IterativeClosestPoint<PointXYZ, PointXYZ> icp;

// Set the input source and target

icp.setInputCloud (cloud_source);

icp.setInputTarget (cloud_target);

// Set the max correspondence distance to 5cm (e.g., correspondences with higher distances will be ignored)

icp.setMaxCorrespondenceDistance (0.05);

// Set the maximum number of iterations (criterion 1)

icp.setMaximumIterations ();

// Set the transformation epsilon (criterion 2)

icp.setTransformationEpsilon (1e-);

// Set the euclidean distance difference epsilon (criterion 3)

icp.setEuclideanFitnessEpsilon ();

// Perform the alignment

icp.align (cloud_source_registered);

// Obtain the transformation that aligned cloud_source to cloud_source_registered

Eigen::Matrix4f transformation = icp.getFinalTransformation ();

#include <iostream>

#include <pcl/io/pcd_io.h>

#include <pcl/point_types.h>

#include <pcl/registration/icp.h>

int main (int argc, char** argv)

{

    //Creates two pcl::PointCloud<pcl::PointXYZ> boost shared pointers and initializes them

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZ>);

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);

    // Fill in the CloudIn data

    cloud_in->width    = ;

    cloud_in->height   = ;

    cloud_in->is_dense = false;

    cloud_in->points.resize (cloud_in->width * cloud_in->height);

    for (size_t i = ; i < cloud_in->points.size (); ++i)

    {

        cloud_in->points[i].x =  * rand () / (RAND_MAX + 1.0f);

        cloud_in->points[i].y =  * rand () / (RAND_MAX + 1.0f);

        cloud_in->points[i].z =  * rand () / (RAND_MAX + 1.0f);

    }

    *cloud_out = *cloud_in;

    //performs a simple rigid transform on the point cloud

    for (size_t i = ; i < cloud_in->points.size (); ++i)

        cloud_out->points[i].x = cloud_in->points[i].x + 1.5f;

    //creates an instance of an IterativeClosestPoint and gives it some useful information

    pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;

    icp.setInputCloud(cloud_in);

    icp.setInputTarget(cloud_out);

    //Creates a pcl::PointCloud<pcl::PointXYZ> to which the IterativeClosestPoint can save the resultant cloud after applying the algorithm

    pcl::PointCloud<pcl::PointXYZ> Final;

    //Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output.

    icp.align(Final);

    //Return the state of convergence after the last align run.

    //If the two PointClouds align correctly then icp.hasConverged() = 1 (true).

    std::cout << "has converged: " << icp.hasConverged() <<std::endl;

    //Obtain the Euclidean fitness score (e.g., sum of squared distances from the source to the target)

    std::cout << "score: " <<icp.getFitnessScore() << std::endl;

    std::cout << "----------------------------------------------------------"<< std::endl;

    //Get the final transformation matrix estimated by the registration method.

    std::cout << icp.getFinalTransformation() << std::endl;

    return ();

}