prosac.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
#if defined __GNUC__
# pragma GCC system_header
#endif
#include <boost/math/distributions/binomial.hpp>
#include <pcl/sample_consensus/prosac.h>
//////////////////////////////////////////////////////////////////////////
// Variable naming uses capital letters to make the comparison with the original paper easier
template<typename PointT> bool
pcl::ProgressiveSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
{
// Warn and exit if no threshold was set
if (threshold_ == DBL_MAX)
{
PCL_ERROR ("[pcl::ProgressiveSampleConsensus::computeModel] No threshold set!\n");
return (false);
}
// Initialize some PROSAC constants
const int T_N = 200000;
const std::size_t N = sac_model_->indices_->size ();
const std::size_t m = sac_model_->getSampleSize ();
float T_n = static_cast<float> (T_N);
for (unsigned int i = 0; i < m; ++i)
T_n *= static_cast<float> (m - i) / static_cast<float> (N - i);
float T_prime_n = 1.0f;
std::size_t I_N_best = 0;
float n = static_cast<float> (m);
// Define the n_Start coefficients from Section 2.2
float n_star = static_cast<float> (N);
float epsilon_n_star = 0.0;
std::size_t k_n_star = T_N;
// Compute the I_n_star_min of Equation 8
std::vector<unsigned int> I_n_star_min (N);
// Initialize the usual RANSAC parameters
iterations_ = 0;
Indices inliers;
Indices selection;
Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
// We will increase the pool so the indices_ vector can only contain m elements at first
Indices index_pool;
index_pool.reserve (N);
for (unsigned int i = 0; i < n; ++i)
index_pool.push_back (sac_model_->indices_->operator[](i));
// Iterate
while (static_cast<unsigned int> (iterations_) < k_n_star)
{
// Choose the samples
// Step 1
// According to Equation 5 in the text text, not the algorithm
if ((iterations_ == T_prime_n) && (n < n_star))
{
// Increase the pool
++n;
if (n >= N)
break;
index_pool.push_back (sac_model_->indices_->at(static_cast<unsigned int> (n - 1)));
// Update other variables
float T_n_minus_1 = T_n;
T_n *= (static_cast<float>(n) + 1.0f) / (static_cast<float>(n) + 1.0f - static_cast<float>(m));
T_prime_n += std::ceil (T_n - T_n_minus_1);
}
// Step 2
sac_model_->indices_->swap (index_pool);
selection.clear ();
sac_model_->getSamples (iterations_, selection);
if (T_prime_n < iterations_)
{
selection.pop_back ();
selection.push_back (sac_model_->indices_->at(static_cast<unsigned int> (n - 1)));
}
// Make sure we use the right indices for testing
sac_model_->indices_->swap (index_pool);
if (selection.empty ())
{
PCL_ERROR ("[pcl::ProgressiveSampleConsensus::computeModel] No samples could be selected!\n");
break;
}
// Search for inliers in the point cloud for the current model
if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
{
++iterations_;
continue;
}
// Select the inliers that are within threshold_ from the model
inliers.clear ();
sac_model_->selectWithinDistance (model_coefficients, threshold_, inliers);
std::size_t I_N = inliers.size ();
// If we find more inliers than before
if (I_N > I_N_best)
{
I_N_best = I_N;
// Save the current model/inlier/coefficients selection as being the best so far
inliers_ = inliers;
model_ = selection;
model_coefficients_ = model_coefficients;
// We estimate I_n_star for different possible values of n_star by using the inliers
std::sort (inliers.begin (), inliers.end ());
// Try to find a better n_star
// We minimize k_n_star and therefore maximize epsilon_n_star = I_n_star / n_star
std::size_t possible_n_star_best = N, I_possible_n_star_best = I_N;
float epsilon_possible_n_star_best = static_cast<float>(I_possible_n_star_best) / static_cast<float>(possible_n_star_best);
// We only need to compute possible better epsilon_n_star for when _n is just about to be removed an inlier
std::size_t I_possible_n_star = I_N;
for (auto last_inlier = inliers.crbegin (), inliers_end = inliers.crend ();
last_inlier != inliers_end;
++last_inlier, --I_possible_n_star)
{
// The best possible_n_star for a given I_possible_n_star is the index of the last inlier
unsigned int possible_n_star = (*last_inlier) + 1;
if (possible_n_star <= m)
break;
// If we find a better epsilon_n_star
float epsilon_possible_n_star = static_cast<float>(I_possible_n_star) / static_cast<float>(possible_n_star);
// Make sure we have a better epsilon_possible_n_star
if ((epsilon_possible_n_star > epsilon_n_star) && (epsilon_possible_n_star > epsilon_possible_n_star_best))
{
// Typo in Equation 7, not (n-m choose i-m) but (n choose i-m)
std::size_t I_possible_n_star_min = m
+ static_cast<std::size_t> (std::ceil (boost::math::quantile (boost::math::complement (boost::math::binomial_distribution<float>(static_cast<float> (possible_n_star), 0.1f), 0.05))));
// If Equation 9 is not verified, exit
if (I_possible_n_star < I_possible_n_star_min)
break;
possible_n_star_best = possible_n_star;
I_possible_n_star_best = I_possible_n_star;
epsilon_possible_n_star_best = epsilon_possible_n_star;
}
}
// Check if we get a better epsilon
if (epsilon_possible_n_star_best > epsilon_n_star)
{
// update the best value
epsilon_n_star = epsilon_possible_n_star_best;
// Compute the new k_n_star
float bottom_log = 1 - std::pow (epsilon_n_star, static_cast<float>(m));
if (bottom_log == 0)
k_n_star = 1;
else if (bottom_log == 1)
k_n_star = T_N;
else
k_n_star = static_cast<int> (std::ceil (std::log (0.05) / std::log (bottom_log)));
// It seems weird to have very few iterations, so do have a few (totally empirical)
k_n_star = (std::max)(k_n_star, 2 * m);
}
}
++iterations_;
if (debug_verbosity_level > 1)
PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] Trial %d out of %d: %d inliers (best is: %d so far).\n", iterations_, k_n_star, I_N, I_N_best);
if (iterations_ > max_iterations_)
{
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
break;
}
}
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), I_N_best);
if (model_.empty ())
{
inliers_.clear ();
return (false);
}
return (true);
}
#define PCL_INSTANTIATE_ProgressiveSampleConsensus(T) template class PCL_EXPORTS pcl::ProgressiveSampleConsensus<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
pcl::ProgressiveSampleConsensus::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: prosac.hpp:54
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133