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.
二、代码
cpp
#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);
}
三、参考
How to use Random Sample Consensus model --- Point Cloud Library 0.0 documentation