PCL点云平面分割
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2024-03-25 10:53:46
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PCL点云平面分割
多平面分割方法封装
/** \brief 多平面分割
* \param[in] cloud_in 待分割点云
* \param[in] maxiter 最大迭代次数
* \param[in] dist 判断是否为平面内点的距离阈值
* \param[in] proportion 非平面点所占点云比例,[0,1]之间取值
* \param[out] out_plane_vect 分割后的平面模型点云集合
* \return 0成功,-1失败
*/
int g_multPlaneSeg(pcl::PointCloud<pcl::PointXYZ>::Ptr & cloud_in, int maxiter, double dist, float proportion, std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr>& out_plane_vect)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloudXYZ(new pcl::PointCloud<pcl::PointXYZ>());
pcl::copyPointCloud(*cloud_in, *cloudXYZ);
std::vector<int> mapping;
pcl::removeNaNFromPointCloud(*cloudXYZ, *cloudXYZ, mapping);
out_plane_vect.clear();
//平面提取,获取平面方程
pcl::SACSegmentation<pcl::PointXYZ> seg;
seg.setModelType(pcl::SACMODEL_PLANE);//平面模型
seg.setOptimizeCoefficients(true); //设置对估计的模型参数进行优化处理
seg.setMethodType(pcl::SAC_RANSAC); // 设置用哪个随机参数估计方法,分割方法:随机采样法
seg.setMaxIterations(maxiter);//设置最大迭代次数
seg.setDistanceThreshold(dist);//设置判断是否为模型内点的距离阈值
size_t nr_points = cloudXYZ->points.size();
while (cloudXYZ->points.size() > proportion * nr_points)
{
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
pcl::PointIndices::Ptr inliersIndices(new pcl::PointIndices());
seg.setInputCloud(cloudXYZ);
seg.segment(*inliersIndices, *coefficients);
if (inliersIndices->indices.size() == 0)//没有提取出点
{
break;
}
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_inliers(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_outliers(new pcl::PointCloud<pcl::PointXYZ>);
pcl::ExtractIndices<pcl::PointXYZ> extract; //创建点云提取对象
extract.setInputCloud(cloudXYZ);//设置输入点云
extract.setIndices(inliersIndices);
extract.setNegative(false); //设置提取内点而非外点
extract.filter(*cloud_inliers);//提取输出存储到cloud_inliers
extract.setNegative(true);//提取外点
extract.filter(*cloud_outliers);
*cloudXYZ = *cloud_outliers;
out_plane_vect.push_back(cloud_inliers);
}
if (out_plane_vect.size() == 0)
{
return -1;
}
return 0;
}
调用如下
int main()
{
std::string filename_ = "bunny.pcd";
pcl::console::TicToc timecal;
timecal.tic();
pcl::PointCloud<pcl::PointXYZ>::Ptr model(new pcl::PointCloud<pcl::PointXYZ>());
if (pcl::io::loadPCDFile(filename_, *model) < 0)
{
std::cout << "Error loading model cloud." << std::endl;
return (-1);
}
std::cout << "加载点云成功.耗时:" << timecal.toc() << "ms" << std::endl;
//速度慢降采样
pcl::VoxelGrid<pcl::PointXYZ> vg;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
vg.setInputCloud(model);
vg.setLeafSize(0.005f, 0.005f, 0.005f);
vg.filter(*cloud_filtered);
timecal.tic();
std::cout << "开始提取平面" << std::endl;
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> outPlaneVect;
g_multPlaneSeg(cloud_filtered, 10000, 0.02, 0.2, outPlaneVect);
std::cout << "结束提取平面" << std::endl;
std::cout << "提取平面个数:" << outPlaneVect.size() <<"。耗时:"<< timecal.toc() << "ms" << std::endl;
// visualization
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("Viewer"));
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_color(new pcl::PointCloud<pcl::PointXYZRGB>());
srand(time(nullptr));
std::vector<unsigned char> color;
for (size_t i = 0; i < outPlaneVect.size(); i++)
{
color.push_back(static_cast<unsigned char>(rand() % 256));
color.push_back(static_cast<unsigned char>(rand() % 256));
color.push_back(static_cast<unsigned char>(rand() % 256));
}
int color_index(0);
for (size_t i = 0; i < outPlaneVect.size(); i++)
{
for (size_t j = 0; j < outPlaneVect[i]->points.size(); j++)
{
if (pcl_isnan(outPlaneVect[i]->points[j].x))
{
continue;
}
pcl::PointXYZRGB n_points;
n_points.x = outPlaneVect[i]->points[j].x;
n_points.y = outPlaneVect[i]->points[j].y;
n_points.z = outPlaneVect[i]->points[j].z;
n_points.r = color[3 * color_index];
n_points.g = color[3 * color_index + 1];
n_points.b = color[3 * color_index + 2];
cloud_color->push_back(n_points);
}
color_index++;
}
viewer->addPointCloud(cloud_color);
viewer->spin();
system("pause");
return 0;
}