点云PCL中小细节

计算点与点之间的距离的平局距离

double

computeCloudResolution (const pcl::PointCloud<PointType>::ConstPtr &cloud)

{

  double res = 0.0;

  int n_points = ;

  int nres;

  std::vector<int> indices ();

  std::vector<float> sqr_distances ();

  pcl::search::KdTree<PointType> tree;

  tree.setInputCloud (cloud);

  for (size_t i = ; i < cloud->size (); ++i)

  {

    if (! pcl_isfinite ((*cloud)[i].x))

    {

      continue;

    }

    //Considering the second neighbor since the first is the point itself.

    nres = tree.nearestKSearch (i, , indices, sqr_distances);

    if (nres == )

    {

      res += sqrt (sqr_distances[]);

      ++n_points;

    }

  }

  if (n_points != )

  {

    res /= n_points;

  }

  return res;

}

PCL中

对于 pcl::UniformSampling函数在PCL1.7版本里  该函数放在keypoints
用法如下：
pcl::PointCloud<int> keypointIndices;
filter.compute(keypointIndices);
pcl::copyPointCloud(*cloud,keypointIndices.points, *filteredCloud);

之后在PCL1.8版本里就将该函数放在filters模块里。并在keypoints模块里也包含了这个头文件
#warning UniformSampling is not a Keypoint anymore, use <pcl/filters/uniform_sampling.h> instead.
这是keypoints模块下的说明
用法是：
  PointCloud<PointXYZ> output_;
 filter.filter (output_);

巴特西

点云PCL中小细节

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