d3818eae-49bc-49c7-b5c8-1dfb684d28d0
- 原始点为：460400个，下采样之后为：41049
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:ec87e78f-33c2-47bb-bad3-3844a462aaf8[En]
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:6b19f7b0-75b3-459d-9292-fbe904e80d0b
- 欧式聚类一共分割出5个目标，点云数分别为：4857、1386、321、291和123
PointCloud before filtering has: 460400 data points.PointCloud after filtering has: 41049 data points.PointCloud representing the planar component: 20536 data points.PointCloud representing the planar component: 20536 data points.PointCloud representing the Cluster: 4857 data points.PointCloud representing the Cluster: 1386 data points.PointCloud representing the Cluster: 321 data points.PointCloud representing the Cluster: 291 data points.PointCloud representing the Cluster: 123 data points.原始pcd点云如下：
欧式聚类分割之后的5个目标点云如下：
- 第一个聚类目标点云
- 第二个聚类目标点云
- 第三个聚类目标点云
- 第四个聚类目标点云
- 第五个聚类目标点云
Original: https://blog.csdn.net/QLeelq/article/details/122311773
Author: 非晚非晚
Title: 【点云处理技术之PCL】点云分割算法1——平面模型分割、圆柱模型分割和欧式聚类提取(含欧式聚类原理)
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/560894/
转载文章受原作者版权保护。转载请注明原作者出处！