166 lines
6.7 KiB
C++
166 lines
6.7 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_RRANSAC_H_
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#define PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
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#include <pcl/sample_consensus/rransac.h>
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//////////////////////////////////////////////////////////////////////////
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template <typename PointT> bool
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pcl::RandomizedRandomSampleConsensus<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::RandomizedRandomSampleConsensus::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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std::size_t n_best_inliers_count = 0;
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double k = std::numeric_limits<double>::max();
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Indices selection;
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Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
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std::set<index_t> indices_subset;
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const double log_probability = std::log (1.0 - probability_);
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const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
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std::size_t n_inliers_count;
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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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// Number of samples to try randomly
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const std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
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// Iterate
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while (iterations_ < k)
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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 ())
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{
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PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No samples could be selected!\n");
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break;
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}
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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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if (skipped_count < max_skip)
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{
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PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, so continue with next iteration.\n");
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continue;
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}
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else
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{
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PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, and RRANSAC reached the maximum number of trials.\n");
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break;
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}
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}
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// RRANSAC addon: verify a random fraction of the data
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// Get X random samples which satisfy the model criterion
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this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
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if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
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{
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++iterations_;
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PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function doSamplesVerifyModel failed, so continue with next iteration.\n");
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continue;
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}
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// Select the inliers that are within threshold_ from the model
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n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_);
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// Better match ?
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if (n_inliers_count > n_best_inliers_count)
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{
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n_best_inliers_count = n_inliers_count;
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// Save the current model/inlier/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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// Compute the k parameter (k=std::log(z)/std::log(1-w^n))
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const double w = static_cast<double> (n_inliers_count) * one_over_indices;
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double p_no_outliers = 1.0 - 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) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0.
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k = log_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::RandomizedRandomSampleConsensus::computeModel] Trial %d out of %d: %u inliers (best is: %u so far).\n", iterations_, static_cast<int> (std::ceil (k)), n_inliers_count, n_best_inliers_count);
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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::RandomizedRandomSampleConsensus::computeModel] RRANSAC 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 (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Model: %lu size, %u inliers.\n", model_.size (), n_best_inliers_count);
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if (model_.empty ())
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{
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PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC found no model.\n");
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inliers_.clear ();
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return (false);
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}
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// Get the set of inliers that correspond to the best model found so far
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sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
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return (true);
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}
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#define PCL_INSTANTIATE_RandomizedRandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedRandomSampleConsensus<T>;
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#endif // PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
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