Point Cloud Library (PCL)  1.13.1-dev
statistical_outlier_removal.hpp
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39 
40 #ifndef PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
41 #define PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
42 
43 #include <pcl/filters/statistical_outlier_removal.h>
44 #include <pcl/search/organized.h> // for OrganizedNeighbor
45 #include <pcl/search/kdtree.h> // for KdTree
46 
47 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
48 template <typename PointT> void
50 {
51  // Initialize the search class
52  if (!searcher_)
53  {
54  if (input_->isOrganized ())
55  searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
56  else
57  searcher_.reset (new pcl::search::KdTree<PointT> (false));
58  }
59  searcher_->setInputCloud (input_);
60 
61  // The arrays to be used
62  const int searcher_k = mean_k_ + 1; // Find one more, since results include the query point.
63  Indices nn_indices (searcher_k);
64  std::vector<float> nn_dists (searcher_k);
65  std::vector<float> distances (indices_->size ());
66  indices.resize (indices_->size ());
67  removed_indices_->resize (indices_->size ());
68  int oii = 0, rii = 0; // oii = output indices iterator, rii = removed indices iterator
69 
70  // First pass: Compute the mean distances for all points with respect to their k nearest neighbors
71  int valid_distances = 0;
72  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii) // iii = input indices iterator
73  {
74  if (!std::isfinite ((*input_)[(*indices_)[iii]].x) ||
75  !std::isfinite ((*input_)[(*indices_)[iii]].y) ||
76  !std::isfinite ((*input_)[(*indices_)[iii]].z))
77  {
78  distances[iii] = 0.0;
79  continue;
80  }
81 
82  // Perform the nearest k search
83  if (searcher_->nearestKSearch ((*indices_)[iii], searcher_k, nn_indices, nn_dists) == 0)
84  {
85  distances[iii] = 0.0;
86  PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);
87  continue;
88  }
89 
90  // Calculate the mean distance to its neighbors
91  double dist_sum = 0.0;
92  for (int k = 1; k < searcher_k; ++k) // k = 0 is the query point
93  dist_sum += sqrt (nn_dists[k]);
94  distances[iii] = static_cast<float> (dist_sum / mean_k_);
95  valid_distances++;
96  }
97 
98  // Estimate the mean and the standard deviation of the distance vector
99  double sum = 0, sq_sum = 0;
100  for (const float &distance : distances)
101  {
102  sum += distance;
103  sq_sum += distance * distance;
104  }
105  double mean = sum / static_cast<double>(valid_distances);
106  double variance = (sq_sum - sum * sum / static_cast<double>(valid_distances)) / (static_cast<double>(valid_distances) - 1);
107  double stddev = sqrt (variance);
108  //getMeanStd (distances, mean, stddev);
109 
110  double distance_threshold = mean + std_mul_ * stddev;
111 
112  // Second pass: Classify the points on the computed distance threshold
113  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii) // iii = input indices iterator
114  {
115  // Points having a too high average distance are outliers and are passed to removed indices
116  // Unless negative was set, then it's the opposite condition
117  if ((!negative_ && distances[iii] > distance_threshold) || (negative_ && distances[iii] <= distance_threshold))
118  {
119  if (extract_removed_indices_)
120  (*removed_indices_)[rii++] = (*indices_)[iii];
121  continue;
122  }
123 
124  // Otherwise it was a normal point for output (inlier)
125  indices[oii++] = (*indices_)[iii];
126  }
127 
128  // Resize the output arrays
129  indices.resize (oii);
130  removed_indices_->resize (rii);
131 }
132 
133 #define PCL_INSTANTIATE_StatisticalOutlierRemoval(T) template class PCL_EXPORTS pcl::StatisticalOutlierRemoval<T>;
134 
135 #endif // PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
136 
void applyFilterIndices(Indices &indices)
Filtered results are indexed by an indices array.
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
OrganizedNeighbor is a class for optimized nearest neighbor search in organized point clouds.
Definition: organized.h:61
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133