96 lines
3.8 KiB
C
96 lines
3.8 KiB
C
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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 PointSource, typename PointTarget, 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
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