int
main (int argc, char* argv[])
{
// The point clouds we will be using
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_in_1 (new pcl::PointCloud<pcl::PointXYZRGBA>); // Original point cloud
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_in_2 (new pcl::PointCloud<pcl::PointXYZRGBA>); // Transformed point cloud
pcl::PointCloud<pcl::PointXYZRGBA>::Ptr cloud_icp (new pcl::PointCloud<pcl::PointXYZRGBA>); // ICP output point cloud
// Load two pcd files
if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> ("PCD/src_1.pcd", *cloud_in_1) < 0) {
PCL_ERROR("Error loading cloud PCD/src_1.pcd");
return -1;
}
if (pcl::io::loadPCDFile<pcl::PointXYZRGBA> ("PCD/src_2.pcd", *cloud_in_2) < 0) {
PCL_ERROR("Error loading cloud PCD/src_2.pcd");
return -1;
}
int iterations = 1;
/*
// If the user passed the number of iteration as an argument
if (argc > 2) {
iterations = atoi(argv[3]);
}
if (iterations < 1) {
PCL_ERROR("Number of initial iterations must be >= 1\n");
return -1;
}
*/
printf ("\nLoaded files successfully\n\n");
pcl::copyPointCloud(*cloud_in_2, *cloud_icp);
// The Iterative Closest Point algorithm
std::cout << "Initial iterations number is set to : " << iterations << "\n";
pcl::IterativeClosestPoint<pcl::PointXYZRGBA, pcl::PointXYZRGBA> icp;
icp.setMaximumIterations(iterations);
icp.setInputSource(cloud_in_1);
icp.setInputTarget(cloud_in_2);
icp.align(cloud_icp);
/*
icp.setMaximumIterations(1); // For the next time we will call .align() function
// Defining a rotation matrix and translation vector
Eigen::Matrix4f transformation_matrix = Eigen::Matrix4f::Identity();
if (icp.hasConverged()) {
printf("\nICP has converged, score is %+.0e\n", icp.getFitnessScore());
std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix = icp.getFinalTransformation();
printMatix4f(transformation_matrix);
} else {
PCL_ERROR ("\nICP has not converged.\n");
return -1;
}
// Visualization
pcl::visualization::PCLVisualizer viewer ("ICP demo");
// Create two verticaly separated viewports
int v1(0);
viewer.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
// The color we will be using
float bckgr_gray_level = 0.0; // Black
float txt_gray_lvl = 1.0-bckgr_gray_level;
// Original point cloud is white
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGBA> cloud_in_1_color_h (cloud_in_1, (int)255* txt_gray_lvl, (int)255* txt_gray_lvl, (int)255* txt_gray_lvl);
viewer.addPointCloud (cloud_in_1, cloud_in_1_color_h, "cloud_in_1_v1", v1);
// Transformed point cloud is green
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZRGBA> cloud_in_2_color_h (cloud_in_2, 20, 180, 20);
viewer.addPointCloud (cloud_in_2, cloud_in_2_color_h, "cloud_in_2_v1", v1);
// Adding text descriptions in each viewport
viewer.addText("White: Original point cloud\nGreen: Matrix transformed point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
std::stringstream ss; ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str();
viewer.addText(iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v1);
// Set background color
viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
// Set camera position and orientation
//viewer.setCameraPosition(-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
//viewer.setSize(1280, 1024); // Visualiser window size
// Register keyboard callback :
viewer.registerKeyboardCallback(&keyboardEventOccurred, (void*) NULL);
// Display the visualiser
while (!viewer.wasStopped ()) {
viewer.spinOnce ();
// The user pressed "space" :
if (next_iteration) {
icp.align(*cloud_in_1);
if (icp.hasConverged()) {
printf("\033[11A"); // Go up 11 lines in terminal output.
printf("\nICP has converged, score is %+.0e\n", icp.getFitnessScore());
std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix *= icp.getFinalTransformation(); // This is not very accurate !
printMatix4f(transformation_matrix); // Print the transformation between original pose and current pose
ss.str (""); ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str();
viewer.updateText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
viewer.updatePointCloud (cloud_in_1, cloud_in_1_color_h, "cloud_icp_v2");
} else {
PCL_ERROR ("\nICP has not converged.\n");
return -1;
}
}
next_iteration = false;
}
*/
return 0;
}
int
main (int argc,
char* argv[])
{
// The point clouds we will be using
PointCloudT::Ptr cloud_in (new PointCloudT); // Original point cloud
PointCloudT::Ptr cloud_tr (new PointCloudT); // Transformed point cloud
PointCloudT::Ptr cloud_icp (new PointCloudT); // ICP output point cloud
// Checking program arguments
if (argc < 2)
{
printf ("Usage :\n");
printf ("\t\t%s file.ply number_of_ICP_iterations\n", argv[0]);
PCL_ERROR ("Provide one ply file.\n");
return (-1);
}
int iterations = 1; // Default number of ICP iterations
if (argc > 2)
{
// If the user passed the number of iteration as an argument
iterations = atoi (argv[2]);
if (iterations < 1)
{
PCL_ERROR ("Number of initial iterations must be >= 1\n");
return (-1);
}
}
pcl::console::TicToc time;
time.tic ();
if (pcl::io::loadPLYFile (argv[1], *cloud_in) < 0)
{
PCL_ERROR ("Error loading cloud %s.\n", argv[1]);
return (-1);
}
std::cout << "\nLoaded file " << argv[1] << " (" << cloud_in->size () << " points) in " << time.toc () << " ms\n" << std::endl;
// Defining a rotation matrix and translation vector
Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity ();
// A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
double theta = M_PI / 8; // The angle of rotation in radians
transformation_matrix (0, 0) = cos (theta);
transformation_matrix (0, 1) = -sin (theta);
transformation_matrix (1, 0) = sin (theta);
transformation_matrix (1, 1) = cos (theta);
// A translation on Z axis (0.4 meters)
transformation_matrix (2, 3) = 0.4;
// Display in terminal the transformation matrix
std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;
print4x4Matrix (transformation_matrix);
// Executing the transformation
pcl::transformPointCloud (*cloud_in, *cloud_icp, transformation_matrix);
*cloud_tr = *cloud_icp; // We backup cloud_icp into cloud_tr for later use
// The Iterative Closest Point algorithm
time.tic ();
pcl::IterativeClosestPoint<PointT, PointT> icp;
icp.setMaximumIterations (iterations);
icp.setInputSource (cloud_icp);
icp.setInputTarget (cloud_in);
icp.align (*cloud_icp);
icp.setMaximumIterations (1); // We set this variable to 1 for the next time we will call .align () function
std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc () << " ms" << std::endl;
if (icp.hasConverged ())
{
std::cout << "\nICP has converged, score is " << icp.getFitnessScore () << std::endl;
std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix = icp.getFinalTransformation ().cast<double>();
print4x4Matrix (transformation_matrix);
}
else
{
PCL_ERROR ("\nICP has not converged.\n");
return (-1);
}
// Visualization
pcl::visualization::PCLVisualizer viewer ("ICP demo");
// Create two vertically separated viewports
int v1 (0);
int v2 (1);
viewer.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
viewer.createViewPort (0.5, 0.0, 1.0, 1.0, v2);
// The color we will be using
float bckgr_gray_level = 0.0; // Black
float txt_gray_lvl = 1.0 - bckgr_gray_level;
// Original point cloud is white
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_in_color_h (cloud_in, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl,
(int) 255 * txt_gray_lvl);
viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v1", v1);
viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v2", v2);
// Transformed point cloud is green
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_tr_color_h (cloud_tr, 20, 180, 20);
viewer.addPointCloud (cloud_tr, cloud_tr_color_h, "cloud_tr_v1", v1);
// ICP aligned point cloud is red
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_icp_color_h (cloud_icp, 180, 20, 20);
viewer.addPointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2", v2);
// Adding text descriptions in each viewport
viewer.addText ("White: Original point cloud\nGreen: Matrix transformed point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
viewer.addText ("White: Original point cloud\nRed: ICP aligned point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);
std::stringstream ss;
ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str ();
viewer.addText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v2);
// Set background color
viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);
// Set camera position and orientation
viewer.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
viewer.setSize (1280, 1024); // Visualiser window size
// Register keyboard callback :
viewer.registerKeyboardCallback (&keyboardEventOccurred, (void*) NULL);
// Display the visualiser
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
// The user pressed "space" :
if (next_iteration)
{
// The Iterative Closest Point algorithm
time.tic ();
icp.align (*cloud_icp);
std::cout << "Applied 1 ICP iteration in " << time.toc () << " ms" << std::endl;
if (icp.hasConverged ())
{
printf ("\033[11A"); // Go up 11 lines in terminal output.
printf ("\nICP has converged, score is %+.0e\n", icp.getFitnessScore ());
std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix *= icp.getFinalTransformation ().cast<double>(); // WARNING /!\ This is not accurate! For "educational" purpose only!
print4x4Matrix (transformation_matrix); // Print the transformation between original pose and current pose
ss.str ("");
ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str ();
viewer.updateText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
viewer.updatePointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2");
}
else
{
PCL_ERROR ("\nICP has not converged.\n");
return (-1);
}
}
next_iteration = false;
}
return (0);
}