191 lines
6.8 KiB
C++
191 lines
6.8 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_LMEDS_H_
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#define PCL_SAMPLE_CONSENSUS_IMPL_LMEDS_H_
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#include <pcl/sample_consensus/lmeds.h>
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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::LeastMedianSquares<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::LeastMedianSquares::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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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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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_ < max_iterations_) && (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 ())
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{
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PCL_ERROR ("[pcl::LeastMedianSquares::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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PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] The function computeModelCoefficients failed, so continue with next iteration.\n");
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continue;
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}
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double d_cur_penalty;
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// d_cur_penalty = sum (min (dist, threshold))
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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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// No distances? The model must not respect the user given constraints
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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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// Move all NaNs in distances to the end
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const auto new_end = (sac_model_->getInputCloud()->is_dense ? distances.end() : std::partition (distances.begin(), distances.end(), [](double d){return !std::isnan (d);}));
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const auto nr_valid_dists = std::distance (distances.begin (), new_end);
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// d_cur_penalty = median (distances)
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PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] There are %lu valid distances remaining after removing NaN values.\n", nr_valid_dists);
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if (nr_valid_dists == 0)
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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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d_cur_penalty = pcl::computeMedian (distances.begin (), new_end, static_cast<double(*)(double)>(std::sqrt));
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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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}
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++iterations_;
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if (debug_verbosity_level > 1)
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{
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PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, max_iterations_, d_best_penalty);
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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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{
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PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Unable to find a solution!\n");
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}
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return (false);
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}
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// Classify the data points into inliers and outliers
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// Sigma = 1.4826 * (1 + 5 / (n-d)) * sqrt (M)
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// @note: See "Robust Regression Methods for Computer Vision: A Review"
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//double sigma = 1.4826 * (1 + 5 / (sac_model_->getIndices ()->size () - best_model.size ())) * sqrt (d_best_penalty);
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//double threshold = 2.5 * sigma;
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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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// No distances? The model must not respect the user given constraints
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if (distances.empty ())
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{
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PCL_ERROR ("[pcl::LeastMedianSquares::computeModel] The model found failed to verify against the given constraints!\n");
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return (false);
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}
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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::LeastMedianSquares::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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std::size_t n_inliers_count = 0;
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for (std::size_t i = 0; i < distances.size (); ++i)
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{
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if (distances[i] <= threshold_)
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{
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inliers_[n_inliers_count++] = indices[i];
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}
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}
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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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{
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PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Model: %lu size, %lu inliers.\n", model_.size (), n_inliers_count);
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}
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return (true);
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}
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#define PCL_INSTANTIATE_LeastMedianSquares(T) template class PCL_EXPORTS pcl::LeastMedianSquares<T>;
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#endif // PCL_SAMPLE_CONSENSUS_IMPL_LMEDS_H_
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