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matlabbe
a067f1369e
Updated parameter LccReextract/MaxWords=600 git-svn-id: http://rtabmap.googlecode.com/svn/trunk/rtabmap@2072 f169173b-cf89-36c8-b27e-44dbe73f0c83
405 lines
30 KiB
C++
405 lines
30 KiB
C++
/*
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Copyright (c) 2010-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
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All rights reserved.
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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 are met:
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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 copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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* Neither the name of the Universite de Sherbrooke nor the
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names of its contributors may be used to endorse or promote products
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derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
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DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef PARAMETERS_H_
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#define PARAMETERS_H_
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// default parameters
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#include "rtabmap/core/RtabmapExp.h" // DLL export/import defines
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#include "rtabmap/core/Version.h" // DLL export/import defines
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#include <string>
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#include <map>
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namespace rtabmap
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{
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typedef std::map<std::string, std::string> ParametersMap; // Key, value
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typedef std::pair<std::string, std::string> ParametersPair;
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/**
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* Macro used to create parameter's key and default value.
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* This macro must be used only in the Parameters class definition (in this file).
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* They are automatically added to the default parameters map of the class Parameters.
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* Example:
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* @code
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* //for PARAM(Video, ImageWidth, int, 640), the output will be :
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* public:
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* static std::string kVideoImageWidth() {return std::string("Video/ImageWidth");}
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* static int defaultVideoImageWidth() {return 640;}
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* private:
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* class DummyVideoImageWidth {
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* public:
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* DummyVideoImageWidth() {parameters_.insert(ParametersPair("Video/ImageWidth", "640"));}
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* };
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* DummyVideoImageWidth dummyVideoImageWidth;
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* @endcode
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*/
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#define RTABMAP_PARAM(PREFIX, NAME, TYPE, DEFAULT_VALUE, DESCRIPTION) \
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public: \
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static std::string k##PREFIX##NAME() {return std::string(#PREFIX "/" #NAME);} \
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static TYPE default##PREFIX##NAME() {return DEFAULT_VALUE;} \
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private: \
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class Dummy##PREFIX##NAME { \
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public: \
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Dummy##PREFIX##NAME() {parameters_.insert(ParametersPair(#PREFIX "/" #NAME, #DEFAULT_VALUE)); \
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descriptions_.insert(ParametersPair(#PREFIX "/" #NAME, DESCRIPTION));} \
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}; \
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Dummy##PREFIX##NAME dummy##PREFIX##NAME;
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// end define PARAM
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/**
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* It's the same as the macro PARAM but it should be used for string parameters.
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* Macro used to create parameter's key and default value.
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* This macro must be used only in the Parameters class definition (in this file).
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* They are automatically added to the default parameters map of the class Parameters.
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* Example:
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* @code
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* //for PARAM_STR(Video, TextFileName, "Hello_world"), the output will be :
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* public:
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* static std::string kVideoFileName() {return std::string("Video/FileName");}
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* static std::string defaultVideoFileName() {return "Hello_world";}
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* private:
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* class DummyVideoFileName {
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* public:
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* DummyVideoFileName() {parameters_.insert(ParametersPair("Video/FileName", "Hello_world"));}
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* };
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* DummyVideoFileName dummyVideoFileName;
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* @endcode
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*/
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#define RTABMAP_PARAM_STR(PREFIX, NAME, DEFAULT_VALUE, DESCRIPTION) \
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public: \
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static std::string k##PREFIX##NAME() {return std::string(#PREFIX "/" #NAME);} \
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static std::string default##PREFIX##NAME() {return DEFAULT_VALUE;} \
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private: \
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class Dummy##PREFIX##NAME { \
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public: \
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Dummy##PREFIX##NAME() {parameters_.insert(ParametersPair(#PREFIX "/" #NAME, DEFAULT_VALUE)); \
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descriptions_.insert(ParametersPair(#PREFIX "/" #NAME, DESCRIPTION));} \
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}; \
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Dummy##PREFIX##NAME dummy##PREFIX##NAME;
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// end define PARAM
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/**
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* Macro used to create parameter's key and default value.
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* This macro must be used only in the Parameters class definition (in this file).
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* They are automatically added to the default parameters map of the class Parameters.
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* Example:
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* @code
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* //for PARAM(Video, ImageWidth, int, 640), the output will be :
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* public:
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* static std::string kVideoImageWidth() {return std::string("Video/ImageWidth");}
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* static int defaultVideoImageWidth() {return 640;}
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* private:
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* class DummyVideoImageWidth {
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* public:
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* DummyVideoImageWidth() {parameters_.insert(ParametersPair("Video/ImageWidth", "640"));}
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* };
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* DummyVideoImageWidth dummyVideoImageWidth;
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* @endcode
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*/
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#define RTABMAP_PARAM_COND(PREFIX, NAME, TYPE, COND, DEFAULT_VALUE1, DEFAULT_VALUE2, DESCRIPTION) \
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public: \
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static std::string k##PREFIX##NAME() {return std::string(#PREFIX "/" #NAME);} \
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static TYPE default##PREFIX##NAME() {return COND?DEFAULT_VALUE1:DEFAULT_VALUE2;} \
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private: \
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class Dummy##PREFIX##NAME { \
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public: \
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Dummy##PREFIX##NAME() {parameters_.insert(ParametersPair(#PREFIX "/" #NAME, COND?#DEFAULT_VALUE1:#DEFAULT_VALUE2)); \
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descriptions_.insert(ParametersPair(#PREFIX "/" #NAME, DESCRIPTION));} \
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}; \
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Dummy##PREFIX##NAME dummy##PREFIX##NAME;
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// end define PARAM
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/**
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* Class Parameters.
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* This class is used to manage all custom parameters
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* we want in the application. It was designed to be very easy to add
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* a new parameter (just by adding one line of code).
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* The macro PARAM(PREFIX, NAME, TYPE, DEFAULT_VALUE) is
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* used to create a parameter in this class. A parameter can be accessed after by
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* Parameters::defaultPARAMETERNAME() for the default value, Parameters::kPARAMETERNAME for his key (parameter name).
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* The class provides also a general map containing all the parameter's key and
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* default value. This map can be accessed anywhere in the application by
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* Parameters::getDefaultParameters();
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* Example:
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* @code
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* //Defining a parameter in this class with the macro PARAM:
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* PARAM(Video, ImageWidth, int, 640);
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*
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* // Now from anywhere in the application (Parameters is a singleton)
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* int width = Parameters::defaultVideoImageWidth(); // theDefaultValue = 640
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* std::string theKey = Parameters::kVideoImageWidth(); // theKey = "Video/ImageWidth"
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* std::string strValue = Util::value(Parameters::getDefaultParameters(), theKey); // strValue = "640"
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* @endcode
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* @see getDefaultParameters()
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* TODO Add a detailed example with simple classes
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*/
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class RTABMAP_EXP Parameters
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{
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// Rtabmap parameters
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RTABMAP_PARAM(Rtabmap, VhStrategy, int, 0, "None 0, Similarity 1, Epipolar 2.");
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RTABMAP_PARAM(Rtabmap, PublishStats, bool, true, "Publishing statistics.");
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RTABMAP_PARAM(Rtabmap, PublishLastSignature, bool, true, "Publishing last signature.");
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RTABMAP_PARAM(Rtabmap, PublishPdf, bool, true, "Publishing pdf.");
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RTABMAP_PARAM(Rtabmap, PublishLikelihood, bool, true, "Publishing likelihood.");
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RTABMAP_PARAM(Rtabmap, TimeThr, float, 0.0, "Maximum time allowed for the detector (ms) (0 means infinity).");
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RTABMAP_PARAM(Rtabmap, MemoryThr, int, 0, "Maximum signatures in the Working Memory (ms) (0 means infinity).");
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RTABMAP_PARAM(Rtabmap, DetectionRate, float, 1.0, "Detection rate. RTAB-Map will filter input images to satisfy this rate.");
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RTABMAP_PARAM(Rtabmap, ImageBufferSize, int, 1, "Data buffer size (0 min inf).");
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RTABMAP_PARAM_STR(Rtabmap, WorkingDirectory, Parameters::getDefaultWorkingDirectory(), "Working directory.");
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RTABMAP_PARAM(Rtabmap, MaxRetrieved, unsigned int, 2, "Maximum locations retrieved at the same time from LTM.");
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RTABMAP_PARAM(Rtabmap, StatisticLogsBufferedInRAM, bool, true, "Statistic logs buffered in RAM instead of written to hard drive after each iteration.");
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RTABMAP_PARAM(Rtabmap, StatisticLogged, bool, false, "Logging enabled.");
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RTABMAP_PARAM(Rtabmap, StatisticLoggedHeaders, bool, true, "Add column header description to log files.");
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RTABMAP_PARAM(Rtabmap, StartNewMapOnLoopClosure, bool, false, "Start a new map only if there is a global loop closure with a previous map.")
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// Hypotheses selection
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RTABMAP_PARAM(Rtabmap, LoopThr, float, 0.11, "Loop closing threshold.");
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RTABMAP_PARAM(Rtabmap, LoopRatio, float, 0.9, "The loop closure hypothesis must be over LoopRatio x lastHypothesisValue.");
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// Memory
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RTABMAP_PARAM(Mem, RehearsalSimilarity, float, 0.6, "Rehearsal similarity.");
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RTABMAP_PARAM(Mem, ImageKept, bool, true, "Keep images in db.");
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RTABMAP_PARAM(Mem, RehearsedNodesKept, bool, true, "Keep rehearsed ndoes in db.");
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RTABMAP_PARAM(Mem, STMSize, unsigned int, 10, "Short-term memory size.");
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RTABMAP_PARAM(Mem, IncrementalMemory, bool, true, "SLAM mode, othwersize it is Localization mode.");
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RTABMAP_PARAM(Mem, RecentWmRatio, float, 0.2, "Ratio of locations after the last loop closure in WM that cannot be transferred.");
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RTABMAP_PARAM(Mem, RehearsalIdUpdatedToNewOne, bool, false, "On merge, update to new id. When false, no copy.");
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RTABMAP_PARAM(Mem, GenerateIds, bool, true, "True=Generate location Ids, False=use input image ids.");
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RTABMAP_PARAM(Mem, BadSignaturesIgnored, bool, false, "Bad signatures are ignored.");
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RTABMAP_PARAM(Mem, InitWMWithAllNodes, bool, false, "Initialize the Working Memory with all nodes in Long-Term Memory. When false, it is initialized with nodes of the previous session.")
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// KeypointMemory (Keypoint-based)
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RTABMAP_PARAM_COND(Kp, NNStrategy, int, RTABMAP_NONFREE, 1, 3, "kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4");
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RTABMAP_PARAM(Kp, IncrementalDictionary, bool, true, "");
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RTABMAP_PARAM(Kp, MaxDepth, float, 0.0, "Filter extracted keypoints by depth (0=inf)");
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RTABMAP_PARAM(Kp, WordsPerImage, int, 400, "");
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RTABMAP_PARAM(Kp, BadSignRatio, float, 0.2, "Bad signature ratio (less than Ratio x AverageWordsPerImage = bad).");
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RTABMAP_PARAM(Kp, NndrRatio, float, 0.8, "NNDR ratio (A matching pair is detected, if its distance is closer than X times the distance of the second nearest neighbor.)");
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RTABMAP_PARAM_COND(Kp, DetectorStrategy, int, RTABMAP_NONFREE, 0, 2, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK.");
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RTABMAP_PARAM(Kp, TfIdfLikelihoodUsed, bool, true, "Use of the td-idf strategy to compute the likelihood.");
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RTABMAP_PARAM(Kp, Parallelized, bool, true, "If the dictionary update and signature creation were parallelized.");
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RTABMAP_PARAM_STR(Kp, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].");
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RTABMAP_PARAM_STR(Kp, DictionaryPath, "", "Path of the pre-computed dictionary");
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RTABMAP_PARAM(Kp, NewWordsComparedTogether, bool, true, "When adding new words to dictionary, they are compared also with each other (to detect same words in the same signature).");
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RTABMAP_PARAM(Kp, SubPixWinSize, int, 3, "See cv::cornerSubPix().");
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RTABMAP_PARAM(Kp, SubPixIterations, int, 0, "See cv::cornerSubPix(). 0 disables sub pixel refining.");
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RTABMAP_PARAM(Kp, SubPixEps, double, 0.02, "See cv::cornerSubPix().");
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//Database
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RTABMAP_PARAM(DbSqlite3, InMemory, bool, false, "Using database in the memory instead of a file on the hard disk.");
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RTABMAP_PARAM(DbSqlite3, CacheSize, unsigned int, 10000, "Sqlite cache size (default is 2000).");
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RTABMAP_PARAM(DbSqlite3, JournalMode, int, 3, "0=DELETE, 1=TRUNCATE, 2=PERSIST, 3=MEMORY, 4=OFF (see sqlite3 doc : \"PRAGMA journal_mode\")");
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RTABMAP_PARAM(DbSqlite3, Synchronous, int, 0, "0=OFF, 1=NORMAL, 2=FULL (see sqlite3 doc : \"PRAGMA synchronous\")");
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RTABMAP_PARAM(DbSqlite3, TempStore, int, 2, "0=DEFAULT, 1=FILE, 2=MEMORY (see sqlite3 doc : \"PRAGMA temp_store\")");
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// Keypoints descriptors/detectors
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RTABMAP_PARAM(SURF, Extended, bool, false, "Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).");
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RTABMAP_PARAM(SURF, HessianThreshold, float, 150.0, "Threshold for hessian keypoint detector used in SURF.");
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RTABMAP_PARAM(SURF, Octaves, int, 4, "Number of pyramid octaves the keypoint detector will use.");
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RTABMAP_PARAM(SURF, OctaveLayers, int, 2, "Number of octave layers within each octave.");
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RTABMAP_PARAM(SURF, Upright, bool, false, "Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).");
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RTABMAP_PARAM(SURF, GpuVersion, bool, false, "GPU-SURF: Use GPU version of SURF. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
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RTABMAP_PARAM(SURF, GpuKeypointsRatio, float, 0.01, "Used with SURF GPU.");
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RTABMAP_PARAM(SIFT, NFeatures, int, 0, "The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast).");
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RTABMAP_PARAM(SIFT, NOctaveLayers, int, 3, "The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.");
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RTABMAP_PARAM(SIFT, ContrastThreshold, double, 0.04, "The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.");
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RTABMAP_PARAM(SIFT, EdgeThreshold, double, 10.0, "The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).");
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RTABMAP_PARAM(SIFT, Sigma, double, 1.6, "The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.");
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RTABMAP_PARAM(BRIEF, Bytes, int, 32, "Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.");
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RTABMAP_PARAM(FAST, Threshold, int, 30, "Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.");
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RTABMAP_PARAM(FAST, NonmaxSuppression, bool, true, "If true, non-maximum suppression is applied to detected corners (keypoints).");
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RTABMAP_PARAM(FAST, Gpu, bool, false, "GPU-FAST: Use GPU version of FAST. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
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RTABMAP_PARAM(FAST, GpuKeypointsRatio, double, 0.05, "Used with FAST GPU.");
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RTABMAP_PARAM(GFTT, MaxCorners, int, 400, "");
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RTABMAP_PARAM(GFTT, QualityLevel, double, 0.01, "");
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RTABMAP_PARAM(GFTT, MinDistance, double, 5, "");
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RTABMAP_PARAM(GFTT, BlockSize, int, 3, "");
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RTABMAP_PARAM(GFTT, UseHarrisDetector, bool, false, "");
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RTABMAP_PARAM(GFTT, K, double, 0.04, "");
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RTABMAP_PARAM(ORB, NFeatures, int, 400, "The maximum number of features to retain.");
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RTABMAP_PARAM(ORB, ScaleFactor, float, 1.2, "Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.");
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RTABMAP_PARAM(ORB, NLevels, int, 1, "The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).");
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RTABMAP_PARAM(ORB, EdgeThreshold, int, 31, "This is size of the border where the features are not detected. It should roughly match the patchSize parameter.");
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RTABMAP_PARAM(ORB, FirstLevel, int, 0, "It should be 0 in the current implementation.");
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RTABMAP_PARAM(ORB, WTA_K, int, 2, "The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).");
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RTABMAP_PARAM(ORB, ScoreType, int, 0, "The default HARRIS_SCORE=0 means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE=1 is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.");
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RTABMAP_PARAM(ORB, PatchSize, int, 31, "size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.");
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RTABMAP_PARAM(ORB, Gpu, bool, false, "GPU-ORB: Use GPU version of ORB. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.");
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RTABMAP_PARAM(FREAK, OrientationNormalized, bool, true, "Enable orientation normalization.");
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RTABMAP_PARAM(FREAK, ScaleNormalized, bool, true, "Enable scale normalization.");
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RTABMAP_PARAM(FREAK, PatternScale, float, 22.0, "Scaling of the description pattern.");
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RTABMAP_PARAM(FREAK, NOctaves, int, 4, "Number of octaves covered by the detected keypoints.");
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RTABMAP_PARAM(BRISK, Thresh, int, 30, "FAST/AGAST detection threshold score.");
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RTABMAP_PARAM(BRISK, Octaves, int, 3, "Detection octaves. Use 0 to do single scale.");
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RTABMAP_PARAM(BRISK, PatternScale, float, 1.0, "Apply this scale to the pattern used for sampling the neighbourhood of a keypoint.");
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// BayesFilter
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RTABMAP_PARAM(Bayes, VirtualPlacePriorThr, float, 0.9, "Virtual place prior");
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RTABMAP_PARAM_STR(Bayes, PredictionLC, "0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-06 4.8e-08 1.2e-09 1.9e-11 2.2e-13 1.7e-15 8.5e-18 2.9e-20 6.9e-23", "Prediction of loop closures (Gaussian-like, here with sigma=1.6) - Format: {VirtualPlaceProb, LoopClosureProb, NeighborLvl1, NeighborLvl2, ...}.");
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RTABMAP_PARAM(Bayes, FullPredictionUpdate, bool, false, "Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).");
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// Verify hypotheses
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RTABMAP_PARAM(VhEp, MatchCountMin, int, 8, "Minimum of matching visual words pairs to accept the loop hypothesis.");
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RTABMAP_PARAM(VhEp, RansacParam1, float, 3.0, "Fundamental matrix (see cvFindFundamentalMat()): Max distance (in pixels) from the epipolar line for a point to be inlier.");
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RTABMAP_PARAM(VhEp, RansacParam2, float, 0.99, "Fundamental matrix (see cvFindFundamentalMat()): Performance of the RANSAC.");
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// RGB-D SLAM
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RTABMAP_PARAM(RGBD, Enabled, bool, true, "");
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RTABMAP_PARAM(RGBD, PoseScanMatching, bool, false, "Laser scan matching for odometry pose correction (laser scans are required).");
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RTABMAP_PARAM(RGBD, LinearUpdate, float, 0.0, "Min linear displacement to update the map. Rehearsal is done prior to this, so weights are still updated.");
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RTABMAP_PARAM(RGBD, AngularUpdate, float, 0.0, "Min angular displacement to update the map. Rehearsal is done prior to this, so weights are still updated.");
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RTABMAP_PARAM(RGBD, NewMapOdomChangeDistance, float, 0, "A new map is created if a change of odometry translation greater than X m is detected (0 m = disabled).");
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RTABMAP_PARAM(RGBD, ToroIterations, int, 100, "TORO graph optimization iterations");
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RTABMAP_PARAM(RGBD, OptimizeFromGraphEnd, bool, false, "Optimize graph from the newest node. If false, the graph is optimized from the oldest node of the current graph (this adds an overhead computation to detect to oldest mode of the current graph, but it can be useful to preserve the map referential from the oldest node). Warning when set to false: when some nodes are transferred, the first referential of the local map may change, resulting in momentary changes in robot/map position (which are annoying in teleoperation).");
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// Local loop closure detection
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RTABMAP_PARAM(RGBD, LocalLoopDetectionTime, bool, false, "Detection over all locations in STM.");
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RTABMAP_PARAM(RGBD, LocalLoopDetectionSpace, bool, false, "Detection over locations (in Working Memory or STM) near in space.");
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RTABMAP_PARAM(RGBD, LocalLoopDetectionRadius, float, 15, "Maximum radius for space detection.");
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RTABMAP_PARAM(RGBD, LocalLoopDetectionNeighbors, int, 20, "Maximum nearest neighbor.");
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RTABMAP_PARAM(RGBD, LocalLoopDetectionMaxDiffID, int, 0, "Maximum ID difference between the current/last loop closure location and the local loop closure hypotheses. Set 0 to ignore.")
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// Odometry
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RTABMAP_PARAM(Odom, Strategy, int, 0, "0=Bag-of-words 1=Optical Flow");
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RTABMAP_PARAM(Odom, FeatureType, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK.");
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RTABMAP_PARAM(Odom, LinearUpdate, float, 0.0, "Min linear displacement to update odometry.");
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RTABMAP_PARAM(Odom, AngularUpdate, float, 0.0, "Min angular displacement to update odometry.");
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RTABMAP_PARAM(Odom, MaxFeatures, int, 0, "0 no limits.");
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RTABMAP_PARAM(Odom, InlierDistance, float, 0.02, "Maximum distance for visual word correspondences.");
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RTABMAP_PARAM(Odom, MinInliers, int, 20, "Minimum visual word correspondences to compute geometry transform.");
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RTABMAP_PARAM(Odom, Iterations, int, 30, "Maximum iterations to compute the transform from visual words.");
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RTABMAP_PARAM(Odom, RefineIterations, int, 5, "Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.");
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RTABMAP_PARAM(Odom, MaxDepth, float, 4.0, "Max depth of the words (0 means no limit).");
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RTABMAP_PARAM(Odom, ResetCountdown, int, 0, "Automatically reset odometry after X consecutive images on which odometry cannot be computed (value=0 disables auto-reset).");
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RTABMAP_PARAM_STR(Odom, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].");
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RTABMAP_PARAM(Odom, FeaturesRatio, float, 0.0, "Minimum ratio of keypoints between the current image and the last image to compute odometry.");
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RTABMAP_PARAM(Odom, Force2D, bool, false, "Force 2D transform (3Dof: x,y and yaw).");
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// Odometry Bag-of-words
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RTABMAP_PARAM(OdomBow, LocalHistorySize, int, 1000, "Local history size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words.");
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RTABMAP_PARAM(OdomBow, NNType, int, 3, "kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4");
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RTABMAP_PARAM(OdomBow, NNDR, float, 0.8, "NNDR: nearest neighbor distance ratio.");
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// Odometry common stuff between BOW and Optical Flow approaches
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RTABMAP_PARAM(OdomFlow, WinSize, int, 16, "Used for optical flow approach and for stereo matching. See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(OdomFlow, Iterations, int, 30, "Used for optical flow approach and for stereo matching. See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(OdomFlow, Eps, double, 0.01, "Used for optical flow approach and for stereo matching. See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(OdomFlow, MaxLevel, int, 3, "Used for optical flow approach and for stereo matching. See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(OdomSubPix, WinSize, int, 3, "Can be used with BOW and optical flow approaches. See cv::cornerSubPix().");
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RTABMAP_PARAM(OdomSubPix, Iterations, int, 0, "Can be used with BOW and optical flow approaches. See cv::cornerSubPix(). 0 disables sub pixel refining.");
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RTABMAP_PARAM(OdomSubPix, Eps, double, 0.02, "Can be used with BOW and optical flow approaches. See cv::cornerSubPix().");
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// Loop closure constraint
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RTABMAP_PARAM(LccIcp, Type, int, 0, "0=No ICP, 1=ICP 3D, 2=ICP 2D");
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RTABMAP_PARAM(LccIcp, MaxDistance, float, 0.2, "Maximum ICP correction distance accepted (m).");
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RTABMAP_PARAM(LccBow, MinInliers, int, 20, "Minimum visual word correspondences to compute geometry transform.");
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RTABMAP_PARAM(LccBow, InlierDistance, float, 0.02, "Maximum distance for visual word correspondences.");
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RTABMAP_PARAM(LccBow, Iterations, int, 100, "Maximum iterations to compute the transform from visual words.");
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RTABMAP_PARAM(LccBow, MaxDepth, float, 4.0, "Max depth of the words (0 means no limit).");
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RTABMAP_PARAM(LccBow, Force2D, bool, false, "Force 2D transform (3Dof: x,y and yaw).");
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RTABMAP_PARAM_COND(LccReextract, Activated, bool, RTABMAP_NONFREE, false, true, "Activate re-extracting features on global loop closure.");
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RTABMAP_PARAM(LccReextract, NNType, int, 3, "kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4.");
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RTABMAP_PARAM(LccReextract, NNDR, float, 0.7, "NNDR: nearest neighbor distance ratio.");
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RTABMAP_PARAM(LccReextract, FeatureType, int, 4, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK.");
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RTABMAP_PARAM(LccReextract, MaxWords, int, 600, "0 no limits.");
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RTABMAP_PARAM(LccIcp3, Decimation, int, 8, "Depth image decimation.");
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RTABMAP_PARAM(LccIcp3, MaxDepth, float, 4.0, "Max cloud depth.");
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RTABMAP_PARAM(LccIcp3, VoxelSize, float, 0.01, "Voxel size to be used for ICP computation.");
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RTABMAP_PARAM(LccIcp3, Samples, int, 0, "Random samples to be used for ICP computation. Not used if voxelSize is set.");
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RTABMAP_PARAM(LccIcp3, MaxCorrespondenceDistance, float, 0.05, "ICP 3D: Max distance for point correspondences.");
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RTABMAP_PARAM(LccIcp3, Iterations, int, 30, "ICP 3D: Max iterations.");
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RTABMAP_PARAM(LccIcp3, MaxFitness, float, 1.0, "ICP 3D: Maximum fitness to accept the computed transform.");
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RTABMAP_PARAM(LccIcp3, PointToPlane, bool, false, "ICP 3D: Use point to plane ICP.");
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RTABMAP_PARAM(LccIcp3, PointToPlaneNormalNeighbors, int, 20, "ICP 3D: Number of neighbors to compute normals for point to plane.");
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RTABMAP_PARAM(LccIcp2, MaxCorrespondenceDistance, float, 0.1, "ICP 2D: Max distance for point correspondences.");
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RTABMAP_PARAM(LccIcp2, Iterations, int, 30, "ICP 2D: Max iterations.");
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RTABMAP_PARAM(LccIcp2, MaxFitness, float, 1.0, "ICP 2D: Maximum fitness to accept the computed transform.");
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RTABMAP_PARAM(LccIcp2, CorrespondenceRatio, float, 0.7, "ICP 2D: Ratio of matching correspondences to accept the transform.");
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RTABMAP_PARAM(LccIcp2, VoxelSize, float, 0.005, "Voxel size to be used for ICP computation.");
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// Stereo disparity
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RTABMAP_PARAM(Stereo, WinSize, int, 16, "See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(Stereo, Iterations, int, 30, "See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(Stereo, Eps, double, 0.01, "See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(Stereo, MaxLevel, int, 3, "See cv::calcOpticalFlowPyrLK().");
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RTABMAP_PARAM(Stereo, MaxSlope, float, 0.1, "The maximum slope for each stereo pairs.");
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public:
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virtual ~Parameters();
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/**
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* Get default parameters
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*
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*/
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static const ParametersMap & getDefaultParameters()
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{
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return parameters_;
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}
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/**
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* Get parameter description
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*
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*/
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static std::string getDescription(const std::string & paramKey);
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static void parse(const ParametersMap & parameters, const std::string & key, bool & value);
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static void parse(const ParametersMap & parameters, const std::string & key, int & value);
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static void parse(const ParametersMap & parameters, const std::string & key, unsigned int & value);
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static void parse(const ParametersMap & parameters, const std::string & key, float & value);
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static void parse(const ParametersMap & parameters, const std::string & key, double & value);
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static void parse(const ParametersMap & parameters, const std::string & key, std::string & value);
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static std::string getDefaultDatabaseName();
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private:
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Parameters();
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static std::string getDefaultWorkingDirectory();
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private:
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static ParametersMap parameters_;
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static ParametersMap descriptions_;
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static Parameters instance_;
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};
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}
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#endif /* PARAMETERS_H_ */
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