连接两个点云中的字段或数据形成新点云以及Opennni Grabber初识-白红宇

（1）学习如何连接两个不同点云为一个点云，进行操作前要确保两个数据集中字段的类型相同和维度相等，同时了解如何连接两个不同点云的字段（例如颜色法线）这种操作的强制约束条件是两个数据集中点的数目必须一样，例如：点云A是N个点XYZ点，点云B是N个点的RGB点，则连接两个字段形成点云C是N个点xyzrgb类型

新建文件concatenate_clouds.cpp CMakeLists.txt

concatenate_clouds.cpp ：

#include 
     
      #include 
      
             //io模块 #include 
       
           //数据类型int  main (int argc, char** argv){  if (argc != 2) //提示如果执行可执行文件输入两个参数 -f 或者-p  {    std::cerr << "please specify command line arg '-f' or '-p'" << std::endl;    exit(0);  }  //申明三个pcl::PointXYZ点云数据类型，分别为cloud_a, cloud_b, cloud_c  pcl::PointCloud
        
          cloud_a, cloud_b, cloud_c;  //存储进行连接时需要的Normal点云,Normal (float n_x, float n_y, float n_z)  pcl::PointCloud
         
           n_cloud_b;   //存储连接XYZ与normal后的点云  pcl::PointCloud
          
            p_n_cloud_c; // 创建点云数据 //设置cloud_a的个数为5 cloud_a.width = 5; cloud_a.height = cloud_b.height = n_cloud_b.height = 1; //设置都为无序点云 cloud_a.points.resize (cloud_a.width * cloud_a.height); //总数 if (strcmp(argv[1], "-p") == 0) //判断是否为连接a+b=c(点云连接) { cloud_b.width = 3; cloud_b.points.resize (cloud_b.width * cloud_b.height); } else{ n_cloud_b.width = 5; //如果是连接XYZ与normal则生成5个法线（字段间连接） n_cloud_b.points.resize (n_cloud_b.width * n_cloud_b.height); }//以下循环生成无序点云填充上面定义的两种类型的点云数据 for (size_t i = 0; i < cloud_a.points.size (); ++i) { //cloud_a产生三个点（每个点都有X Y Z 三个随机填充的值） cloud_a.points[i].x = 1024 * rand () / (RAND_MAX + 1.0f); cloud_a.points[i].y = 1024 * rand () / (RAND_MAX + 1.0f); cloud_a.points[i].z = 1024 * rand () / (RAND_MAX + 1.0f); } if (strcmp(argv[1], "-p") == 0) for (size_t i = 0; i < cloud_b.points.size (); ++i) { //如果连接a+b=c，则cloud_b用三个点作为xyz的数据 cloud_b.points[i].x = 1024 * rand () / (RAND_MAX + 1.0f); cloud_b.points[i].y = 1024 * rand () / (RAND_MAX + 1.0f); cloud_b.points[i].z = 1024 * rand () / (RAND_MAX + 1.0f); } else for (size_t i = 0; i < n_cloud_b.points.size (); ++i) { //如果连接xyz+normal=xyznormal则n_cloud_b用5个点作为normal数据 n_cloud_b.points[i].normal[0] = 1024 * rand () / (RAND_MAX + 1.0f); n_cloud_b.points[i].normal[1] = 1024 * rand () / (RAND_MAX + 1.0f); n_cloud_b.points[i].normal[2] = 1024 * rand () / (RAND_MAX + 1.0f); }/*******************************************************************定义了连接点云会用到的5个点云对象：3个输入（cloud_a cloud_b 和n_cloud_b）两个输出（cloud_c n_cloud_c）然后就是为两个输入点云cloud_a和 cloud_b或者cloud_a 和n_cloud_b填充数据 ********************************************************************///输出Cloud A std::cerr << "Cloud A: " << std::endl; for (size_t i = 0; i < cloud_a.points.size (); ++i) std::cerr << " " << cloud_a.points[i].x << " " << cloud_a.points[i].y << " " << cloud_a.points[i].z << std::endl;//输出Cloud B std::cerr << "Cloud B: " << std::endl; if (strcmp(argv[1], "-p") == 0) for (size_t i = 0; i < cloud_b.points.size (); ++i) std::cerr << " " << cloud_b.points[i].x << " " << cloud_b.points[i].y << " " << cloud_b.points[i].z << std::endl; else//输出n_Cloud_b for (size_t i = 0; i < n_cloud_b.points.size (); ++i) std::cerr << " " << n_cloud_b.points[i].normal[0] << " " << n_cloud_b.points[i].normal[1] << " " << n_cloud_b.points[i].normal[2] << std::endl; // Copy the point cloud data if (strcmp(argv[1], "-p") == 0) { cloud_c = cloud_a; cloud_c += cloud_b;//把cloud_a和cloud_b连接一起创建cloud_c 后输出 std::cerr << "Cloud C: " << std::endl; for (size_t i = 0; i < cloud_c.points.size (); ++i) std::cerr << " " << cloud_c.points[i].x << " " << cloud_c.points[i].y << " " << cloud_c.points[i].z << " " << std::endl; } else { //连接字段 把cloud_a和 n_cloud_b字段连接 一起创建 p_n_cloud_c) pcl::concatenateFields (cloud_a, n_cloud_b, p_n_cloud_c); std::cerr << "Cloud C: " << std::endl; for (size_t i = 0; i < p_n_cloud_c.points.size (); ++i) std::cerr << " " << p_n_cloud_c.points[i].x << " " << p_n_cloud_c.points[i].y << " " << p_n_cloud_c.points[i].z << " " << p_n_cloud_c.points[i].normal[0] << " " << p_n_cloud_c.points[i].normal[1] << " " << p_n_cloud_c.points[i].normal[2] << std::endl; } return (0);}

CMakeLists.txt：

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)project(ch2_2)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})link_directories(${PCL_LIBRARY_DIRS})add_definitions(${PCL_DEFINITIONS})add_executable (concatenate_clouds concatenate_clouds.cpp)target_link_libraries (concatenate_clouds ${PCL_LIBRARIES})

编译执行后的结果，仔细研究看一下就可以看出点云连接和字段间连接的区别，字段间连接是在行的基础后连接，而点云连接是在列的下方连接，最重要的就是要考虑维度问题，同时每个点云都有XYZ三个数据值

字段间连接：

点云连接

（2）对于获取传感器的深度信息可以使用OpenNI Grabber类，（其中涉及到如何安装传感器的驱动等问题，比如我使用的是kinect 1.0 可能会遇到一些安装问题，但是网上还是有很多的解决办法的，在这里不对于叙述）

新建文件openni_grabber.cpp

#include 
     
         //点云类定义头文件#include 
      
           //点 类型定义头文件#include 
       
          //OpenNI数据流获取头文件#include 
        
                  //时间头文件//类SimpleOpenNIProcessor  的回调函数，作为在获取数据时，对数据进行处理的回调函数的封装，在本例中并没有什么处理，只是实时的在标准输出设备打印处信息。class SimpleOpenNIProcessor{public:  void cloud_cb_ (const pcl::PointCloud
         
          ::ConstPtr &cloud)  {    static unsigned count = 0;    static double last = pcl::getTime ();  //获取当前时间    if (++count == 30)      //每30ms一次输出    {      double now = pcl::getTime ();      //  >>右移      std::cout << "distance of center pixel :" << cloud->points [(cloud->width >> 1) * (cloud->height + 1)].z << " mm. Average framerate: " << double(count)/double(now - last) << " Hz" <<  std::endl;      count = 0;      last = now;    }  }    void run ()  {    pcl::Grabber* interface = new pcl::OpenNIGrabber();  //创建OpenNI采集对象    // 定义回调函数    boost::function
          
           ::ConstPtr&)> f = boost::bind (&SimpleOpenNIProcessor::cloud_cb_, this, _1); boost::signals2::connection c = interface->registerCallback (f);//注册回调函数 interface->start (); //开始接受点云数据 //直到用户按下Ctrl -c while (true) boost::this_thread::sleep (boost::posix_time::seconds (1)); // 停止采集 interface->stop (); }};int main (){ SimpleOpenNIProcessor v; v.run (); return (0);}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)project(openni_grabber)find_package(PCL 1.2 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})link_directories(${PCL_LIBRARY_DIRS})add_definitions(${PCL_DEFINITIONS})add_executable (openni_grabber openni_grabber.cpp)target_link_libraries (openni_grabber ${PCL_LIBRARIES})

编译后执行可执行文件的结果如下

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