交互式ICP

以下程序演示如何编写交互式ICP查看器。该程序将加载点云并对其进行刚性变换。之后，使用ICP算法将变换后的点云与原来的点云对齐。每次用户按下“空格”，进行ICP迭代，刷新可视化界面。

代码实现

资源准备 monkey.ply

#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>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));
}
/*** 此函数是查看器的回调。当查看器窗口位于顶部时，只要按任意键，就会调用此函数。如果碰到“空格”，将布尔值设置为true。*/
void keyboardEventOccurred(const pcl::visualization::KeyboardEvent& event,void* nothing)
{if (event.getKeySym() == "space" && event.keyDown())next_iteration = true;
}int main(int argc,char* argv[])
{// 准备将使用的点云PointCloudT::Ptr cloud_in(new PointCloudT);  // Original point cloudPointCloudT::Ptr cloud_tr(new PointCloudT);  // Transformed point cloudPointCloudT::Ptr cloud_icp(new PointCloudT);  // ICP output point cloud// 检查程序的参数，设置初始ICP迭代的次数，然后尝试加载PLY文件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;  // 默认的ICP迭代次数if (argc > 2){// 如果用户将迭代次数作为参数传递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;// 我们使用刚性矩阵变换来变换原始点云。// cloud_in包含原始点云。// cloud_tr和cloud_icp包含平移/旋转的点云。// cloud_tr是我们将用于显示的备份（绿点云）。// 定义旋转矩阵和平移向量Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity();// 一个旋转矩阵 (see https://en.wikipedia.org/wiki/Rotation_matrix)double theta = M_PI / 8;  // 以弧度为单位的旋转角度transformation_matrix(0, 0) = std::cos(theta);transformation_matrix(0, 1) = -sin(theta);transformation_matrix(1, 0) = sin(theta);transformation_matrix(1, 1) = std::cos(theta);// Z轴上的平移（0.4米）transformation_matrix(2, 3) = 0.4;// 在终端中显示变换矩阵std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;print4x4Matrix(transformation_matrix);// 执行转换pcl::transformPointCloud(*cloud_in, *cloud_icp, transformation_matrix);*cloud_tr = *cloud_icp;  // 我们将cloud_icp备份到cloud_tr中以备将来使用// 这是ICP对象的创建。我们设置ICP算法的参数。// setMaximumIterations（iterations）设置要执行的初始迭代次数（默认值为1）。// 然后，我们将点云转换为cloud_icp。 第一次对齐后，我们将在下一次使用该ICP对象时（当用户按下“空格”时）将ICP最大迭代次数设置为1。// ICP算法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);  // 下次调用.align（）函数时，我们将此变量设置为1std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc() << " ms" << std::endl;// 检查ICP算法是否收敛；否则退出程序。如果返回true，我们将转换矩阵存储在4x4矩阵中，然后打印刚性矩阵转换。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);}// 可视化pcl::visualization::PCLVisualizer viewer("ICP demo");// 创建两个垂直分隔的窗口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);// 颜色float bckgr_gray_level = 0.0;  // 黑色float txt_gray_lvl = 1.0 - bckgr_gray_level;// 原始点云为白色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);// 变换后的点云为绿色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对齐点云为红色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);// 在每个视口中添加文本描述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);// 设置背景颜色viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);viewer.setBackgroundColor(bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);// 设置相机位置和方向viewer.setCameraPosition(-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);viewer.setSize(1280, 1024);  // 可视化窗口的尺寸// 注册键盘回调：viewer.registerKeyboardCallback(&keyboardEventOccurred, (void*)NULL);//可视化while (!viewer.wasStopped()){viewer.spinOnce();// 如果用户按下空格:if (next_iteration){// ICP算法time.tic();// 如果用户按下键盘上的任意键，则会调用keyboardEventOccurred函数。 此功能检查键是否为“空格”。// 如果是，则全局布尔值next_iteration设置为true，从而允许查看器循环输入代码的下一部分：调用ICP对象以进行对齐。// 记住，我们已经配置了该对象输入/输出云，并且之前通过setMaximumIterations将最大迭代次数设置为1。icp.align(*cloud_icp);std::cout << "Applied 1 ICP iteration in " << time.toc() << " ms" << std::endl;// 和以前一样，我们检查ICP是否收敛，如果不收敛，则退出程序。if (icp.hasConverged()){// printf（“ 033 [11A”）; 在终端增加11行以覆盖显示的最后一个矩阵是一个小技巧。// 简而言之，它允许替换文本而不是编写新行； 使输出更具可读性。 我们增加迭代次数以更新可视化器中的文本值。printf("\033[11A");  // Go up 11 lines in terminal output.printf("\nICP has converged, score is %+.0e\n", icp.getFitnessScore());// 这意味着，如果您已经完成了10次迭代，则此函数返回矩阵以将点云从迭代10转换为11。std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;// 函数getFinalTransformation（）返回在迭代过程中完成的刚性矩阵转换（此处为1次迭代）。transformation_matrix *= icp.getFinalTransformation().cast<double>();  // WARNING /!\ This is not accurate! For "educational" purpose only!print4x4Matrix(transformation_matrix);  // 打印原始姿态和当前姿态之间的转换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);}//这不是我们想要的。如果我们将最后一个矩阵与新矩阵相乘，那么结果就是从开始到当前迭代的转换矩阵。}next_iteration = false;}return (0);
}