#include <iostream>
#include <string>
#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/time.h> // TicToc
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;
bool next_iteration = false;
void print4x4Matrix(const Eigen::Matrix4d & matrix)
{
printf("Rotation matrix :\n");
printf(" | %6.3f %6.3f %6.3f | \n", matrix(0, 0), matrix(0, 1), matrix(0, 2));
printf("R = | %6.3f %6.3f %6.3f | \n", matrix(1, 0), matrix(1, 1), matrix(1, 2));
printf(" | %6.3f %6.3f %6.3f | \n", matrix(2, 0), matrix(2, 1), matrix(2, 2));
printf("Translation vector :\n");
printf("t = < %6.3f, %6.3f, %6.3f >\n\n", matrix(0, 3), matrix(1, 3), matrix(2, 3));}
void keyboardEventOccurred(const pcl::visualization::KeyboardEvent& event,void* nothing)
{
if (event.getKeySym() == "space" && event.keyDown())
next_iteration = true;}
int main()
{
// 声明需要用到的点云（读入的，转换的，ICP调整的三个点云）
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
int iterations = 0; // Default number of ICP iterations
pcl::console::TicToc time;
time.tic();
std::string filename = "cow-2.ply";
if (pcl::io::loadPLYFile(filename, *cloud_in) < 0)
{
PCL_ERROR("Error loading cloud %s.\n", filename);
system("pause");
return (-1);
}
std::cout << "\nLoaded file " << filename << " (" << cloud_in->size() << " points) in " << time.toc() << " ms\n" << std::endl;
// 定义旋转平移的转换矩阵
Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity(); // A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
double theta = M_PI/4 ; // 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);
//Z轴平移0.4米 // A translation on Z axis (0.4 meters)
transformation_matrix(2, 3) = 0.4;
//打印出旋转矩阵R和平移T
std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl; print4x4Matrix(transformation_matrix);
//转移后
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");
system("pause");
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"); system("pause"); return (-1); } } next_iteration = false; } system("pause"); return (0);}
