184 lines
7.0 KiB
C++
184 lines
7.0 KiB
C++
/*
|
|
* Software License Agreement (BSD License)
|
|
*
|
|
* Point Cloud Library (PCL) - www.pointclouds.org
|
|
* Copyright (c) 2009, Willow Garage, Inc.
|
|
* Copyright (c) 2012-, Open Perception, 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_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
|
|
#define PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
|
|
|
|
#include <pcl/sample_consensus/rmsac.h>
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename PointT> bool
|
|
pcl::RandomizedMEstimatorSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
|
|
{
|
|
// Warn and exit if no threshold was set
|
|
if (threshold_ == std::numeric_limits<double>::max())
|
|
{
|
|
PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] No threshold set!\n");
|
|
return (false);
|
|
}
|
|
|
|
iterations_ = 0;
|
|
double d_best_penalty = std::numeric_limits<double>::max();
|
|
double k = 1.0;
|
|
|
|
Indices selection;
|
|
Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
|
|
std::vector<double> distances;
|
|
std::set<index_t> indices_subset;
|
|
|
|
int n_inliers_count = 0;
|
|
unsigned skipped_count = 0;
|
|
// suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
|
|
const unsigned max_skip = max_iterations_ * 10;
|
|
|
|
// Number of samples to try randomly
|
|
std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
|
|
|
|
// Iterate
|
|
while (iterations_ < k && skipped_count < max_skip)
|
|
{
|
|
// Get X samples which satisfy the model criteria
|
|
sac_model_->getSamples (iterations_, selection);
|
|
|
|
if (selection.empty ()) break;
|
|
|
|
// Search for inliers in the point cloud for the current plane model M
|
|
if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
|
|
{
|
|
//iterations_++;
|
|
++ skipped_count;
|
|
continue;
|
|
}
|
|
|
|
// RMSAC addon: verify a random fraction of the data
|
|
// Get X random samples which satisfy the model criterion
|
|
this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
|
|
|
|
if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
|
|
{
|
|
// Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented
|
|
if (k != 1.0)
|
|
{
|
|
++iterations_;
|
|
continue;
|
|
}
|
|
}
|
|
|
|
double d_cur_penalty = 0;
|
|
// Iterate through the 3d points and calculate the distances from them to the model
|
|
sac_model_->getDistancesToModel (model_coefficients, distances);
|
|
|
|
if (distances.empty () && k > 1.0)
|
|
continue;
|
|
|
|
for (const double &distance : distances)
|
|
d_cur_penalty += std::min (distance, threshold_);
|
|
|
|
// Better match ?
|
|
if (d_cur_penalty < d_best_penalty)
|
|
{
|
|
d_best_penalty = d_cur_penalty;
|
|
|
|
// Save the current model/coefficients selection as being the best so far
|
|
model_ = selection;
|
|
model_coefficients_ = model_coefficients;
|
|
|
|
n_inliers_count = 0;
|
|
// Need to compute the number of inliers for this model to adapt k
|
|
for (const double &distance : distances)
|
|
if (distance <= threshold_)
|
|
n_inliers_count++;
|
|
|
|
// Compute the k parameter (k=std::log(z)/std::log(1-w^n))
|
|
double w = static_cast<double> (n_inliers_count) / static_cast<double>(sac_model_->getIndices ()->size ());
|
|
double p_no_outliers = 1 - std::pow (w, static_cast<double> (selection.size ()));
|
|
p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf
|
|
p_no_outliers = (std::min) (1 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0.
|
|
k = std::log (1 - probability_) / std::log (p_no_outliers);
|
|
}
|
|
|
|
++iterations_;
|
|
if (debug_verbosity_level > 1)
|
|
PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
|
|
if (iterations_ > max_iterations_)
|
|
{
|
|
if (debug_verbosity_level > 0)
|
|
PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (model_.empty ())
|
|
{
|
|
if (debug_verbosity_level > 0)
|
|
PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
|
|
return (false);
|
|
}
|
|
|
|
// Iterate through the 3d points and calculate the distances from them to the model again
|
|
sac_model_->getDistancesToModel (model_coefficients_, distances);
|
|
Indices &indices = *sac_model_->getIndices ();
|
|
if (distances.size () != indices.size ())
|
|
{
|
|
PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
|
|
return (false);
|
|
}
|
|
|
|
inliers_.resize (distances.size ());
|
|
// Get the inliers for the best model found
|
|
n_inliers_count = 0;
|
|
for (std::size_t i = 0; i < distances.size (); ++i)
|
|
if (distances[i] <= threshold_)
|
|
inliers_[n_inliers_count++] = indices[i];
|
|
|
|
// Resize the inliers vector
|
|
inliers_.resize (n_inliers_count);
|
|
|
|
if (debug_verbosity_level > 0)
|
|
PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
|
|
|
|
return (true);
|
|
}
|
|
|
|
#define PCL_INSTANTIATE_RandomizedMEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedMEstimatorSampleConsensus<T>;
|
|
|
|
#endif // PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
|
|
|