[PCL]点云渐进形态学滤波

PCL支持点云的形态学滤波，四种操作：侵蚀、膨胀、开（先侵蚀后膨胀）、闭（先膨胀后侵蚀）

关于渐进的策略，在初始cell_size_ 的基础上逐渐变大。满足如下公式：

$$window\_size =cell\_size *(2*base^{k}+1)$$

$$window\_size =cell\_size *(2*base*(k+1)+1)$$

 1 // Compute the series of window sizes and height thresholds
 2   std::vector<float> height_thresholds;
 3   std::vector<float> window_sizes;
 4   int iteration = 0;
 5   float window_size = 0.0f;
 6   float height_threshold = 0.0f;
 7 
 8   while (window_size < max_window_size_)
 9   {
10     // Determine the initial window size.
11     if (exponential_)
12       window_size = cell_size_ * (2.0f * std::pow (base_, iteration) + 1.0f);
13     else
14       window_size = cell_size_ * (2.0f * (iteration+1) * base_ + 1.0f);
15 
16     // Calculate the height threshold to be used in the next iteration.
17     if (iteration == 0)
18       height_threshold = initial_distance_;
19     else
20       height_threshold = slope_ * (window_size - window_sizes[iteration-1]) * cell_size_ + initial_distance_;
21 
22     // Enforce max distance on height threshold
23     if (height_threshold > max_distance_)
24       height_threshold = max_distance_;
25 
26     window_sizes.push_back (window_size);
27     height_thresholds.push_back (height_threshold);
28 
29     iteration++;
30   }

 

在#include <pcl/filters/morphological_filter.h>中定义了枚举类型

1  enum MorphologicalOperators
2   {
3     MORPH_OPEN,
4     MORPH_CLOSE,
5     MORPH_DILATE,
6     MORPH_ERODE
7   };

 具体实现：

  1 template <typename PointT> void
  2 pcl::applyMorphologicalOperator (const typename pcl::PointCloud<PointT>::ConstPtr &cloud_in,
  3                                  float resolution, const int morphological_operator,
  4                                  pcl::PointCloud<PointT> &cloud_out)
  5 {
  6   if (cloud_in->empty ())
  7     return;
  8 
  9   pcl::copyPointCloud<PointT, PointT> (*cloud_in, cloud_out);
 10 
 11   pcl::octree::OctreePointCloudSearch<PointT> tree (resolution);
 12 
 13   tree.setInputCloud (cloud_in);
 14   tree.addPointsFromInputCloud ();
 15 
 16   float half_res = resolution / 2.0f;
 17 
 18   switch (morphological_operator)
 19   {
 20     case MORPH_DILATE:
 21     case MORPH_ERODE:
 22     {
 23       for (size_t p_idx = 0; p_idx < cloud_in->points.size (); ++p_idx)
 24       {
 25         Eigen::Vector3f bbox_min, bbox_max;
 26         std::vector<int> pt_indices;
 27         float minx = cloud_in->points[p_idx].x - half_res;
 28         float miny = cloud_in->points[p_idx].y - half_res;
 29         float minz = -std::numeric_limits<float>::max ();
 30         float maxx = cloud_in->points[p_idx].x + half_res;
 31         float maxy = cloud_in->points[p_idx].y + half_res;
 32         float maxz = std::numeric_limits<float>::max ();
 33         bbox_min = Eigen::Vector3f (minx, miny, minz);
 34         bbox_max = Eigen::Vector3f (maxx, maxy, maxz);
 35         tree.boxSearch (bbox_min, bbox_max, pt_indices);
 36 
 37         if (pt_indices.size () > 0)
 38         {
 39           Eigen::Vector4f min_pt, max_pt;
 40           pcl::getMinMax3D<PointT> (*cloud_in, pt_indices, min_pt, max_pt);
 41 
 42           switch (morphological_operator)
 43           {
 44             case MORPH_DILATE:
 45             {
 46               cloud_out.points[p_idx].z = max_pt.z ();
 47               break;
 48             }
 49             case MORPH_ERODE:
 50             {
 51               cloud_out.points[p_idx].z = min_pt.z ();
 52               break;
 53             }
 54           }
 55         }
 56       }
 57       break;
 58     }
 59     case MORPH_OPEN:
 60     case MORPH_CLOSE:
 61     {
 62       pcl::PointCloud<PointT> cloud_temp;
 63 
 64       pcl::copyPointCloud<PointT, PointT> (*cloud_in, cloud_temp);
 65 
 66       for (size_t p_idx = 0; p_idx < cloud_temp.points.size (); ++p_idx)
 67       {
 68         Eigen::Vector3f bbox_min, bbox_max;
 69         std::vector<int> pt_indices;
 70         float minx = cloud_temp.points[p_idx].x - half_res;
 71         float miny = cloud_temp.points[p_idx].y - half_res;
 72         float minz = -std::numeric_limits<float>::max ();
 73         float maxx = cloud_temp.points[p_idx].x + half_res;
 74         float maxy = cloud_temp.points[p_idx].y + half_res;
 75         float maxz = std::numeric_limits<float>::max ();
 76         bbox_min = Eigen::Vector3f (minx, miny, minz);
 77         bbox_max = Eigen::Vector3f (maxx, maxy, maxz);
 78         tree.boxSearch (bbox_min, bbox_max, pt_indices);
 79 
 80         if (pt_indices.size () > 0)
 81         {
 82           Eigen::Vector4f min_pt, max_pt;
 83           pcl::getMinMax3D<PointT> (cloud_temp, pt_indices, min_pt, max_pt);
 84 
 85           switch (morphological_operator)
 86           {
 87             case MORPH_OPEN:
 88             {
 89               cloud_out.points[p_idx].z = min_pt.z ();
 90               break;
 91             }
 92             case MORPH_CLOSE:
 93             {
 94               cloud_out.points[p_idx].z = max_pt.z ();
 95               break;
 96             }
 97           }
 98         }
 99       }
100 
101       cloud_temp.swap (cloud_out);
102 
103       for (size_t p_idx = 0; p_idx < cloud_temp.points.size (); ++p_idx)
104       {
105         Eigen::Vector3f bbox_min, bbox_max;
106         std::vector<int> pt_indices;
107         float minx = cloud_temp.points[p_idx].x - half_res;
108         float miny = cloud_temp.points[p_idx].y - half_res;
109         float minz = -std::numeric_limits<float>::max ();
110         float maxx = cloud_temp.points[p_idx].x + half_res;
111         float maxy = cloud_temp.points[p_idx].y + half_res;
112         float maxz = std::numeric_limits<float>::max ();
113         bbox_min = Eigen::Vector3f (minx, miny, minz);
114         bbox_max = Eigen::Vector3f (maxx, maxy, maxz);
115         tree.boxSearch (bbox_min, bbox_max, pt_indices);
116 
117         if (pt_indices.size () > 0)
118         {
119           Eigen::Vector4f min_pt, max_pt;
120           pcl::getMinMax3D<PointT> (cloud_temp, pt_indices, min_pt, max_pt);
121 
122           switch (morphological_operator)
123           {
124             case MORPH_OPEN:
125             default:
126             {
127               cloud_out.points[p_idx].z = max_pt.z ();
128               break;
129             }
130             case MORPH_CLOSE:
131             {
132               cloud_out.points[p_idx].z = min_pt.z ();
133               break;
134             }
135           }
136         }
137       }
138       break;
139     }
140     default:
141     {
142       PCL_ERROR ("Morphological operator is not supported!\n");
143       break;
144     }
145   }
146 
147   return;
148 }

 而渐进形态学滤波则是逐渐增大窗口，循环进行开操作

template <typename PointT> void
pcl::ProgressiveMorphologicalFilter<PointT>::extract (std::vector<int>& ground)
{
  bool segmentation_is_possible = initCompute ();
  if (!segmentation_is_possible)
  {
    deinitCompute ();
    return;
  }

  // Compute the series of window sizes and height thresholds
  std::vector<float> height_thresholds;
  std::vector<float> window_sizes;
  int iteration = 0;
  float window_size = 0.0f;
  float height_threshold = 0.0f;

  while (window_size < max_window_size_)
  {
    // Determine the initial window size.
    if (exponential_)
      window_size = cell_size_ * (2.0f * std::pow (base_, iteration) + 1.0f);
    else
      window_size = cell_size_ * (2.0f * (iteration+1) * base_ + 1.0f);

    // Calculate the height threshold to be used in the next iteration.
    if (iteration == 0)
      height_threshold = initial_distance_;
    else
      height_threshold = slope_ * (window_size - window_sizes[iteration-1]) * cell_size_ + initial_distance_;

    // Enforce max distance on height threshold
    if (height_threshold > max_distance_)
      height_threshold = max_distance_;

    window_sizes.push_back (window_size);
    height_thresholds.push_back (height_threshold);

    iteration++;
  }

  // Ground indices are initially limited to those points in the input cloud we
  // wish to process
  ground = *indices_;

  // Progressively filter ground returns using morphological open
  for (size_t i = 0; i < window_sizes.size (); ++i)
  {
    PCL_DEBUG ("      Iteration %d (height threshold = %f, window size = %f)...",
               i, height_thresholds[i], window_sizes[i]);

    // Limit filtering to those points currently considered ground returns
    typename pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>);
    pcl::copyPointCloud<PointT> (*input_, ground, *cloud);

    // Create new cloud to hold the filtered results. Apply the morphological
    // opening operation at the current window size.
    typename pcl::PointCloud<PointT>::Ptr cloud_f (new pcl::PointCloud<PointT>);
    pcl::applyMorphologicalOperator<PointT> (cloud, window_sizes[i], MORPH_OPEN, *cloud_f);

    // Find indices of the points whose difference between the source and
    // filtered point clouds is less than the current height threshold.
    std::vector<int> pt_indices;
    for (size_t p_idx = 0; p_idx < ground.size (); ++p_idx)
    {
      float diff = cloud->points[p_idx].z - cloud_f->points[p_idx].z;
      if (diff < height_thresholds[i])
        pt_indices.push_back (ground[p_idx]);
    }

    // Ground is now limited to pt_indices
    ground.swap (pt_indices);

    PCL_DEBUG ("ground now has %d points\n", ground.size ());
  }

  deinitCompute ();
}

 

posted @ 2016-10-10 16:54  太一吾鱼水  阅读(5801)  评论(0)  编辑  收藏  举报