PCL点云库：对点云进行变换（Using a matrix to transform a point cloud）

点云数据可以用ASCII码的形式存储在PCD文件中（关于该格式的描述可以参考链接：The PCD (Point Cloud Data) file format）。为了生成三维点云数据，在excel中用rand()函数生成200行0-1的小数，ABC三列分别代表空间点的xyz坐标。

# .PCD v.7 - Point Cloud Data file format
VERSION .7        
FIELDS x y z     
SIZE 4 4 4         
TYPE F F F         
COUNT 1 1 1     
WIDTH 200        
HEIGHT 1        
VIEWPOINT 0 0 0 1 0 0 0
POINTS 200        
DATA ascii        
0.88071666    0.369209703    0.062937221
0.06418104    0.579762553    0.221359779
...
...
0.640053058    0.480279041    0.843647334
0.245554712    0.825770496    0.626442137

　　进行点云的变换主要用到的函数是pcl::transformPointCloud，函数原型为：

　　void pcl::transformPointCloud(const pcl::PointCloud< PointT > & cloud_in,
　　　　　　　　　　　　　　　　　　 pcl::PointCloud< PointT > & cloud_out,
　　　　　　　　　　　　　　　　　 const Eigen::Matrix4f & transform )

　　参数中cloud_in为源点云，cloud_out为变换后的点云，transform为变换矩阵。下面的代码对源点云绕Z轴旋转45°，然后沿X轴平移了2.5个单位：

#include <iostream>

#include <pcl/io/pcd_io.h>
#include <pcl/point_cloud.h>
#include <pcl/common/transforms.h>                  //allows us to use pcl::transformPointCloud function
#include <pcl/visualization/pcl_visualizer.h>


// This is the main function
int main (int argc, char** argv)
{

    //creates a PointCloud<PointXYZ> boost shared pointer and initializes it.
    pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud (new pcl::PointCloud<pcl::PointXYZ> ());


    // Load PCD file
    if (pcl::io::loadPCDFile<pcl::PointXYZ> ("sample.pcd", *source_cloud) == -1) 
    {
        PCL_ERROR ("Couldn't read file sample.pcd \n");
        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)
    // Here we defined a 45° (PI/4) rotation around the Z axis and a translation on the X axis.
    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;


    // Executing the transformation
    pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
    /*
    void pcl::transformPointCloud(const pcl::PointCloud< PointT > & cloud_in, 
                                    pcl::PointCloud< PointT > &  cloud_out,  
                                    const Eigen::Matrix4f &  transform  ) 
    */
    // Apply an affine transform defined by an Eigen Transform.
    pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_1);

    // 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, 0);  //Adds 3D axes describing a coordinate system to screen at 0,0,0. 
    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;
}

　　在PCL官网下载All-in-one installers，由于使用的是Win7 32位系统，因此选择了Windows MSVC 2010 (32bit)进行安装，这个All-in-one的安装程序会同时安装除QT外的一些第三方依赖库，比如boost、Eigen、VTK、OpenNI等。

　　安装好之后如果是自己在VS2010中配置工程属性，将会很麻烦，可以参考Using PCL in your own project。下面通过Cmake来自动生成VS2010的项目。首先创建一个CMakeLists.txt文件（注意其中PCL的版本）。如果编译软件使用了外部库，事先并不知道它的头文件和链接库的位置。得在编译命令中加上包含它们的查找路径。CMake使用find_package命令来解决这个问题。

cmake_minimum_required(VERSION 2.6 FATAL_ERROR)

project(pcl-matrix_transform)

find_package(PCL 1.6 REQUIRED)

include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})

add_executable (matrix_transform matrix_transform.cpp)
target_link_libraries (matrix_transform ${PCL_LIBRARIES})

　　然后使用CMake的GUI程序来生成工程文件。Where is the source code栏选择源代码所在路径，这里代码放在桌面上；Where to build the binaries栏选择生成文件所在的路径，默认在桌面上的build文件夹中。点击Configure之后会弹出对话框，选择Visual Studio 2010，最后点击生成按钮，将会在build文件夹中生成VS2010的工程文件。