Loading...
Searching...
No Matches
sac_model_stick.hpp
/*
* 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: sac_model_line.hpp 2328 2011-08-31 08:11:00Z rusu $
*
*/
#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
#include <pcl/sample_consensus/sac_model_stick.h>
#include <pcl/common/centroid.h>
#include <pcl/common/concatenate.h>
#include <pcl/common/eigen.h> // for eigen33
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::isSampleGood (const Indices &samples) const
{
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
return (false);
}
if (
((*input_)[samples[0]].x != (*input_)[samples[1]].x)
&&
((*input_)[samples[0]].y != (*input_)[samples[1]].y)
&&
((*input_)[samples[0]].z != (*input_)[samples[1]].z))
{
return (true);
}
return (false);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::computeModelCoefficients (
const Indices &samples, Eigen::VectorXf &model_coefficients) const
{
// Need 2 samples
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
model_coefficients.resize (model_size_);
model_coefficients[0] = (*input_)[samples[0]].x;
model_coefficients[1] = (*input_)[samples[0]].y;
model_coefficients[2] = (*input_)[samples[0]].z;
model_coefficients[3] = (*input_)[samples[1]].x;
model_coefficients[4] = (*input_)[samples[1]].y;
model_coefficients[5] = (*input_)[samples[1]].z;
// model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
// model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
// model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
// model_coefficients.template segment<3> (3).normalize ();
// We don't care about model_coefficients[6] which is the width (radius) of the stick
PCL_DEBUG ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Model is (%g,%g,%g,%g,%g,%g).\n",
model_coefficients[0], model_coefficients[1], model_coefficients[2],
model_coefficients[3], model_coefficients[4], model_coefficients[5]);
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::getDistancesToModel] Given model is invalid!\n");
return;
}
float sqr_threshold = static_cast<float> (radius_max_ * radius_max_);
distances.resize (indices_->size ());
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
line_dir.normalize ();
// Iterate through the 3d points and calculate the distances from them to the line
for (std::size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
float sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
{
// Need to estimate sqrt here to keep MSAC and friends general
distances[i] = std::sqrt (sqr_distance);
}
else
{
// Penalize outliers by doubling the distance
distances[i] = 2 * std::sqrt (sqr_distance);
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::selectWithinDistance] Given model is invalid!\n");
return;
}
float sqr_threshold = static_cast<float> (threshold * threshold);
inliers.clear ();
error_sqr_dists_.clear ();
inliers.reserve (indices_->size ());
error_sqr_dists_.reserve (indices_->size ());
// Obtain the line point and direction
Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
Eigen::Vector4f line_dir = line_pt2 - line_pt1;
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
//Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
line_dir.normalize ();
//float norm = line_dir.squaredNorm ();
// Iterate through the 3d points and calculate the distances from them to the line
for (std::size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
//float u = dir.dot (line_dir);
// If the point falls outside of the segment, ignore it
//if (u < 0.0f || u > 1.0f)
// continue;
float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
{
// Returns the indices of the points whose squared distances are smaller than the threshold
inliers.push_back ((*indices_)[i]);
error_sqr_dists_.push_back (static_cast<double> (sqr_distance));
}
}
}
///////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelStick<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::countWithinDistance] Given model is invalid!\n");
return (0);
}
float sqr_threshold = static_cast<float> (threshold * threshold);
std::size_t nr_i = 0, nr_o = 0;
// Obtain the line point and direction
Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
Eigen::Vector4f line_dir = line_pt2 - line_pt1;
line_dir.normalize ();
//Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
// Iterate through the 3d points and calculate the distances from them to the line
for (std::size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
Eigen::Vector4f dir = (*input_)[(*indices_)[i]].getVector4fMap () - line_pt1;
//float u = dir.dot (line_dir);
// If the point falls outside of the segment, ignore it
//if (u < 0.0f || u > 1.0f)
// continue;
float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
// Use a larger threshold (4 times the radius) to get more points in
if (sqr_distance < sqr_threshold)
{
nr_i++;
}
else if (sqr_distance < 4.0f * sqr_threshold)
{
nr_o++;
}
}
return (nr_i <= nr_o ? 0 : nr_i - nr_o);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::optimizeModelCoefficients (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
optimized_coefficients = model_coefficients;
return;
}
// Need more than the minimum sample size to make a difference
if (inliers.size () <= sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
optimized_coefficients = model_coefficients;
return;
}
optimized_coefficients.resize (model_size_);
// Compute the 3x3 covariance matrix
Eigen::Vector4f centroid;
Eigen::Matrix3f covariance_matrix;
if (0 == computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, centroid))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] computeMeanAndCovarianceMatrix failed (returned 0) because there are no valid inliers.\n");
optimized_coefficients = model_coefficients;
return;
}
optimized_coefficients[0] = centroid[0];
optimized_coefficients[1] = centroid[1];
optimized_coefficients[2] = centroid[2];
// Extract the eigenvalues and eigenvectors
Eigen::Vector3f eigen_values;
Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_values);
pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
optimized_coefficients.template segment<3> (3).matrix () = eigen_vector;
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::projectPoints (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
{
// Needs a valid model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::projectPoints] Given model is invalid!\n");
return;
}
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
projected_points.header = input_->header;
projected_points.is_dense = input_->is_dense;
// Copy all the data fields from the input cloud to the projected one?
if (copy_data_fields)
{
// Allocate enough space and copy the basics
projected_points.resize (input_->size ());
projected_points.width = input_->width;
projected_points.height = input_->height;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < projected_points.size (); ++i)
{
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
}
// Iterate through the 3d points and calculate the distances from them to the line
for (const auto &inlier : inliers)
{
Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
Eigen::Vector4f pp = line_pt + k * line_dir;
// Calculate the projection of the point on the line (pointProj = A + k * B)
projected_points[inlier].x = pp[0];
projected_points[inlier].y = pp[1];
projected_points[inlier].z = pp[2];
}
}
else
{
// Allocate enough space and copy the basics
projected_points.resize (inliers.size ());
projected_points.width = inliers.size ();
projected_points.height = 1;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < inliers.size (); ++i)
{
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
}
// Iterate through the 3d points and calculate the distances from them to the line
for (std::size_t i = 0; i < inliers.size (); ++i)
{
Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
Eigen::Vector4f pp = line_pt + k * line_dir;
// Calculate the projection of the point on the line (pointProj = A + k * B)
projected_points[i].x = pp[0];
projected_points[i].y = pp[1];
projected_points[i].z = pp[2];
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::doSamplesVerifyModel (
const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::doSamplesVerifyModel] Given model is invalid!\n");
return (false);
}
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0.0f);
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
line_dir.normalize ();
float sqr_threshold = static_cast<float> (threshold * threshold);
// Iterate through the 3d points and calculate the distances from them to the line
for (const auto &index : indices)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
{
return (false);
}
}
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelStick(T) template class PCL_EXPORTS pcl::SampleConsensusModelStick<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
centroid.h
Define methods for centroid estimation and covariance matrix calculus.
pcl::SampleConsensusModelStick::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
Definition sac_model_stick.hpp:147
pcl::SampleConsensusModelStick::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given stick model coefficients.
Definition sac_model_stick.hpp:375
pcl::SampleConsensusModelStick::computeModelCoefficients
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid stick model, compute the model coefficients fr...
Definition sac_model_stick.hpp:73
pcl::SampleConsensusModelStick::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all squared distances from the cloud data to a given stick model.
Definition sac_model_stick.hpp:107
pcl::SampleConsensusModelStick::PointCloud
typename SampleConsensusModel< PointT >::PointCloud PointCloud
Definition sac_model_stick.h:79
pcl::SampleConsensusModelStick::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition sac_model_stick.hpp:51
pcl::SampleConsensusModelStick::projectPoints
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the stick model.
Definition sac_model_stick.hpp:295
pcl::SampleConsensusModelStick::countWithinDistance
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
Definition sac_model_stick.hpp:197
pcl::SampleConsensusModelStick::optimizeModelCoefficients
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the stick coefficients using the given inlier set and return them to the user.
Definition sac_model_stick.hpp:249
pcl::computeCorrespondingEigenVector
void computeCorrespondingEigenVector(const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi defin...
Definition eigen.hpp:226
pcl::computeMeanAndCovarianceMatrix
unsigned int computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition centroid.hpp:509
pcl::eigen33
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition eigen.hpp:295
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133