284 lines
11 KiB
C++
284 lines
11 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2009, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_
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#define PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_
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#include <pcl/sample_consensus/mlesac.h>
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#include <cfloat> // for FLT_MAX
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#include <pcl/common/common.h> // for computeMedian
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//////////////////////////////////////////////////////////////////////////
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template <typename PointT> bool
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pcl::MaximumLikelihoodSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
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{
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// Warn and exit if no threshold was set
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if (threshold_ == std::numeric_limits<double>::max())
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{
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PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] No threshold set!\n");
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return (false);
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}
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iterations_ = 0;
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double d_best_penalty = std::numeric_limits<double>::max();
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double k = 1.0;
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Indices selection;
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Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
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std::vector<double> distances;
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// Compute sigma - remember to set threshold_ correctly !
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sigma_ = computeMedianAbsoluteDeviation (sac_model_->getInputCloud (), sac_model_->getIndices (), threshold_);
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const double dist_scaling_factor = -1.0 / (2.0 * sigma_ * sigma_); // Precompute since this does not change
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const double normalization_factor = 1.0 / (sqrt (2 * M_PI) * sigma_);
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if (debug_verbosity_level > 1)
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PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated sigma value: %f.\n", sigma_);
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// Compute the bounding box diagonal: V = sqrt (sum (max(pointCloud) - min(pointCloud)^2))
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Eigen::Vector4f min_pt, max_pt;
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getMinMax (sac_model_->getInputCloud (), sac_model_->getIndices (), min_pt, max_pt);
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max_pt -= min_pt;
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double v = sqrt (max_pt.dot (max_pt));
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int n_inliers_count = 0;
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std::size_t indices_size;
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unsigned skipped_count = 0;
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// suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
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const unsigned max_skip = max_iterations_ * 10;
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// Iterate
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while (iterations_ < k && skipped_count < max_skip)
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{
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// Get X samples which satisfy the model criteria
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sac_model_->getSamples (iterations_, selection);
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if (selection.empty ()) break;
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// Search for inliers in the point cloud for the current plane model M
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if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
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{
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//iterations_++;
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++ skipped_count;
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continue;
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}
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// Iterate through the 3d points and calculate the distances from them to the model
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sac_model_->getDistancesToModel (model_coefficients, distances);
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if (distances.empty ())
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{
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//iterations_++;
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++skipped_count;
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continue;
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}
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// Use Expectation-Maximization to find out the right value for d_cur_penalty
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// ---[ Initial estimate for the gamma mixing parameter = 1/2
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double gamma = 0.5;
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double p_outlier_prob = 0;
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indices_size = sac_model_->getIndices ()->size ();
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std::vector<double> p_inlier_prob (indices_size);
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for (int j = 0; j < iterations_EM_; ++j)
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{
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const double weighted_normalization_factor = gamma * normalization_factor;
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// Likelihood of a datum given that it is an inlier
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for (std::size_t i = 0; i < indices_size; ++i)
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p_inlier_prob[i] = weighted_normalization_factor * std::exp ( dist_scaling_factor * distances[i] * distances[i] );
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// Likelihood of a datum given that it is an outlier
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p_outlier_prob = (1 - gamma) / v;
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gamma = 0;
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for (std::size_t i = 0; i < indices_size; ++i)
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gamma += p_inlier_prob [i] / (p_inlier_prob[i] + p_outlier_prob);
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gamma /= static_cast<double>(sac_model_->getIndices ()->size ());
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}
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// Find the std::log likelihood of the model -L = -sum [std::log (pInlierProb + pOutlierProb)]
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double d_cur_penalty = 0;
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for (std::size_t i = 0; i < indices_size; ++i)
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d_cur_penalty += std::log (p_inlier_prob[i] + p_outlier_prob);
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d_cur_penalty = - d_cur_penalty;
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// Better match ?
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if (d_cur_penalty < d_best_penalty)
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{
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d_best_penalty = d_cur_penalty;
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// Save the current model/coefficients selection as being the best so far
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model_ = selection;
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model_coefficients_ = model_coefficients;
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n_inliers_count = 0;
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// Need to compute the number of inliers for this model to adapt k
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for (const double &distance : distances)
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if (distance <= 2 * sigma_)
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n_inliers_count++;
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// Compute the k parameter (k=std::log(z)/std::log(1-w^n))
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double w = static_cast<double> (n_inliers_count) / static_cast<double> (sac_model_->getIndices ()->size ());
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double p_no_outliers = 1 - std::pow (w, static_cast<double> (selection.size ()));
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p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf
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p_no_outliers = (std::min) (1 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0.
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k = std::log (1 - probability_) / std::log (p_no_outliers);
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}
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++iterations_;
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if (debug_verbosity_level > 1)
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PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
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if (iterations_ > max_iterations_)
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{
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if (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] MLESAC reached the maximum number of trials.\n");
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break;
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}
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}
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if (model_.empty ())
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{
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if (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Unable to find a solution!\n");
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return (false);
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}
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// Iterate through the 3d points and calculate the distances from them to the model again
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sac_model_->getDistancesToModel (model_coefficients_, distances);
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Indices &indices = *sac_model_->getIndices ();
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if (distances.size () != indices.size ())
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{
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PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
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return (false);
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}
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inliers_.resize (distances.size ());
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// Get the inliers for the best model found
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n_inliers_count = 0;
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for (std::size_t i = 0; i < distances.size (); ++i)
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if (distances[i] <= 2 * sigma_)
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inliers_[n_inliers_count++] = indices[i];
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// Resize the inliers vector
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inliers_.resize (n_inliers_count);
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if (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename PointT> double
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pcl::MaximumLikelihoodSampleConsensus<PointT>::computeMedianAbsoluteDeviation (
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const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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double sigma) const
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{
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std::vector<double> distances (indices->size ());
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Eigen::Vector4f median;
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// median (dist (x - median (x)))
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computeMedian (cloud, indices, median);
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for (std::size_t i = 0; i < indices->size (); ++i)
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{
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pcl::Vector4fMapConst pt = (*cloud)[(*indices)[i]].getVector4fMap ();
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Eigen::Vector4f ptdiff = pt - median;
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ptdiff[3] = 0;
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distances[i] = ptdiff.dot (ptdiff);
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}
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const double result = pcl::computeMedian (distances.begin (), distances.end (), static_cast<double(*)(double)>(std::sqrt));
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return (sigma * result);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename PointT> void
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pcl::MaximumLikelihoodSampleConsensus<PointT>::getMinMax (
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const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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Eigen::Vector4f &min_p,
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Eigen::Vector4f &max_p) const
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{
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min_p.setConstant (FLT_MAX);
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max_p.setConstant (-FLT_MAX);
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min_p[3] = max_p[3] = 0;
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for (std::size_t i = 0; i < indices->size (); ++i)
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{
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if ((*cloud)[(*indices)[i]].x < min_p[0]) min_p[0] = (*cloud)[(*indices)[i]].x;
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if ((*cloud)[(*indices)[i]].y < min_p[1]) min_p[1] = (*cloud)[(*indices)[i]].y;
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if ((*cloud)[(*indices)[i]].z < min_p[2]) min_p[2] = (*cloud)[(*indices)[i]].z;
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if ((*cloud)[(*indices)[i]].x > max_p[0]) max_p[0] = (*cloud)[(*indices)[i]].x;
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if ((*cloud)[(*indices)[i]].y > max_p[1]) max_p[1] = (*cloud)[(*indices)[i]].y;
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if ((*cloud)[(*indices)[i]].z > max_p[2]) max_p[2] = (*cloud)[(*indices)[i]].z;
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename PointT> void
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pcl::MaximumLikelihoodSampleConsensus<PointT>::computeMedian (
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const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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Eigen::Vector4f &median) const
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{
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// Copy the values to vectors for faster sorting
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std::vector<float> x (indices->size ());
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std::vector<float> y (indices->size ());
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std::vector<float> z (indices->size ());
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for (std::size_t i = 0; i < indices->size (); ++i)
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{
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x[i] = (*cloud)[(*indices)[i]].x;
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y[i] = (*cloud)[(*indices)[i]].y;
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z[i] = (*cloud)[(*indices)[i]].z;
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}
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median[0] = pcl::computeMedian (x.begin(), x.end());
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median[1] = pcl::computeMedian (y.begin(), y.end());
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median[2] = pcl::computeMedian (z.begin(), z.end());
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median[3] = 0;
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}
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#define PCL_INSTANTIATE_MaximumLikelihoodSampleConsensus(T) template class PCL_EXPORTS pcl::MaximumLikelihoodSampleConsensus<T>;
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#endif // PCL_SAMPLE_CONSENSUS_IMPL_MLESAC_H_
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