一、介绍
In this tutorial we learn how to use a RandomSampleConsensus with a plane model to obtain the cloud fitting to this model.
pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud));
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud));
pcl::SACSegmentation<pcl::PointXYZ> seg;
pcl::ModelOutlierRemoval<pcl::PointXYZ> sphere_filter;
二、代码
#include <iostream>
#include <thread>
#include <pcl/point_types.h>
#include <pcl/common/io.h>
#include <pcl/point_cloud.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
#include <pcl/visualization/pcl_visualizer.h>
using namespace std::chrono_literals;
pcl::visualization::PCLVisualizer::Ptr simpleVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
viewer->setBackgroundColor(0, 0, 0);
viewer->addPointCloud<pcl::PointXYZ>(cloud, "sample cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
viewer->addCoordinateSystem (1.0, "global");
viewer->initCameraParameters();
return (viewer);
}
void ranFit()
{
bool is_plane = true;
bool is_show_final = false;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr final(new pcl::PointCloud<pcl::PointXYZ>);
cloud->width = 500;
cloud->height = 1;
cloud->is_dense = false;
cloud->points.resize(cloud->width * cloud->height);
for (int i = 0; i < cloud->size(); ++i)
{
if (is_plane==false)
{
// x*x+y*y+z*z=1
(*cloud)[i].x = 2.0 * rand() / (RAND_MAX + 1.0) - 1.0;
(*cloud)[i].y = 2.0 * rand() / (RAND_MAX + 1.0) - 1.0;
if ((*cloud)[i].x * (*cloud)[i].x + ((*cloud)[i].y * (*cloud)[i].y) > 1)
(*cloud)[i].z = 2.0 * rand() / (RAND_MAX + 1.0) - 1.0;
else if (i % 2 == 0)
(*cloud)[i].z = sqrt(1 - ((*cloud)[i].x * (*cloud)[i].x) - ((*cloud)[i].y * (*cloud)[i].y));
else
(*cloud)[i].z = -sqrt(1 - ((*cloud)[i].x * (*cloud)[i].x) - ((*cloud)[i].y * (*cloud)[i].y));
}
else
{
// 0.5x+0.5y-z=0
(*cloud)[i].x = rand() / (RAND_MAX + 1.0);
(*cloud)[i].y = rand() / (RAND_MAX + 1.0);
if (i % 3 == 0)
(*cloud)[i].z = rand() / (RAND_MAX + 1.0);
else
(*cloud)[i].z = 0.5 * (*cloud)[i].x + 0.5 * (*cloud)[i].y;
}
}
std::vector<int> inliers;
std::vector<int> outliers;
pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud));
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud));
Eigen::VectorXf coef;
if (is_plane)
{
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p);
ransac.setDistanceThreshold(.01);
ransac.computeModel();
ransac.getInliers(inliers);
ransac.getModelCoefficients(coef);
}
else
{
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_s);
ransac.setDistanceThreshold(.01);
ransac.computeModel();
ransac.getInliers(inliers);
ransac.getModelCoefficients(coef);
}
std::cout << coef << std::endl;
pcl::copyPointCloud(*cloud, inliers, *final);
pcl::visualization::PCLVisualizer::Ptr viewer;
if (is_show_final)
viewer = simpleVis(final);
else
viewer = simpleVis(cloud);
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
std::this_thread::sleep_for(100ms);
}
}
int main()
{
ranFit();
return (0);
}
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <thread>
using namespace std::chrono_literals;
pcl::visualization::PCLVisualizer::Ptr simpleView(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
viewer->setBackgroundColor(0, 0, 0);
viewer->addPointCloud<pcl::PointXYZ>(cloud, "sample cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
viewer->addCoordinateSystem(1.0, "global");
viewer->initCameraParameters();
return viewer;
}
void planeSeg()
{
// 生成输入点云数据
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 = 10 * rand() / (RAND_MAX + 1.0f);
point.y = 10 * rand() / (RAND_MAX + 1.0f);
point.z = 1.0;
}
(*cloud)[0].z = -2.0;
(*cloud)[3].z = 2.0;
(*cloud)[6].z = 4.0;
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); // 平面模型
seg.setMethodType(pcl::SAC_RANSAC); // 随机采样一致算法
seg.setDistanceThreshold(0.1); // 距离门限
seg.setInputCloud(cloud);
seg.segment(*inliers, *coefficients); // 切割
// 打印模型参数
std::cout << "Model coefficients: " << coefficients->values[0]
<< " " << coefficients->values[1] << " " << coefficients->values[2]
<< " " << coefficients->values[3] << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr final(new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud(*cloud, inliers->indices, *final); // 过滤非平面点
pcl::visualization::PCLVisualizer::Ptr viewer = simpleView(final);
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
std::this_thread::sleep_for(100ms);
}
}#include <iostream>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/filters/model_outlier_removal.h>
void sphereFilter()
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr filtered(new pcl::PointCloud<pcl::PointXYZ>);
std::size_t noise_size = 5;
std::size_t sphere_data_size = 10;
cloud->width = noise_size + sphere_data_size;
cloud->height = 1;
cloud->points.resize(15 * 1);
for (std::size_t i = 0; i < noise_size; ++i)
{
(*cloud)[i].x = rand() / (RAND_MAX + 1.0) * 2.0 - 1.0;
(*cloud)[i].y = rand() / (RAND_MAX + 1.0) * 2.0 - 1.0;
(*cloud)[i].z = rand() / (RAND_MAX + 1.0) * 2.0 - 1.0;
}
double rand_x1 = 1;
double rand_x2 = 1;
for (std::size_t i = noise_size; i < (noise_size + sphere_data_size); ++i)
{
while (pow(rand_x1, 2) + pow(rand_x2, 2) >= 1)
{
rand_x1 = rand() / (RAND_MAX + 1.0) * 2.0 - 1.0; // -1 ~ 1
rand_x2 = rand() / (RAND_MAX + 1.0) * 2.0 - 1.0; // -1 ~ 1
}
double pre_calc = sqrt(1 - pow(rand_x1, 2) - pow(rand_x2, 2));
(*cloud)[i].x = 2 * rand_x1 * pre_calc;
(*cloud)[i].y = 2 * rand_x2 * pre_calc;
(*cloud)[i].z = 1 - 2 * (pow(rand_x1, 2) + pow(rand_x2, 2));
rand_x1 = 1;
rand_x2 = 1;
}
std::cerr << "Cloud before filtering: " << std::endl;
for (const auto& point : *cloud)
std::cout << " " << point.x << " " << point.y << " " << point.z << std::endl;
pcl::ModelCoefficients sphere_coeff;
sphere_coeff.values.resize(4);
sphere_coeff.values[0] = 0; // center_x
sphere_coeff.values[1] = 0; // center_y
sphere_coeff.values[2] = 0; // center_z
sphere_coeff.values[3] = 1; // radius
pcl::ModelOutlierRemoval<pcl::PointXYZ> sphere_filter;
sphere_filter.setModelCoefficients(sphere_coeff);
sphere_filter.setThreshold(0.005);
sphere_filter.setModelType(pcl::SACMODEL_SPHERE); // 球体
sphere_filter.setInputCloud(cloud);
sphere_filter.filter(*filtered);
std::cerr << "Sphere after filtering: " << std::endl;
for (const auto& point : *filtered)
std::cout << " " << point.x << " " << point.y << " " << point.z << std::endl;
}三、参考
How to use Random Sample Consensus model — Point Cloud Library 0.0 documentation
Plane model segmentation — Point Cloud Library 0.0 documentation
Filtering a PointCloud using ModelOutlierRemoval — Point Cloud Library 0.0 documentation