template <typename PointT> bool
pcl::MaximumLikelihoodSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
{
// Warn and exit if no threshold was set
if (threshold_ == std::numeric_limits<double>::max())
{
PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] No threshold set!\n");
return (false);
}
iterations_ = 0;
double d_best_penalty = std::numeric_limits<double>::max();
double k = 1.0;
std::vector<int> best_model;
std::vector<int> selection;
Eigen::VectorXf model_coefficients;
std::vector<double> distances;
// Compute sigma - remember to set threshold_ correctly !
sigma_ = computeMedianAbsoluteDeviation (sac_model_->getInputCloud (), sac_model_->getIndices (), threshold_);
if (debug_verbosity_level > 1)
PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated sigma value: %f.\n", sigma_);
// Compute the bounding box diagonal: V = sqrt (sum (max(pointCloud) - min(pointCloud)^2))
Eigen::Vector4f min_pt, max_pt;
getMinMax (sac_model_->getInputCloud (), sac_model_->getIndices (), min_pt, max_pt);
max_pt -= min_pt;
double v = sqrt (max_pt.dot (max_pt));
int n_inliers_count = 0;
size_t indices_size;
unsigned skipped_count = 0;
// supress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
const unsigned max_skip = max_iterations_ * 10;
// 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;
}
// Iterate through the 3d points and calculate the distances from them to the model
sac_model_->getDistancesToModel (model_coefficients, distances);
// Use Expectiation-Maximization to find out the right value for d_cur_penalty
// ---[ Initial estimate for the gamma mixing parameter = 1/2
double gamma = 0.5;
double p_outlier_prob = 0;
indices_size = sac_model_->getIndices ()->size ();
std::vector<double> p_inlier_prob (indices_size);
for (int j = 0; j < iterations_EM_; ++j)
{
// Likelihood of a datum given that it is an inlier
for (size_t i = 0; i < indices_size; ++i)
p_inlier_prob[i] = gamma * exp (- (distances[i] * distances[i] ) / 2 * (sigma_ * sigma_) ) /
(sqrt (2 * M_PI) * sigma_);
// Likelihood of a datum given that it is an outlier
p_outlier_prob = (1 - gamma) / v;
gamma = 0;
for (size_t i = 0; i < indices_size; ++i)
gamma += p_inlier_prob [i] / (p_inlier_prob[i] + p_outlier_prob);
gamma /= static_cast<double>(sac_model_->getIndices ()->size ());
}
// Find the log likelihood of the model -L = -sum [log (pInlierProb + pOutlierProb)]
double d_cur_penalty = 0;
for (size_t i = 0; i < indices_size; ++i)
d_cur_penalty += log (p_inlier_prob[i] + p_outlier_prob);
d_cur_penalty = - d_cur_penalty;
// 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 (size_t i = 0; i < distances.size (); ++i)
if (distances[i] <= 2 * sigma_)
n_inliers_count++;
// Compute the k parameter (k=log(z)/log(1-w^n))
double w = static_cast<double> (n_inliers_count) / static_cast<double> (sac_model_->getIndices ()->size ());
double p_no_outliers = 1 - 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 = log (1 - probability_) / log (p_no_outliers);
}
++iterations_;
if (debug_verbosity_level > 1)
PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (ceil (k)), d_best_penalty);
if (iterations_ > max_iterations_)
{
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] MLESAC reached the maximum number of trials.\n");
break;
}
}
if (model_.empty ())
{
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::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);
std::vector<int> &indices = *sac_model_->getIndices ();
if (distances.size () != indices.size ())
{
PCL_ERROR ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Estimated distances (%zu) differs than the normal of indices (%zu).\n", distances.size (), indices.size ());
return (false);
}
inliers_.resize (distances.size ());
// Get the inliers for the best model found
n_inliers_count = 0;
for (size_t i = 0; i < distances.size (); ++i)
if (distances[i] <= 2 * sigma_)
inliers_[n_inliers_count++] = indices[i];
// Resize the inliers vector
inliers_.resize (n_inliers_count);
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::MaximumLikelihoodSampleConsensus::computeModel] Model: %zu size, %d inliers.\n", model_.size (), n_inliers_count);
return (true);
}
/** \brief Compute the actual model and find the inliers
* \param debug enable/disable on-screen debug information
*/
bool
MLESAC::computeModel (int debug)
{
iterations_ = 0;
double d_best_penalty = DBL_MAX;
double k = 1.0;
std::vector<int> best_model;
std::vector<int> best_inliers, inliers;
std::vector<int> selection;
// Compute sigma - remember to set threshold_ correctly !
sigma_ = cloud_geometry::computeMedianAbsoluteDeviation (*sac_model_->getCloud (), *sac_model_->getIndices (), threshold_);
if (debug)
std::cerr << "[MLESAC::computeModel] Estimated sigma value : " << sigma_ << "." << std::endl;
// Compute the bounding box diagonal: V = sqrt (sum (max(pointCloud) - min(pointCloud)^2))
std_msgs::Point32 min_pt, max_pt;
cloud_geometry::getMinMax (*sac_model_->getCloud (), *sac_model_->getIndices (), min_pt, max_pt);
double v = sqrt ( (max_pt.x - min_pt.x) * (max_pt.x - min_pt.x) +
(max_pt.y - min_pt.y) * (max_pt.y - min_pt.y) +
(max_pt.z - min_pt.z) * (max_pt.z - min_pt.z)
);
// Iterate
while (iterations_ < k)
{
// Get X samples which satisfy the model criteria
selection = sac_model_->getSamples (iterations_);
if (selection.size () == 0) break;
// Search for inliers in the point cloud for the current plane model M
sac_model_->computeModelCoefficients (selection);
// Iterate through the 3d points and calculate the distances from them to the model
std::vector<double> distances = sac_model_->getDistancesToModel (sac_model_->getModelCoefficients ());
// Use Expectiation-Maximization to find out the right value for d_cur_penalty
// ---[ Initial estimate for the gamma mixing parameter = 1/2
double gamma = 0.5;
double p_outlier_prob = 0;
std::vector<double> p_inlier_prob (sac_model_->getIndices ()->size ());
for (int j = 0; j < iterations_EM_; j++)
{
// Likelihood of a datum given that it is an inlier
for (unsigned int i = 0; i < sac_model_->getIndices ()->size (); i++)
p_inlier_prob[i] = gamma * exp (- (distances.at (i) * distances.at (i) ) / 2 * (sigma_ * sigma_) ) /
(sqrt (2 * M_PI) * sigma_);
// Likelihood of a datum given that it is an outlier
p_outlier_prob = (1 - gamma) / v;
gamma = 0;
for (unsigned int i = 0; i < sac_model_->getIndices ()->size (); i++)
gamma += p_inlier_prob [i] / (p_inlier_prob[i] + p_outlier_prob);
gamma /= sac_model_->getIndices ()->size ();
}
// Find the log likelihood of the model -L = -sum [log (pInlierProb + pOutlierProb)]
double d_cur_penalty = 0;
for (unsigned int i = 0; i < sac_model_->getIndices ()->size (); i++)
d_cur_penalty += log (p_inlier_prob[i] + p_outlier_prob);
d_cur_penalty = - d_cur_penalty;
// Better match ?
if (d_cur_penalty < d_best_penalty)
{
d_best_penalty = d_cur_penalty;
best_model = selection;
// Save the inliers
best_inliers.resize (sac_model_->getIndices ()->size ());
int n_inliers_count = 0;
for (unsigned int i = 0; i < sac_model_->getIndices ()->size (); i++)
{
if (distances[i] <= 2 * sigma_)
{
best_inliers[n_inliers_count] = sac_model_->getIndices ()->at (i);
n_inliers_count++;
}
}
best_inliers.resize (n_inliers_count);
// Compute the k parameter (k=log(z)/log(1-w^n))
double w = (double)((double)n_inliers_count / (double)sac_model_->getIndices ()->size ());
double p_no_outliers = 1 - pow (w, (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 = log (1 - probability_) / log (p_no_outliers);
}
iterations_ += 1;
if (debug > 1)
std::cerr << "[MLESAC::computeModel] Trial " << iterations_ << " out of " << ceil (k) << ": best number of inliers so far is " << best_inliers.size () << "." << std::endl;
if (iterations_ > max_iterations_)
{
if (debug > 0)
std::cerr << "[MLESAC::computeModel] MSAC reached the maximum number of trials." << std::endl;
break;
}
}
if (best_model.size () != 0)
{
if (debug > 0)
std::cerr << "[MLESAC::computeModel] Model found: " << best_inliers.size () << " inliers." << std::endl;
sac_model_->setBestModel (best_model);
sac_model_->setBestInliers (best_inliers);
return (true);
}
else
if (debug > 0)
std::cerr << "[MLESAC::computeModel] Unable to find a solution!" << std::endl;
return (false);
}
