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| StatisticalOutlierRemoval (bool extract_removed_indices=false) |
| Constructor.
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void | setMeanK (int nr_k) |
| Set the number of nearest neighbors to use for mean distance estimation.
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int | getMeanK () |
| Get the number of nearest neighbors to use for mean distance estimation.
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void | setStddevMulThresh (double stddev_mult) |
| Set the standard deviation multiplier for the distance threshold calculation.
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double | getStddevMulThresh () |
| Get the standard deviation multiplier for the distance threshold calculation.
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| FilterIndices (bool extract_removed_indices=false) |
| Constructor.
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void | filter (Indices &indices) |
| Calls the filtering method and returns the filtered point cloud indices.
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void | setNegative (bool negative) |
| Set whether the regular conditions for points filtering should apply, or the inverted conditions.
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bool | getNegative () const |
| Get whether the regular conditions for points filtering should apply, or the inverted conditions.
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void | setKeepOrganized (bool keep_organized) |
| Set whether the filtered points should be kept and set to the value given through setUserFilterValue (default: NaN), or removed from the PointCloud, thus potentially breaking its organized structure.
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bool | getKeepOrganized () const |
| Get whether the filtered points should be kept and set to the value given through setUserFilterValue (default = NaN), or removed from the PointCloud, thus potentially breaking its organized structure.
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void | setUserFilterValue (float value) |
| Provide a value that the filtered points should be set to instead of removing them.
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| Filter (bool extract_removed_indices=false) |
| Empty constructor.
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IndicesConstPtr const | getRemovedIndices () const |
| Get the point indices being removed.
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void | getRemovedIndices (PointIndices &pi) |
| Get the point indices being removed.
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void | filter (PointCloud &output) |
| Calls the filtering method and returns the filtered dataset in output.
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| PCLBase () |
| Empty constructor.
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| PCLBase (const PCLBase &base) |
| Copy constructor.
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virtual | ~PCLBase ()=default |
| Destructor.
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virtual void | setInputCloud (const PointCloudConstPtr &cloud) |
| Provide a pointer to the input dataset.
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PointCloudConstPtr const | getInputCloud () const |
| Get a pointer to the input point cloud dataset.
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virtual void | setIndices (const IndicesPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data.
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virtual void | setIndices (const IndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data.
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virtual void | setIndices (const PointIndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data.
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virtual void | setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols) |
| Set the indices for the points laying within an interest region of the point cloud.
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IndicesPtr | getIndices () |
| Get a pointer to the vector of indices used.
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IndicesConstPtr const | getIndices () const |
| Get a pointer to the vector of indices used.
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const PointT & | operator[] (std::size_t pos) const |
| Override PointCloud operator[] to shorten code.
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void | applyFilter (Indices &indices) override |
| Filtered results are indexed by an indices array.
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void | applyFilterIndices (Indices &indices) |
| Filtered results are indexed by an indices array.
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virtual void | applyFilter (Indices &indices)=0 |
| Abstract filter method for point cloud indices.
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void | applyFilter (PointCloud &output) override |
| Abstract filter method for point cloud.
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virtual void | applyFilter (PointCloud &output)=0 |
| Abstract filter method.
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const std::string & | getClassName () const |
| Get a string representation of the name of this class.
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bool | initCompute () |
| This method should get called before starting the actual computation.
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bool | deinitCompute () |
| This method should get called after finishing the actual computation.
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template<typename
PointT>
class pcl::StatisticalOutlierRemoval< PointT >
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
The algorithm iterates through the entire input twice: During the first iteration it will compute the average distance that each point has to its nearest k neighbors. The value of k can be set using setMeanK(). Next, the mean and standard deviation of all these distances are computed in order to determine a distance threshold. The distance threshold will be equal to: mean + stddev_mult * stddev. The multiplier for the standard deviation can be set using setStddevMulThresh(). During the next iteration the points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
The neighbors found for each query point will be found amongst ALL points of setInputCloud(), not just those indexed by setIndices(). The setIndices() method only indexes the points that will be iterated through as search query points.
For more information:
- R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz. Towards 3D Point Cloud Based Object Maps for Household Environments Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.
Usage example:
sorfilter.setInputCloud (cloud_in);
sorfilter.setMeanK (8);
sorfilter.setStddevMulThresh (1.0);
sorfilter.filter (*cloud_out);
indices_rem = sorfilter.getRemovedIndices ();
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
- Author
- Radu Bogdan Rusu
Definition at line 80 of file statistical_outlier_removal.h.