PCL点云配准（1）

#include <iostream>                 //标准输入输出头文件
#include <pcl/io/pcd_io.h>         //I/O操作头文件
#include <pcl/point_types.h>        //点类型定义头文件
#include <pcl/registration/icp.h>   //ICP配准类相关头文件

int
 main (int argc, char** argv)
{ 
  //创建两个pcl::PointCloud<pcl::PointXYZ>共享指针，并初始化它们
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in (new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);

  // 随机填充点云
  cloud_in->width    = 5;               //设置点云宽度
  cloud_in->height   = 1;               //设置点云为无序点
  cloud_in->is_dense = false;
  cloud_in->points.resize (cloud_in->width * cloud_in->height);
  for (size_t i = 0; i < cloud_in->points.size (); ++i)
  {
    cloud_in->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud_in->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
    cloud_in->points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
  }
  std::cout << "Saved " << cloud_in->points.size () << " data points to input:"//打印处点云总数
      << std::endl;

  for (size_t i = 0; i < cloud_in->points.size (); ++i) std::cout << "    " <<    //打印处实际坐标
      cloud_in->points[i].x << " " << cloud_in->points[i].y << " " <<
      cloud_in->points[i].z << std::endl;
  *cloud_out = *cloud_in;
  std::cout << "size:" << cloud_out->points.size() << std::endl;

   //实现一个简单的点云刚体变换，以构造目标点云
  for (size_t i = 0; i < cloud_in->points.size (); ++i)
    cloud_out->points[i].x = cloud_in->points[i].x + 0.7f;
  std::cout << "Transformed " << cloud_in->points.size () << " data points:"
      << std::endl;
  for (size_t i = 0; i < cloud_out->points.size (); ++i)     //打印构造出来的目标点云
    std::cout << "    " << cloud_out->points[i].x << " " <<
      cloud_out->points[i].y << " " << cloud_out->points[i].z << std::endl;

  pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;   //创建IterativeClosestPoint的对象
  icp.setInputCloud(cloud_in);                 //cloud_in设置为点云的源点
  icp.setInputTarget(cloud_out);               //cloud_out设置为与cloud_in对应的匹配目标
  pcl::PointCloud<pcl::PointXYZ> Final;         //存储经过配准变换点云后的点云
  icp.align(Final);                             //打印配准相关输入信息
  std::cout << "has converged:" << icp.hasConverged() << " score: " <<
  icp.getFitnessScore() << std::endl;
  std::cout << icp.getFinalTransformation() << std::endl;

 return (0);
}

#include <boost/make_shared.hpp>              //boost指针相关头文件
#include <pcl/point_types.h>                  //点类型定义头文件
#include <pcl/point_cloud.h>                  //点云类定义头文件
#include <pcl/point_representation.h>         //点表示相关的头文件
#include <pcl/io/pcd_io.h>                    //PCD文件打开存储类头文件
#include <pcl/filters/voxel_grid.h>           //用于体素网格化的滤波类头文件 
#include <pcl/filters/filter.h>             //滤波相关头文件
#include <pcl/features/normal_3d.h>         //法线特征头文件
#include <pcl/registration/icp.h>           //ICP类相关头文件
#include <pcl/registration/icp_nl.h>        //非线性ICP 相关头文件
#include <pcl/registration/transforms.h>      //变换矩阵类头文件
#include <pcl/visualization/pcl_visualizer.h>  //可视化类头文件

using pcl::visualization::PointCloudColorHandlerGenericField;
using pcl::visualization::PointCloudColorHandlerCustom;

//定义
typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloud;   //申明pcl::PointXYZ数据
typedef pcl::PointNormal PointNormalT;
typedef pcl::PointCloud<PointNormalT> PointCloudWithNormals;

// 申明一个全局可视化对象变量，定义左右视点分别显示配准前和配准后的结果点云
    pcl::visualization::PCLVisualizer *p;     //创建可视化对象 
    int vp_1, vp_2;                           //定义存储 左 右视点的ID

//申明一个结构体方便对点云以文件名和点云对象进行成对处理和管理点云，处理过程中可以同时接受多个点云文件的输入
struct PCD
{
  PointCloud::Ptr cloud;  //点云共享指针
  std::string f_name;   //文件名称

  PCD() : cloud (new PointCloud) {};
};

struct PCDComparator  //文件比较处理
{
  bool operator () (const PCD& p1, const PCD& p2)
  {
    return (p1.f_name < p2.f_name);
  }
};


// 以< x, y, z, curvature >形式定义一个新的点表示
class MyPointRepresentation : public pcl::PointRepresentation <PointNormalT>
{
  using pcl::PointRepresentation<PointNormalT>::nr_dimensions_;
public:
  MyPointRepresentation ()
  {
    nr_dimensions_ = 4;    //定义点的维度
  }

  // 重载copyToFloatArray方法将点转化为四维数组 
  virtual void copyToFloatArray (const PointNormalT &p, float * out) const
  {
    // < x, y, z, curvature >
    out[0] = p.x;
    out[1] = p.y;
    out[2] = p.z;
    out[3] = p.curvature;
  }
};

/** \左视图用来显示未匹配的源和目标点云*/
void showCloudsLeft(const PointCloud::Ptr cloud_target, const PointCloud::Ptr cloud_source)
{
  p->removePointCloud ("vp1_target");
  p->removePointCloud ("vp1_source");

  PointCloudColorHandlerCustom<PointT> tgt_h (cloud_target, 0, 255, 0);
  PointCloudColorHandlerCustom<PointT> src_h (cloud_source, 255, 0, 0);
  p->addPointCloud (cloud_target, tgt_h, "vp1_target", vp_1);
  p->addPointCloud (cloud_source, src_h, "vp1_source", vp_1);

  PCL_INFO ("Press q to begin the registration.\n");
  p-> spin();
}



/** \右边显示配准后的源和目标点云*/
void showCloudsRight(const PointCloudWithNormals::Ptr cloud_target, const PointCloudWithNormals::Ptr cloud_source)
{
  p->removePointCloud ("source");
  p->removePointCloud ("target");


  PointCloudColorHandlerGenericField<PointNormalT> tgt_color_handler (cloud_target, "curvature");
  if (!tgt_color_handler.isCapable ())
      PCL_WARN ("Cannot create curvature color handler!");

  PointCloudColorHandlerGenericField<PointNormalT> src_color_handler (cloud_source, "curvature");
  if (!src_color_handler.isCapable ())
      PCL_WARN ("Cannot create curvature color handler!");


  p->addPointCloud (cloud_target, tgt_color_handler, "target", vp_2);
  p->addPointCloud (cloud_source, src_color_handler, "source", vp_2);

  p->spinOnce();
}

////////////////////////////////////////////////////////////////////////////////
/** \brief Load a set of PCD files that we want to register together
  * \param argc the number of arguments (pass from main ())
  * \param argv the actual command line arguments (pass from main ())
  * \param models the resultant vector of point cloud datasets
  */
void loadData (int argc, char **argv, std::vector<PCD, Eigen::aligned_allocator<PCD> > &models)
{
  std::string extension (".pcd");
  // 第一个参数是命令本身，所以要从第二个参数开始解析
  for (int i = 1; i < argc; i++)
  {
    std::string fname = std::string (argv[i]);
    // PCD文件名至少为5个字符大小字符串（因为后缀名.pcd就已经占了四个字符位置）
    if (fname.size () <= extension.size ())
      continue;

    std::transform (fname.begin (), fname.end (), fname.begin (), (int(*)(int))tolower);
    //检查参数是否为一个pcd后缀的文件
    if (fname.compare (fname.size () - extension.size (), extension.size (), extension) == 0)
    {
      //加载点云并保存在总体的点云列表中
      PCD m;
      m.f_name = argv[i];
      pcl::io::loadPCDFile (argv[i], *m.cloud);
      //从点云中移除NAN点也就是无效点
      std::vector<int> indices;
      pcl::removeNaNFromPointCloud(*m.cloud,*m.cloud, indices);

      models.push_back (m);
    }
  }
}


////////////////////////////////////////////////////////////////////////////////
/** \brief Align a pair of PointCloud datasets and return the result
  * \param cloud_src the source PointCloud
  * \param cloud_tgt the target PointCloud
  * \param output the resultant aligned source PointCloud
  * \param final_transform the resultant transform between source and target
  *//
//实现匹配，其中参数有输入一组需要配准的点云，以及是否需要进行下采样，其他参数输出配准后的点云以及变换矩阵
void pairAlign (const PointCloud::Ptr cloud_src, const PointCloud::Ptr cloud_tgt, PointCloud::Ptr output, Eigen::Matrix4f &final_transform, bool downsample = false)
{
  //
  // Downsample for consistency and speed
  // \note enable this for large datasets
  PointCloud::Ptr src (new PointCloud);   //存储滤波后的源点云
  PointCloud::Ptr tgt (new PointCloud);   //存储滤波后的目标点云
  pcl::VoxelGrid<PointT> grid;         /////滤波处理对象
  if (downsample)
  {
    grid.setLeafSize (0.05, 0.05, 0.05);    //设置滤波时采用的体素大小
    grid.setInputCloud (cloud_src);
    grid.filter (*src);

    grid.setInputCloud (cloud_tgt);
    grid.filter (*tgt);
  }
  else
  {
    src = cloud_src;
    tgt = cloud_tgt;
  }
  // 计算表面的法向量和曲率
  PointCloudWithNormals::Ptr points_with_normals_src (new PointCloudWithNormals);
  PointCloudWithNormals::Ptr points_with_normals_tgt (new PointCloudWithNormals);

  pcl::NormalEstimation<PointT, PointNormalT> norm_est;     //点云法线估计对象
  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
  norm_est.setSearchMethod (tree);
  norm_est.setKSearch (30);
  
  norm_est.setInputCloud (src);
  norm_est.compute (*points_with_normals_src);
  pcl::copyPointCloud (*src, *points_with_normals_src);

  norm_est.setInputCloud (tgt);
  norm_est.compute (*points_with_normals_tgt);
  pcl::copyPointCloud (*tgt, *points_with_normals_tgt);

  //
  // Instantiate our custom point representation (defined above) ...
  MyPointRepresentation point_representation;
  // ... and weight the 'curvature' dimension so that it is balanced against x, y, and z
  float alpha[4] = {1.0, 1.0, 1.0, 1.0};
  point_representation.setRescaleValues (alpha);

  //
  // 配准
  pcl::IterativeClosestPointNonLinear<PointNormalT, PointNormalT> reg;   // 配准对象
  reg.setTransformationEpsilon (1e-6);   ///设置收敛判断条件，越小精度越大，收敛也越慢 
  // Set the maximum distance between two correspondences (src<->tgt) to 10cm大于此值的点对不考虑
  // Note: adjust this based on the size of your datasets
  reg.setMaxCorrespondenceDistance (0.1);  
  // 设置点表示
  reg.setPointRepresentation (boost::make_shared<const MyPointRepresentation> (point_representation));

  reg.setInputSource (points_with_normals_src);   // 设置源点云
  reg.setInputTarget (points_with_normals_tgt);    // 设置目标点云
  //
  // Run the same optimization in a loop and visualize the results
  Eigen::Matrix4f Ti = Eigen::Matrix4f::Identity (), prev, targetToSource;
  PointCloudWithNormals::Ptr reg_result = points_with_normals_src;
  reg.setMaximumIterations (2);////设置最大的迭代次数，即每迭代两次就认为收敛，停止内部迭代
  for (int i = 0; i < 30; ++i)   ////手动迭代，每手动迭代一次，在配准结果视口对迭代的最新结果进行刷新显示
  {
    PCL_INFO ("Iteration Nr. %d.\n", i);

    // 存储点云以便可视化
    points_with_normals_src = reg_result;

    // Estimate
    reg.setInputSource (points_with_normals_src);
    reg.align (*reg_result);

        //accumulate transformation between each Iteration
    Ti = reg.getFinalTransformation () * Ti;

        //if the difference between this transformation and the previous one
        //is smaller than the threshold, refine the process by reducing
        //the maximal correspondence distance
    if (fabs ((reg.getLastIncrementalTransformation () - prev).sum ()) < reg.getTransformationEpsilon ())
      reg.setMaxCorrespondenceDistance (reg.getMaxCorrespondenceDistance () - 0.001);
    
    prev = reg.getLastIncrementalTransformation ();

    // visualize current state
    showCloudsRight(points_with_normals_tgt, points_with_normals_src);
  }

    //
  // Get the transformation from target to source
  targetToSource = Ti.inverse();//deidao

  //
  // Transform target back in source frame
  pcl::transformPointCloud (*cloud_tgt, *output, targetToSource);

  p->removePointCloud ("source");
  p->removePointCloud ("target");

  PointCloudColorHandlerCustom<PointT> cloud_tgt_h (output, 0, 255, 0);
  PointCloudColorHandlerCustom<PointT> cloud_src_h (cloud_src, 255, 0, 0);
  p->addPointCloud (output, cloud_tgt_h, "target", vp_2);
  p->addPointCloud (cloud_src, cloud_src_h, "source", vp_2);

    PCL_INFO ("Press q to continue the registration.\n");
  p->spin ();

  p->removePointCloud ("source"); 
  p->removePointCloud ("target");

  //add the source to the transformed target
  *output += *cloud_src;
  
  final_transform = targetToSource;
 }


int main (int argc, char** argv)
{
  // 存储管理所有打开的点云
  std::vector<PCD, Eigen::aligned_allocator<PCD> > data;
  loadData (argc, argv, data);  // 加载所有点云到data

  // 检查输入
  if (data.empty ())
  {
    PCL_ERROR ("Syntax is: %s <source.pcd> <target.pcd> [*]", argv[0]);
    PCL_ERROR ("[*] - multiple files can be added. The registration results of (i, i+1) will be registered against (i+2), etc");
    return (-1);
  }
  PCL_INFO ("Loaded %d datasets.", (int)data.size ());
  
  // 创建PCL可视化对象
  p = new pcl::visualization::PCLVisualizer (argc, argv, "Pairwise Incremental Registration example");
  p->createViewPort (0.0, 0, 0.5, 1.0, vp_1);  //用左半窗口创建视口vp_1
  p->createViewPort (0.5, 0, 1.0, 1.0, vp_2);  //用右半窗口创建视口vp_2

    PointCloud::Ptr result (new PointCloud), source, target;
  Eigen::Matrix4f GlobalTransform = Eigen::Matrix4f::Identity (), pairTransform;
  
  for (size_t i = 1; i < data.size (); ++i)   //循环处理所有点云
  {
    source = data[i-1].cloud;   //连续配准
    target = data[i].cloud;          // 相邻两组点云

    showCloudsLeft(source, target);  //可视化为配准的源和目标点云
      //调用子函数完成一组点云的配准，temp返回配准后两组点云在第一组点云坐标下的点云
    PointCloud::Ptr temp (new PointCloud);
    PCL_INFO ("Aligning %s (%d) with %s (%d).\n", data[i-1].f_name.c_str (), source->points.size (), data[i].f_name.c_str (), target->points.size ());
    // pairTransform返回从目标点云target到source的变换矩阵
    pairAlign (source, target, temp, pairTransform, true);

    //把当前两两配准后的点云temp转化到全局坐标系下返回result
    pcl::transformPointCloud (*temp, *result, GlobalTransform);

    //用当前的两组点云之间的变换更新全局变换
    GlobalTransform = GlobalTransform * pairTransform;

    //保存转换到第一个点云坐标下的当前配准后的两组点云result到文件i.pcd
    std::stringstream ss;
    ss << i << ".pcd";
    pcl::io::savePCDFile (ss.str (), *result, true);

  }
}
/* ]--- */