[点云分割] 圆柱体分割

与平面分割类似，关于理论的东西不多阐释了，直接上代码：

#include 
#include 
 
#include  // 模型系数的定义
#include 
#include  // 各种点云数据类型
#include  // 包含用于采样一致性算法的不同方法的定义，如RANSAC、MSAC等
#include  // 包含用于采样一致性算法的不同模型的定义，如平面、球体、圆柱体
#include  // 包含用于分割点云的采样一致性算法（SACSegmentation）的定义，用于识别点云的几何模型
#include  // 包含用于从点云中提取特定索引的函数和类，用于根据索引提取点云中的子集
#include  // 包含了用于可视化点云的函数和类，用于在3D视窗中现实点云数据
 
#include  // 估计法线
#include  // 直通滤波
 
 
typedef pcl::PointXYZ PointT;
 
int main(){
 
    // All the objectss needed
    pcl::PCDReader reader;
    pcl::PassThrough pass;
    pcl::NormalEstimation ne;
    pcl::SACSegmentationFromNormals seg;
 
    pcl::PCDWriter writer;
    pcl::ExtractIndices extract;
    pcl::ExtractIndices extract_normals;
    pcl::search::KdTree::Ptr tree (new pcl::search::KdTree());
 
 
    // Datasets
    pcl::PointCloud::Ptr cloud (new pcl::PointCloud);
    pcl::PointCloud::Ptr cloud_filtered (new pcl::PointCloud);
    pcl::PointCloud::Ptr cloud_filtered2 (new pcl::PointCloud);
 
    pcl::PointCloud::Ptr cloud_normals (new pcl::PointCloud);
    pcl::PointCloud::Ptr cloud_normals2 (new pcl::PointCloud);
 
    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);
 
    // Read in the cloud data
    reader.read("/home/lrj/work/pointCloudData/table_scene_mug_stereo_textured.pcd", *cloud);
    std::cerr << "PointCloud has: " << cloud->size() << " data points." << std::endl;
 
    // Build a passthrough filter to remove spurious NaNs and scene background
    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.\n";
 
    // Estimate point normals
    ne.setSearchMethod(tree);
    ne.setInputCloud(cloud_filtered);
    ne.setKSearch(50);
    ne.compute(*cloud_normals);
 
    // Create the segmentation object for the plannar model and set all the parameters
    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);
    // Obtain the plane inliers anda coefficients
    seg.segment(*inliers_plane, *coefficients_plane);
    std::cerr << "Plane coefficients: " << *coefficients_plane << std::endl;
 
    // Extract the planar inliers from the input cloud
    extract.setInputCloud(cloud_filtered);
    extract.setIndices(inliers_plane);
    extract.setNegative(false);
 
    // Wirte the planar inliers to disk
    pcl::PointCloud::Ptr cloud_plane(new pcl::PointCloud());
    extract.filter(*cloud_plane);
    std::cerr << "PointCloud representing the planar component: " << cloud_plane->size() << " data points." << std::endl;
    writer.write("/home/lrj/work/pointCloudData/table_scene_mug_stereo_textured_plane.pcd", *cloud_plane, false);
 
    // Remove the planar inliers, extract the rest
    extract.setNegative(true);
    extract.filter(*cloud_filtered2);
 
    extract_normals.setNegative(true);
    extract_normals.setIndices(inliers_plane);
    extract_normals.filter(*cloud_normals2);
 
    // Create the segmentation object for cylinder segmentation and set all the parameters
    seg.setOptimizeCoefficients(true);
    seg.setMethodType(pcl::SACMODEL_CYLINDER);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setNormalDistanceWeight(0.1);
    seg.setMaxIterations(10000);
    seg.setDistanceThreshold(0.05);
    seg.setRadiusLimits(0, 0.01);
    seg.setInputCloud(cloud_filtered2);
    seg.setInputNormals(cloud_normals2);
 
    // Obtain the cylinder inliers and coefficients
    seg.segment(*inliers_cylinder, *coefficients_cylinder);
    std::cerr << "Cylinder cofficients: " << *coefficients_cylinder << std::endl;
 
    // Write the cylinder inliers to disk
    extract.setInputCloud(cloud_filtered2);
    extract.setIndices(inliers_cylinder);
    extract.setNegative(false);
    pcl::PointCloud::Ptr cloud_cylinder (new pcl::PointCloud ());
    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.\n";
        writer.write("/home/lrj/work/pointCloudData/table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
    }
 
 
    return(0);
 
}