pcl 学习2 利用矩阵转移一个点云

点云处理中，我们有可能需要将点云旋转或者平移到某个位置。pcl提供了相应的函数。假如转移矩阵是[R|T],那么目标点dst和原始点src的关系是：
dst=[R|t]*src.
转移矩阵的格式是
|——-> This column is the translation
| R R R x | \
| R R R y | }-> The identity 3x3 matrix (no rotation) on the left
| R R R z | /
| 0 0 0 1 | -> We do not use this line (and it has to stay 0,0,0,1)
不懂的移步到http://blog.sina.com.cn/s/blog_620bf89501011fl8.html
官方教程
http://pointclouds.org/documentation/tutorials/matrix_transform.php#matrix-transform
代码

#include <iostream>#include <pcl/io/pcd_io.h>#include <pcl/io/ply_io.h>#include <pcl/point_cloud.h>#include <pcl/console/parse.h>#include <pcl/common/transforms.h>#include <pcl/visualization/pcl_visualizer.h>// This function displays the helpvoidshowHelp(char * program_name){    std::cout << std::endl;    std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;    std::cout << "-h:  Show this help." << std::endl;}// This is the main functionintmain(int argc, char** argv){    // Show help    if (pcl::console::find_switch(argc, argv, "-h") || pcl::console::find_switch(argc, argv, "--help")) {        showHelp(argv[0]);        return 0;    }    // Fetch point cloud filename in arguments | Works with PCD and PLY files    std::vector<int> filenames;    bool file_is_pcd = false;    filenames = pcl::console::parse_file_extension_argument(argc, argv, ".ply");    if (filenames.size() != 1)  {        filenames = pcl::console::parse_file_extension_argument(argc, argv, ".pcd");        if (filenames.size() != 1) {            showHelp(argv[0]);            return -1;        }        else {            file_is_pcd = true;        }    }    // Load file | Works with PCD and PLY files    pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud(new pcl::PointCloud<pcl::PointXYZ>());    if (file_is_pcd) {        if (pcl::io::loadPCDFile(argv[filenames[0]], *source_cloud) < 0)  {            std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;            showHelp(argv[0]);            return -1;        }    }    else {        if (pcl::io::loadPLYFile(argv[filenames[0]], *source_cloud) < 0)  {            std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;            showHelp(argv[0]);            return -1;        }    }    /* Reminder: how transformation matrices work :    |-------> This column is the translation    | 1 0 0 x |  \    | 0 1 0 y |   }-> The identity 3x3 matrix (no rotation) on the left    | 0 0 1 z |  /    | 0 0 0 1 |    -> We do not use this line (and it has to stay 0,0,0,1)    METHOD #1: Using a Matrix4f    This is the "manual" method, perfect to understand but error prone !    */    Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();    // Define a rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)    float theta = M_PI / 4; // The angle of rotation in radians    transform_1(0, 0) = cos(theta);    transform_1(0, 1) = -sin(theta);    transform_1(1, 0) = sin(theta);    transform_1(1, 1) = cos(theta);    //    (row, column)    // Define a translation of 2.5 meters on the x axis.    transform_1(0, 3) = 2.5;    // Print the transformation    printf("Method #1: using a Matrix4f\n");    std::cout << transform_1 << std::endl;    /*  METHOD #2: Using a Affine3f    This method is easier and less error prone    */    Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();    // Define a translation of 2.5 meters on the x axis.    transform_2.translation() << 2.5, 0.0, 0.0;    // The same rotation matrix as before; theta radians arround Z axis    transform_2.rotate(Eigen::AngleAxisf(theta, Eigen::Vector3f::UnitZ()));    // Print the transformation    printf("\nMethod #2: using an Affine3f\n");    std::cout << transform_2.matrix() << std::endl;    // Executing the transformation    pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud(new pcl::PointCloud<pcl::PointXYZ>());    // You can either apply transform_1 or transform_2; they are the same    pcl::transformPointCloud(*source_cloud, *transformed_cloud, transform_2);    // Visualization    printf("\nPoint cloud colors :  white  = original point cloud\n"        "                        red  = transformed point cloud\n");    pcl::visualization::PCLVisualizer viewer("Matrix transformation example");    // Define R,G,B colors for the point cloud    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> source_cloud_color_handler(source_cloud, 255, 255, 255);    // We add the point cloud to the viewer and pass the color handler    viewer.addPointCloud(source_cloud, source_cloud_color_handler, "original_cloud");    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler(transformed_cloud, 230, 20, 20); // Red    viewer.addPointCloud(transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");    viewer.addCoordinateSystem(1.0, "cloud", 0);    viewer.setBackgroundColor(0.05, 0.05, 0.05, 0); // Setting background to a dark grey    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "original_cloud");    viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "transformed_cloud");    //viewer.setPosition(800, 400); // Setting visualiser window position    while (!viewer.wasStopped()) { // Display the visualiser until 'q' key is pressed        viewer.spinOnce();    }    return 0;}

源代码中用了两种方式构造转移矩阵，一种是直接赋值，另一种用eigen自动生成。
效果如下

点云文件来源请参考上一个博客。运行代码时在工程 属性 配置属性 调试 命令参数中输入点云文件路径（Vs用户）