PCL移动最小二乘平滑MLS

利用移动最小二乘法进行平滑采样。
代码如下：

#include <pcl/PCLPointCloud2.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>
#include <pcl/surface/mls.h>
#include <pcl/filters/voxel_grid.h>

using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;

int default_polynomial_order = 2;
bool default_use_polynomial_fit = false;
double default_search_radius = 0.0,
    default_sqr_gauss_param = 0.0;


void
printHelp (int, char **argv)
{
  print_error ("Syntax is: %s input.pcd output.pcd <options>\n", argv[0]);
  print_info ("  where options are:\n");
  print_info ("                     -radius X          = sphere radius to be used for finding the k-nearest neighbors used for fitting (default: ");
  print_value ("%f", default_search_radius); print_info (")\n");
  print_info ("                     -sqr_gauss_param X = parameter used for the distance based weighting of neighbors (recommended = search_radius^2) (default: ");
  print_value ("%f", default_sqr_gauss_param); print_info (")\n");
  print_info ("                     -use_polynomial_fit X = decides whether the surface and normal are approximated using a polynomial or only via tangent estimation (default: ");
  print_value ("%d", default_use_polynomial_fit); print_info (")\n");
  print_info ("                     -polynomial_order X = order of the polynomial to be fit (implicitly, use_polynomial_fit = 1) (default: ");
  print_value ("%d", default_polynomial_order); print_info (")\n");
}

bool
loadCloud (const std::string &filename, pcl::PCLPointCloud2 &cloud)
{
  TicToc tt;
  print_highlight ("Loading "); print_value ("%s ", filename.c_str ());

  tt.tic ();
  if (loadPCDFile (filename, cloud) < 0)
    return (false);
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", cloud.width * cloud.height); print_info (" points]\n");
  print_info ("Available dimensions: "); print_value ("%s\n", pcl::getFieldsList (cloud).c_str ());

  return (true);
}

void
compute (const pcl::PCLPointCloud2::ConstPtr &input, pcl::PCLPointCloud2 &output,
         double search_radius, bool sqr_gauss_param_set, double sqr_gauss_param,
         bool use_polynomial_fit, int polynomial_order)
{

  PointCloud<PointXYZ>::Ptr xyz_cloud_pre (new pcl::PointCloud<PointXYZ> ()),
      xyz_cloud (new pcl::PointCloud<PointXYZ> ());
  fromPCLPointCloud2 (*input, *xyz_cloud_pre);

  // Filter the NaNs from the cloud
  for (size_t i = 0; i < xyz_cloud_pre->size (); ++i)
    if (pcl_isfinite (xyz_cloud_pre->points[i].x))
      xyz_cloud->push_back (xyz_cloud_pre->points[i]);
  xyz_cloud->header = xyz_cloud_pre->header;
  xyz_cloud->height = 1;
  xyz_cloud->width = static_cast<uint32_t> (xyz_cloud->size ());
  xyz_cloud->is_dense = false;
  
  

  PointCloud<PointNormal>::Ptr xyz_cloud_smoothed (new PointCloud<PointNormal> ());

  MovingLeastSquares<PointXYZ, PointNormal> mls;
  mls.setInputCloud (xyz_cloud);
  mls.setSearchRadius (search_radius);
  if (sqr_gauss_param_set) mls.setSqrGaussParam (sqr_gauss_param);
  mls.setPolynomialFit (use_polynomial_fit);
  mls.setPolynomialOrder (polynomial_order);

//  mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::SAMPLE_LOCAL_PLANE);
//  mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::RANDOM_UNIFORM_DENSITY);
//  mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::VOXEL_GRID_DILATION);
  mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::NONE);
  mls.setPointDensity (60000 * int (search_radius)); // 300 points in a 5 cm radius
  mls.setUpsamplingRadius (0.025);
  mls.setUpsamplingStepSize (0.015);
  mls.setDilationIterations (2);
  mls.setDilationVoxelSize (0.01f);

  search::KdTree<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ> ());
  mls.setSearchMethod (tree);
  mls.setComputeNormals (true);

  PCL_INFO ("Computing smoothed surface and normals with search_radius %f , sqr_gaussian_param %f, polynomial fitting %d, polynomial order %d\n",
            mls.getSearchRadius(), mls.getSqrGaussParam(), mls.getPolynomialFit(), mls.getPolynomialOrder());
  TicToc tt;
  tt.tic ();
  mls.process (*xyz_cloud_smoothed);
  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", xyz_cloud_smoothed->width * xyz_cloud_smoothed->height); print_info (" points]\n");

  toPCLPointCloud2 (*xyz_cloud_smoothed, output);
}

void
saveCloud (const std::string &filename, const pcl::PCLPointCloud2 &output)
{
  TicToc tt;
  tt.tic ();

  print_highlight ("Saving "); print_value ("%s ", filename.c_str ());

  pcl::io::savePCDFile (filename, output,  Eigen::Vector4f::Zero (),
                        Eigen::Quaternionf::Identity (), true);

  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", output.width * output.height); print_info (" points]\n");
}

/* ---[ */
int
main (int argc, char** argv)
{
  print_info ("Moving Least Squares smoothing of a point cloud. For more information, use: %s -h\n", argv[0]);

  if (argc < 3)
  {
    printHelp (argc, argv);
    return (-1);
  }

  // Parse the command line arguments for .pcd files
  std::vector<int> p_file_indices;
  p_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
  if (p_file_indices.size () != 2)
  {
    print_error ("Need one input PCD file and one output PCD file to continue.\n");
    return (-1);
  }

  // Command line parsing
  double search_radius = default_search_radius;
  double sqr_gauss_param = default_sqr_gauss_param;
  bool sqr_gauss_param_set = true;
  int polynomial_order = default_polynomial_order;
  bool use_polynomial_fit = default_use_polynomial_fit;

  parse_argument (argc, argv, "-radius", search_radius);
  if (parse_argument (argc, argv, "-sqr_gauss_param", sqr_gauss_param) == -1)
    sqr_gauss_param_set = false;
  if (parse_argument (argc, argv, "-polynomial_order", polynomial_order) != -1 )
    use_polynomial_fit = true;
  parse_argument (argc, argv, "-use_polynomial_fit", use_polynomial_fit);

  // Load the first file
  pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
  if (!loadCloud (argv[p_file_indices[0]], *cloud))
    return (-1);

  // Do the smoothing
  pcl::PCLPointCloud2 output;
  compute (cloud, output, search_radius, sqr_gauss_param_set, sqr_gauss_param,
           use_polynomial_fit, polynomial_order);

  // Save into the second file
  saveCloud (argv[p_file_indices[1]], output);
}

来源：PCL官方示例

标签：polynomial,info,mls,xyz,MLS,二乘,PCL,print,cloud
来源： https://blog.csdn.net/com1098247427/article/details/120704972