我有一个点云库功能，可以检测点云中最大的平面。这很好用。现在，我想扩展此功能以分割云中的每个平面并将这些点复制到新的云（例如，房间地板上有一个球体的场景将给我地板和墙壁，但不是球体，因为它不是平面的）。如何扩展以下代码以获取所有飞机，而不仅仅是最大的飞机？（运行时间是这里的一个因素，所以我不希望只是在循环中运行相同的代码，每次都剥离新的最大平面）
int
main(int argc, char** argv)
{
pcl::visualization::CloudViewer viewer("viewer1");
pcl::PCLPointCloud2::Ptr cloud_blob(new pcl::PCLPointCloud2), cloud_filtered_blob(new pcl::PCLPointCloud2);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>), cloud_p(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);
// Fill in the cloud data
pcl::PCDReader reader;
reader.read("clouds/table.pcd", *cloud_blob);
// Create the filtering object: downsample the dataset using a leaf size of 1cm
pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
sor.setInputCloud(cloud_blob);
sor.setLeafSize(0.01f, 0.01f, 0.01f);
sor.filter(*cloud_filtered_blob);
// Convert to the templated PointCloud
pcl::fromPCLPointCloud2(*cloud_filtered_blob, *cloud_filtered);
std::cerr << "PointCloud after filtering: " << cloud_filtered->width * cloud_filtered->height << " data points." << std::endl;
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients());
pcl::PointIndices::Ptr inliers(new pcl::PointIndices());
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZ> seg;
// Optional
seg.setOptimizeCoefficients(true);
seg.setModelType(pcl::SACMODEL_PLANE);
seg.setMethodType(pcl::SAC_RANSAC);
seg.setMaxIterations(1000);
seg.setDistanceThreshold(0.01);
// Create the filtering object
pcl::ExtractIndices<pcl::PointXYZ> extract;
int i = 0, nr_points = (int)cloud_filtered->points.size();
// While 30% of the original cloud is still there
while (cloud_filtered->points.size() > 0.3 * nr_points)
{
// Segment the largest planar component from the remaining cloud
seg.setInputCloud(cloud_filtered);
pcl::ScopeTime scopeTime("Test loop");
{
seg.segment(*inliers, *coefficients);
}
if (inliers->indices.size() == 0)
{
std::cerr << "Could not estimate a planar model for the given dataset." << std::endl;
break;
}
// Extract the inliers
extract.setInputCloud(cloud_filtered);
extract.setIndices(inliers);
extract.setNegative(false);
extract.filter(*cloud_p);
std::cerr << "PointCloud representing the planar component: " << cloud_p->width * cloud_p->height << " data points." << std::endl;
}
viewer.showCloud(cloud_p, "viewer1");
while (!viewer.wasStopped()) {}
return (0);
}