PCL Series--Planar model segmentation

Welcome to visit our blog PCL Series PCL Series--read in PCD format File Operation PCL Series--writing point cloud Data to PCD format File PCL series--stitching two point clouds PCL series--extracting Narf key points from depth image (Rangeimage) PCL series--how to visualize depth image PCL series-- How to use the iterative nearest point method (ICP) Registration PCL Series--How to gradually register a pair of point clouds PCL series--three-dimensional reconstruction of Poisson reconstruction PCL series--three-dimensional reconstruction of greedy triangular projection algorithm PCL series--three-dimensional reconstruction of moving Cube algorithm PCL series--Plane model segmentation Description

Code Download Code

#include <iostream> #include <pcl/ModelCoefficients.h>//model factor #include <pcl/io/pcd_io.h>  Input/Output #include <pcl/point_types.h>//Point cloud (type) #include <pcl/sample_consensus/method_types.h> Random sample Consistency algorithm method type #include <pcl/sample_consensus/model_types.h>//random sample Conformance algorithm model type #include <pcl/segmentation /sac_segmentation.h>//Random Sample Consistency algorithm segmentation method int main (int argc, char** argv) {PCL::P OINTCLOUD&LT;PCL::P ointxyz> Cloud ;
  Create a point cloud object for storing point cloud data//Fill point cloud data cloud.width = 15;
  Cloud.height = 1;

    Cloud.points.resize (Cloud.width * cloud.height);  Generate random data for (size_t i = 0; i < cloud.points.size (); ++i) {cloud.points[i].x = 1024x768 * RAND ()/(Rand_max +
    1.0f);
    CLOUD.POINTS[I].Y = 1024x768 * RAND ()/(Rand_max + 1.0f);
  CLOUD.POINTS[I].Z = 1.0;
    }//Set several outsider points cloud.points[0].z = 2.0;
    Cloud.points[3].z =-2.0;
    CLOUD.POINTS[6].Z = 4.0; Display point cloud number and coordinate information Std::cerr << "points cloud DATA: "<< cloud.points.size () <<" points "<< Std::endl;  for (size_t i = 0; i < cloud.points.size (); ++i) Std::cerr << "" << cloud.points[i].x << "<< cloud.points[i].y <<" "<< cloud.poin

    Ts[i].z << Std::endl; Pcl::modelcoefficients::P TR coefficients (new pcl::modelcoefficients); Coefficient of the model that stores the output PCL::P ointindices::P tr inliers (new PCL::P ointindices);
    Store inside points, use points//Create segmented Objects PCL::SACSEGMENTATION&LT;PCL::P ointxyz> seg;
    Optional setting seg.setoptimizecoefficients (TRUE); Seg.setmodeltype (Pcl::sacmodel_plane) must be set;      Set the model type, detect plane Seg.setmethodtype (PCL::SAC_RANSAC);
  Set method "Cluster or random sample consistency" seg.setdistancethreshold (0.01);
  Seg.setinputcloud (cloud.makeshared ());    Seg.segment (*inliers, *coefficients); Split operation if (inliers->indices.size () = = 0)//depending on the number of dots, determine if the success {Pcl_error ("Could not estimate a planar modelFor the given dataset. ");
  Return (-1); 
                        }//Display the model's coefficients std::cerr << "model coefficients:" << coefficients->values[0] << "" <<coefficients->values[1] << "<<coefficients->values[2" &lt
    ;< "<<coefficients->values[3" <<std::endl;
    Displays the interior points used during the estimation of the plane model Std::cerr << "Model inliers:" << inliers->indices.size () << Std::endl;  for (size_t i = 0; i < inliers->indices.size (); ++i) Std::cerr << Inliers->indices[i] << "<<cloud.points[inliers->indices[i]].x <<" "<<cloud.points[i Nliers->indices[i]].y << "<<cloud.points[inliers->indices[i]].z <& Lt
    Std::endl;
return (0);
 }

Run Results

Reference

"Point Cloud Library PCL Learning Tutorial" Chapter 14