标签：include PCL ICP 配准 argv pcl icp pcd Cloud

针对二维数据配准，需要控制的是变换矩阵的估计，一个旋转，两个平移为3D参数。更改ICP默认的估计变换矩阵的方法即可，代码中使用TransformationEstimationLM方法并且通过
te->setWarpFunction (warp_fcn);控制点云为3D变换。

setWarpFunction默认为6D变换

代码如下：

#include <pcl/console/parse.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/icp_nl.h>
#include <pcl/registration/transformation_estimation_lm.h>
#include <pcl/registration/warp_point_rigid_3d.h>

#include <string>
#include <iostream>
#include <fstream>
#include <vector>

typedef pcl::PointXYZ PointType;
typedef pcl::PointCloud<PointType> Cloud;
typedef Cloud::ConstPtr CloudConstPtr;
typedef Cloud::Ptr CloudPtr;

int
main (int argc, char **argv)
{
  double dist = 0.05;
  pcl::console::parse_argument (argc, argv, "-d", dist);

  double rans = 0.05;
  pcl::console::parse_argument (argc, argv, "-r", rans);

  int iter = 50;
  pcl::console::parse_argument (argc, argv, "-i", iter);

  std::vector<int> pcd_indices;
  pcd_indices = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");

  CloudPtr model (new Cloud);
  if (pcl::io::loadPCDFile (argv[pcd_indices[0]], *model) == -1)
  {
    std::cout << "Could not read file" << std::endl;
    return -1;
  }
  std::cout << argv[pcd_indices[0]] << " width: " << model->width << " height: " << model->height << std::endl;

  std::string result_filename (argv[pcd_indices[0]]);
  result_filename = result_filename.substr (result_filename.rfind ("/") + 1);
  pcl::io::savePCDFile (result_filename.c_str (), *model);
  std::cout << "saving first model to " << result_filename << std::endl;

  Eigen::Matrix4f t (Eigen::Matrix4f::Identity ());

  for (size_t i = 1; i < pcd_indices.size (); i++)
  {
    CloudPtr data (new Cloud);
    if (pcl::io::loadPCDFile (argv[pcd_indices[i]], *data) == -1)
    {
      std::cout << "Could not read file" << std::endl;
      return -1;
    }
    std::cout << argv[pcd_indices[i]] << " width: " << data->width << " height: " << data->height << std::endl;

    pcl::IterativeClosestPointNonLinear<PointType, PointType> icp;

    boost::shared_ptr<pcl::registration::WarpPointRigid3D<PointType, PointType> > warp_fcn 
      (new pcl::registration::WarpPointRigid3D<PointType, PointType>);

    // Create a TransformationEstimationLM object, and set the warp to it
    boost::shared_ptr<pcl::registration::TransformationEstimationLM<PointType, PointType> > te (new pcl::registration::TransformationEstimationLM<PointType, PointType>);
    te->setWarpFunction (warp_fcn);

    // Pass the TransformationEstimation objec to the ICP algorithm
    icp.setTransformationEstimation (te);

    icp.setMaximumIterations (iter);
    icp.setMaxCorrespondenceDistance (dist);
    icp.setRANSACOutlierRejectionThreshold (rans);

    icp.setInputTarget (model);

    icp.setInputSource (data);

    CloudPtr tmp (new Cloud);
    icp.align (*tmp);

    t = t * icp.getFinalTransformation ();

    pcl::transformPointCloud (*data, *tmp, t);

    std::cout << icp.getFinalTransformation () << std::endl;

    *model = *data;

    std::string result_filename (argv[pcd_indices[i]]);
    result_filename = result_filename.substr (result_filename.rfind ("/") + 1);
    pcl::io::savePCDFileBinary (result_filename.c_str (), *tmp);
    std::cout << "saving result to " << result_filename << std::endl;
  }

  return 0;
}

来源：PCL官方示例

标签：include,PCL,ICP,配准,argv,pcl,icp,pcd,Cloud
来源： https://blog.csdn.net/com1098247427/article/details/120698287

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。