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- /*
- * Software License Agreement (BSD License)
- *
- * Point Cloud Library (PCL) - www.pointclouds.org
- * Copyright (c) 2010-2012, Willow Garage, Inc.
- *
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.
- * * Neither the name of the copyright holder(s) nor the names of its
- * contributors may be used to endorse or promote products derived
- * from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
- * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
- * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
- * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- * POSSIBILITY OF SUCH DAMAGE.
- *
- * $Id$
- *
- */
- #ifndef PCL_FILTERS_STATISTICAL_OUTLIER_REMOVAL_H_
- #define PCL_FILTERS_STATISTICAL_OUTLIER_REMOVAL_H_
- #include <pcl/filters/filter_indices.h>
- #include <pcl/search/pcl_search.h>
- namespace pcl
- {
- /** \brief @b StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
- * \details 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.
- * <br>
- * 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.
- * <br><br>
- * 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.
- * <br><br>
- * Usage example:
- * \code
- * pcl::StatisticalOutlierRemoval<PointType> sorfilter (true); // Initializing with true will allow us to extract the removed indices
- * sorfilter.setInputCloud (cloud_in);
- * sorfilter.setMeanK (8);
- * sorfilter.setStddevMulThresh (1.0);
- * sorfilter.filter (*cloud_out);
- * // The resulting cloud_out contains all points of cloud_in that have an average distance to their 8 nearest neighbors that is below the computed threshold
- * // Using a standard deviation multiplier of 1.0 and assuming the average distances are normally distributed there is a 84.1% chance that a point will be an inlier
- * indices_rem = sorfilter.getRemovedIndices ();
- * // The indices_rem array indexes all points of cloud_in that are outliers
- * \endcode
- * \author Radu Bogdan Rusu
- * \ingroup filters
- */
- template<typename PointT>
- class StatisticalOutlierRemoval : public FilterIndices<PointT>
- {
- protected:
- typedef typename FilterIndices<PointT>::PointCloud PointCloud;
- typedef typename PointCloud::Ptr PointCloudPtr;
- typedef typename PointCloud::ConstPtr PointCloudConstPtr;
- typedef typename pcl::search::Search<PointT>::Ptr SearcherPtr;
- public:
- typedef boost::shared_ptr< StatisticalOutlierRemoval<PointT> > Ptr;
- typedef boost::shared_ptr< const StatisticalOutlierRemoval<PointT> > ConstPtr;
- /** \brief Constructor.
- * \param[in] extract_removed_indices Set to true if you want to be able to extract the indices of points being removed (default = false).
- */
- StatisticalOutlierRemoval (bool extract_removed_indices = false) :
- FilterIndices<PointT>::FilterIndices (extract_removed_indices),
- searcher_ (),
- mean_k_ (1),
- std_mul_ (0.0)
- {
- filter_name_ = "StatisticalOutlierRemoval";
- }
- /** \brief Set the number of nearest neighbors to use for mean distance estimation.
- * \param[in] nr_k The number of points to use for mean distance estimation.
- */
- inline void
- setMeanK (int nr_k)
- {
- mean_k_ = nr_k;
- }
- /** \brief Get the number of nearest neighbors to use for mean distance estimation.
- * \return The number of points to use for mean distance estimation.
- */
- inline int
- getMeanK ()
- {
- return (mean_k_);
- }
- /** \brief Set the standard deviation multiplier for the distance threshold calculation.
- * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
- * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
- * \param[in] stddev_mult The standard deviation multiplier.
- */
- inline void
- setStddevMulThresh (double stddev_mult)
- {
- std_mul_ = stddev_mult;
- }
- /** \brief Get the standard deviation multiplier for the distance threshold calculation.
- * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
- * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
- */
- inline double
- getStddevMulThresh ()
- {
- return (std_mul_);
- }
- protected:
- using PCLBase<PointT>::input_;
- using PCLBase<PointT>::indices_;
- using Filter<PointT>::filter_name_;
- using Filter<PointT>::getClassName;
- using FilterIndices<PointT>::negative_;
- using FilterIndices<PointT>::keep_organized_;
- using FilterIndices<PointT>::user_filter_value_;
- using FilterIndices<PointT>::extract_removed_indices_;
- using FilterIndices<PointT>::removed_indices_;
- /** \brief Filtered results are stored in a separate point cloud.
- * \param[out] output The resultant point cloud.
- */
- void
- applyFilter (PointCloud &output);
- /** \brief Filtered results are indexed by an indices array.
- * \param[out] indices The resultant indices.
- */
- void
- applyFilter (std::vector<int> &indices)
- {
- applyFilterIndices (indices);
- }
- /** \brief Filtered results are indexed by an indices array.
- * \param[out] indices The resultant indices.
- */
- void
- applyFilterIndices (std::vector<int> &indices);
- private:
- /** \brief A pointer to the spatial search object. */
- SearcherPtr searcher_;
- /** \brief The number of points to use for mean distance estimation. */
- int mean_k_;
- /** \brief Standard deviations threshold (i.e., points outside of
- * \f$ \mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers). */
- double std_mul_;
- };
- /** \brief @b StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more
- * information check:
- * - 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.
- *
- * \note setFilterFieldName (), setFilterLimits (), and setFilterLimitNegative () are ignored.
- * \author Radu Bogdan Rusu
- * \ingroup filters
- */
- template<>
- class PCL_EXPORTS StatisticalOutlierRemoval<pcl::PCLPointCloud2> : public FilterIndices<pcl::PCLPointCloud2>
- {
- using FilterIndices<pcl::PCLPointCloud2>::filter_name_;
- using FilterIndices<pcl::PCLPointCloud2>::getClassName;
- using FilterIndices<pcl::PCLPointCloud2>::removed_indices_;
- using FilterIndices<pcl::PCLPointCloud2>::extract_removed_indices_;
- typedef pcl::search::Search<pcl::PointXYZ> KdTree;
- typedef pcl::search::Search<pcl::PointXYZ>::Ptr KdTreePtr;
- typedef pcl::PCLPointCloud2 PCLPointCloud2;
- typedef PCLPointCloud2::Ptr PCLPointCloud2Ptr;
- typedef PCLPointCloud2::ConstPtr PCLPointCloud2ConstPtr;
- public:
- /** \brief Empty constructor. */
- StatisticalOutlierRemoval (bool extract_removed_indices = false) :
- FilterIndices<pcl::PCLPointCloud2>::FilterIndices (extract_removed_indices), mean_k_ (2),
- std_mul_ (0.0), tree_ ()
- {
- filter_name_ = "StatisticalOutlierRemoval";
- }
- /** \brief Set the number of points (k) to use for mean distance estimation
- * \param nr_k the number of points to use for mean distance estimation
- */
- inline void
- setMeanK (int nr_k)
- {
- mean_k_ = nr_k;
- }
- /** \brief Get the number of points to use for mean distance estimation. */
- inline int
- getMeanK ()
- {
- return (mean_k_);
- }
- /** \brief Set the standard deviation multiplier threshold. All points outside the
- * \f[ \mu \pm \sigma \cdot std\_mul \f]
- * will be considered outliers, where \f$ \mu \f$ is the estimated mean,
- * and \f$ \sigma \f$ is the standard deviation.
- * \param std_mul the standard deviation multiplier threshold
- */
- inline void
- setStddevMulThresh (double std_mul)
- {
- std_mul_ = std_mul;
- }
- /** \brief Get the standard deviation multiplier threshold as set by the user. */
- inline double
- getStddevMulThresh ()
- {
- return (std_mul_);
- }
- protected:
- /** \brief The number of points to use for mean distance estimation. */
- int mean_k_;
- /** \brief Standard deviations threshold (i.e., points outside of
- * \f$ \mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers).
- */
- double std_mul_;
- /** \brief A pointer to the spatial search object. */
- KdTreePtr tree_;
- virtual void
- applyFilter (std::vector<int> &indices);
- virtual void
- applyFilter (PCLPointCloud2 &output);
- /**
- * \brief Compute the statistical values used in both applyFilter methods.
- *
- * This method tries to avoid duplicate code.
- */
- virtual void
- generateStatistics (double& mean, double& variance, double& stddev, std::vector<float>& distances);
- };
- }
- #ifdef PCL_NO_PRECOMPILE
- #include <pcl/filters/impl/statistical_outlier_removal.hpp>
- #endif
- #endif // PCL_FILTERS_STATISTICAL_OUTLIER_REMOVAL_H_
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