Point Cloud Library (PCL) 1.13.0
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
48template <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 neigbhor 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