MatchersImpl.h
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00001 // kate: replace-tabs off; indent-width 4; indent-mode normal
00002 // vim: ts=4:sw=4:noexpandtab
00003 /*
00004 
00005 Copyright (c) 2010--2012,
00006 François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
00007 You can contact the authors at <f dot pomerleau at gmail dot com> and
00008 <stephane at magnenat dot net>
00009 
00010 All rights reserved.
00011 
00012 Redistribution and use in source and binary forms, with or without
00013 modification, are permitted provided that the following conditions are met:
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 copyright
00017       notice, this list of conditions and the following disclaimer in the
00018       documentation and/or other materials provided with the distribution.
00019     * Neither the name of the <organization> nor the
00020       names of its contributors may be used to endorse or promote products
00021       derived from this software without specific prior written permission.
00022 
00023 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
00024 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
00025 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
00026 DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY
00027 DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
00028 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
00029 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
00030 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
00031 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00032 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00033 
00034 */
00035 
00036 #ifndef __POINTMATCHER_MATCHERS_H
00037 #define __POINTMATCHER_MATCHERS_H
00038 
00039 #include "PointMatcher.h"
00040 
00041 template<typename T>
00042 struct MatchersImpl
00043 {
00044         typedef PointMatcherSupport::Parametrizable Parametrizable;
00045         typedef PointMatcherSupport::Parametrizable P;
00046         typedef Parametrizable::Parameters Parameters;
00047         typedef Parametrizable::ParameterDoc ParameterDoc;
00048         typedef Parametrizable::ParametersDoc ParametersDoc;
00049         
00050         typedef typename Nabo::NearestNeighbourSearch<T> NNS;
00051         typedef typename NNS::SearchType NNSearchType;
00052         
00053         typedef typename PointMatcher<T>::DataPoints DataPoints;
00054         typedef typename PointMatcher<T>::Matcher Matcher;
00055         typedef typename PointMatcher<T>::Matches Matches;
00056         
00057         struct NullMatcher: public Matcher
00058         {
00059                 inline static const std::string description()
00060                 {
00061                         return "Does nothing, returns no match.";
00062                 }
00063                 
00064                 virtual void init(const DataPoints& filteredReference);
00065                 virtual Matches findClosests(const DataPoints& filteredReading);
00066         };
00067 
00068         struct KDTreeMatcher: public Matcher
00069         {
00070                 inline static const std::string description()
00071                 {
00072                         return "This matcher matches a point from the reading to its closest neighbors in the reference.";
00073                 }
00074                 inline static const ParametersDoc availableParameters()
00075                 {
00076                         return boost::assign::list_of<ParameterDoc>
00077                                 ( "knn", "number of nearest neighbors to consider it the reference", "1", "1", "2147483647", &P::Comp<unsigned> )
00078                                 ( "epsilon", "approximation to use for the nearest-neighbor search", "0", "0", "inf", &P::Comp<T> )
00079                                 ( "searchType", "Nabo search type. 0: brute force, check distance to every point in the data (very slow), 1: kd-tree with linear heap, good for small knn (~up to 30) and 2: kd-tree with tree heap, good for large knn (~from 30)", "1", "0", "2", &P::Comp<unsigned> )
00080                                 ( "maxDist", "maximum distance to consider for neighbors", "inf", "0", "inf", &P::Comp<T> )
00081                         ;
00082                 }
00083                 
00084                 const int knn;
00085                 const T epsilon;
00086                 const NNSearchType searchType;
00087                 const T maxDist;
00088 
00089         protected:
00090                 boost::shared_ptr<NNS> featureNNS;
00091 
00092         public:
00093                 KDTreeMatcher(const Parameters& params = Parameters());
00094                 virtual ~KDTreeMatcher();
00095                 virtual void init(const DataPoints& filteredReference);
00096                 virtual Matches findClosests(const DataPoints& filteredReading);
00097         };
00098 
00099         struct KDTreeVarDistMatcher: public Matcher
00100         {
00101                 inline static const std::string description()
00102                 {
00103                         return "This matcher matches a point from the reading to its closest neighbors in the reference. A maximum search radius per point can be defined.";
00104                 }
00105                 inline static const ParametersDoc availableParameters()
00106                 {
00107                         return boost::assign::list_of<ParameterDoc>
00108                                 ( "knn", "number of nearest neighbors to consider it the reference", "1", "1", "2147483647", &P::Comp<unsigned> )
00109                                 ( "epsilon", "approximation to use for the nearest-neighbor search", "0", "0", "inf", &P::Comp<T> )
00110                                 ( "searchType", "Nabo search type. 0: brute force, check distance to every point in the data (very slow), 1: kd-tree with linear heap, good for small knn (~up to 30) and 2: kd-tree with tree heap, good for large knn (~from 30)", "1", "0", "2", &P::Comp<unsigned> )
00111                                 ( "maxDistField", "descriptor field name used to set a maximum distance to consider for neighbors per point", "maxSearchDist" )
00112                         ;
00113                 }
00114                 
00115                 const int knn;
00116                 const T epsilon;
00117                 const NNSearchType searchType;
00118                 const std::string maxDistField;
00119 
00120         protected:
00121                 boost::shared_ptr<NNS> featureNNS;
00122 
00123         public:
00124                 KDTreeVarDistMatcher(const Parameters& params = Parameters());
00125                 virtual ~KDTreeVarDistMatcher();
00126                 virtual void init(const DataPoints& filteredReference);
00127                 virtual Matches findClosests(const DataPoints& filteredReading);
00128         };
00129 
00130 }; // MatchersImpl
00131 
00132 #endif // __POINTMATCHER_MATCHERS_H


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autogenerated on Mon Sep 14 2015 02:59:06