Point Cloud Library (PCL)
1.15.1
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pcl
registration
icp_nl.h
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/*
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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) 2010-2011, 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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#pragma once
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// PCL includes
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#include <pcl/registration/icp.h>
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#include <pcl/registration/transformation_estimation_lm.h>
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namespace
pcl
{
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/** \brief @b IterativeClosestPointNonLinear is an ICP variant that uses
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* Levenberg-Marquardt optimization backend. The resultant transformation is optimized
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* as a quaternion.
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*
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* The algorithm has several termination criteria:
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*
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* <ol>
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* <li>Number of iterations has reached the maximum user imposed number of iterations
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* (via \ref setMaximumIterations)</li>
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* <li>The epsilon (difference) between the previous transformation and the current
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* estimated transformation is smaller than an user imposed value (via \ref
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* setTransformationEpsilon)</li> <li>The sum of Euclidean squared errors is smaller
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* than a user defined threshold (via \ref setEuclideanFitnessEpsilon)</li>
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* </ol>
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*
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* \author Radu B. Rusu, Michael Dixon
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* \ingroup registration
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*/
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template
<
typename
Po
int
Source,
typename
Po
int
Target,
typename
Scalar =
float
>
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class
IterativeClosestPointNonLinear
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:
public
IterativeClosestPoint
<PointSource, PointTarget, Scalar> {
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using
IterativeClosestPoint
<PointSource, PointTarget, Scalar>::
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min_number_correspondences_;
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using
IterativeClosestPoint
<PointSource, PointTarget, Scalar>
::reg_name_
;
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using
IterativeClosestPoint
<PointSource, PointTarget, Scalar>::
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transformation_estimation_;
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using
IterativeClosestPoint
<PointSource, PointTarget, Scalar>
::computeTransformation
;
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public
:
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using
Ptr
=
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shared_ptr<IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar>>;
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using
ConstPtr
= shared_ptr<
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const
IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar>
>;
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using
Matrix4
=
typename
Registration<PointSource, PointTarget, Scalar>::Matrix4
;
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/** \brief Empty constructor. */
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IterativeClosestPointNonLinear
()
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{
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min_number_correspondences_
= 4;
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reg_name_
=
"IterativeClosestPointNonLinear"
;
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transformation_estimation_
.reset(
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new
pcl::registration
::
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TransformationEstimationLM<PointSource, PointTarget, Scalar>);
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}
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};
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}
// namespace pcl
pcl::IterativeClosestPoint< PointSource, PointTarget, float >::IterativeClosestPoint
IterativeClosestPoint()
Definition
icp.h:146
pcl::IterativeClosestPoint< PointSource, PointTarget, float >::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Definition
icp.hpp:115
pcl::IterativeClosestPointNonLinear::Matrix4
typename Registration< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition
icp_nl.h:82
pcl::IterativeClosestPointNonLinear::Ptr
shared_ptr< IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar > > Ptr
Definition
icp_nl.h:77
pcl::IterativeClosestPointNonLinear::ConstPtr
shared_ptr< const IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar > > ConstPtr
Definition
icp_nl.h:79
pcl::IterativeClosestPointNonLinear::IterativeClosestPointNonLinear
IterativeClosestPointNonLinear()
Empty constructor.
Definition
icp_nl.h:85
pcl::Registration< PointSource, PointTarget, float >::reg_name_
std::string reg_name_
Definition
registration.h:548
pcl::Registration::Matrix4
Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition
registration.h:59
pcl::Registration< PointSource, PointTarget, float >::transformation_estimation_
TransformationEstimationPtr transformation_estimation_
Definition
registration.h:625
pcl::Registration< PointSource, PointTarget, float >::min_number_correspondences_
unsigned int min_number_correspondences_
Definition
registration.h:618
pcl::registration
Definition
convergence_criteria.h:46
pcl
Definition
convolution.h:46