This tutorial exemplifies how to run a Sample Consensus segmentation for cylindrical models. To make the example a bit more practical, the following operations are applied to the input dataset (in order):
Note
The cylindrical model is not perfect due to the presence of noise in the data.
First, download the dataset table_scene_mug_stereo_textured.pcd and save it somewhere to disk.
Then, create a file, let’s say, cylinder_segmentation.cpp in your favorite editor, and place the following inside it:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | #include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
typedef pcl::PointXYZ PointT;
int
main (int argc, char** argv)
{
// All the objects needed
pcl::PCDReader reader;
pcl::PassThrough<PointT> pass;
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointT, pcl::Normal> seg;
pcl::PCDWriter writer;
pcl::ExtractIndices<PointT> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointT>::Ptr tree (new pcl::search::KdTree<PointT> ());
// Datasets
pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
pcl::PointCloud<PointT>::Ptr cloud_filtered (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointT>::Ptr cloud_filtered2 (new pcl::PointCloud<PointT>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
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 ("table_scene_mug_stereo_textured.pcd", *cloud);
std::cerr << "PointCloud has: " << cloud->points.size () << " data points." << std::endl;
// Build a passthrough filter to remove spurious NaNs
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 1.5);
pass.filter (*cloud_filtered);
std::cerr << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl;
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
ne.setKSearch (50);
ne.compute (*cloud_normals);
// Create the segmentation object for the planar 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 and 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);
// Write the planar inliers to disk
pcl::PointCloud<PointT>::Ptr cloud_plane (new pcl::PointCloud<PointT> ());
extract.filter (*cloud_plane);
std::cerr << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
writer.write ("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.setInputCloud (cloud_normals);
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.setModelType (pcl::SACMODEL_CYLINDER);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
seg.setInputCloud (cloud_filtered2);
seg.setInputNormals (cloud_normals2);
// Obtain the cylinder inliers and coefficients
seg.segment (*inliers_cylinder, *coefficients_cylinder);
std::cerr << "Cylinder coefficients: " << *coefficients_cylinder << std::endl;
// Write the cylinder inliers to disk
extract.setInputCloud (cloud_filtered2);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
pcl::PointCloud<PointT>::Ptr cloud_cylinder (new pcl::PointCloud<PointT> ());
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->points.size () << " data points." << std::endl;
writer.write ("table_scene_mug_stereo_textured_cylinder.pcd", *cloud_cylinder, false);
}
return (0);
}
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The only relevant lines are the lines below, as the other operations are already described in the other tutorials.
// Create the segmentation object for cylinder segmentation and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_CYLINDER);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setNormalDistanceWeight (0.1);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
As seen, we’re using a RANSAC robust estimator to obtain the cylinder coefficients, and we’re imposing a distance threshold from each inlier point to the model no greater than 5cm. In addition, we set the surface normals influence to a weight of 0.1, and we limit the radius of the cylindrical model to be smaller than 10cm.
Add the following lines to your CMakeLists.txt file:
1 2 3 4 5 6 7 8 9 10 11 12 | cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
project(cylinder_segmentation)
find_package(PCL 1.2 REQUIRED)
include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
add_executable (cylinder_segmentation cylinder_segmentation.cpp)
target_link_libraries (cylinder_segmentation ${PCL_LIBRARIES})
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After you have made the executable, you can run it. Simply do:
$ ./cylinder_segmentation
You will see something similar to:
PointCloud has: 307200 data points.
PointCloud after filtering has: 139897 data points.
[pcl::SACSegmentationFromNormals::initSACModel] Using a model of type: SACMODEL_NORMAL_PLANE
[pcl::SACSegmentationFromNormals::initSACModel] Setting normal distance weight to 0.100000
[pcl::SACSegmentationFromNormals::initSAC] Using a method of type: SAC_RANSAC with a model threshold of 0.030000
[pcl::SACSegmentationFromNormals::initSAC] Setting the maximum number of iterations to 100
Plane coefficients: header:
seq: 0
stamp: 0.000000000
frame_id:
values[]
values[0]: -0.0161854
values[1]: 0.837724
values[2]: 0.545855
values[3]: -0.528787
PointCloud representing the planar component: 117410 data points.
[pcl::SACSegmentationFromNormals::initSACModel] Using a model of type: SACMODEL_CYLINDER
[pcl::SACSegmentationFromNormals::initSACModel] Setting radius limits to 0.000000/0.100000
[pcl::SACSegmentationFromNormals::initSACModel] Setting normal distance weight to 0.100000
[pcl::SACSegmentationFromNormals::initSAC] Using a method of type: SAC_RANSAC with a model threshold of 0.050000
[pcl::SampleConsensusModelCylinder::optimizeModelCoefficients] LM solver finished with exit code 2, having a residual norm of 0.322616.
Initial solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0422902
Final solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0396354
Cylinder coefficients: header:
seq: 0
stamp: 0.000000000
frame_id:
values[]
values[0]: 0.0452105
values[1]: 0.0924601
values[2]: 0.790215
values[3]: 0.20495
values[4]: -0.721649
values[5]: -0.661225
values[6]: 0.0396354
PointCloud representing the cylindrical component: 8625 data points.