Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2010-2011, Willow Garage, Inc. 00006 * Copyright (c) 2012-, Open Perception, Inc. 00007 * 00008 * All rights reserved. 00009 * 00010 * Redistribution and use in source and binary forms, with or without 00011 * modification, are permitted provided that the following conditions 00012 * are met: 00013 * 00014 * * Redistributions of source code must retain the above copyright 00015 * notice, this list of conditions and the following disclaimer. 00016 * * Redistributions in binary form must reproduce the above 00017 * copyright notice, this list of conditions and the following 00018 * disclaimer in the documentation and/or other materials provided 00019 * with the distribution. 00020 * * Neither the name of the copyright holder(s) nor the names of its 00021 * contributors may be used to endorse or promote products derived 00022 * from this software without specific prior written permission. 00023 * 00024 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00025 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00026 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00027 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00028 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00029 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00030 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00031 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00032 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00033 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00034 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00035 * POSSIBILITY OF SUCH DAMAGE. 00036 * 00037 * $Id$ 00038 * 00039 */ 00040 00041 #ifndef PCL_ICP_NL_H_ 00042 #define PCL_ICP_NL_H_ 00043 00044 // PCL includes 00045 #include <pcl/registration/icp.h> 00046 #include <pcl/registration/transformation_estimation_lm.h> 00047 00048 namespace pcl 00049 { 00050 /** \brief @b IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization 00051 * backend. The resultant transformation is optimized as a quaternion. 00052 * 00053 * The algorithm has several termination criteria: 00054 * 00055 * <ol> 00056 * <li>Number of iterations has reached the maximum user imposed number of iterations 00057 * (via \ref setMaximumIterations)</li> 00058 * <li>The epsilon (difference) between the previous transformation and the current estimated transformation is 00059 * smaller than an user imposed value (via \ref setTransformationEpsilon)</li> 00060 * <li>The sum of Euclidean squared errors is smaller than a user defined threshold 00061 * (via \ref setEuclideanFitnessEpsilon)</li> 00062 * </ol> 00063 * 00064 * \author Radu B. Rusu, Michael Dixon 00065 * \ingroup registration 00066 */ 00067 template <typename PointSource, typename PointTarget, typename Scalar = float> 00068 class IterativeClosestPointNonLinear : public IterativeClosestPoint<PointSource, PointTarget, Scalar> 00069 { 00070 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::min_number_correspondences_; 00071 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::reg_name_; 00072 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformation_estimation_; 00073 using IterativeClosestPoint<PointSource, PointTarget, Scalar>::computeTransformation; 00074 00075 public: 00076 00077 typedef boost::shared_ptr< IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar> > Ptr; 00078 typedef boost::shared_ptr< const IterativeClosestPointNonLinear<PointSource, PointTarget, Scalar> > ConstPtr; 00079 00080 typedef typename Registration<PointSource, PointTarget, Scalar>::Matrix4 Matrix4; 00081 00082 /** \brief Empty constructor. */ 00083 IterativeClosestPointNonLinear () 00084 { 00085 min_number_correspondences_ = 4; 00086 reg_name_ = "IterativeClosestPointNonLinear"; 00087 00088 transformation_estimation_.reset (new pcl::registration::TransformationEstimationLM<PointSource, PointTarget, Scalar>); 00089 } 00090 }; 00091 } 00092 00093 #endif //#ifndef PCL_ICP_NL_H_