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Increased version 0.20.4. Added parameter Kp/ByteToFloat (default true to use less memory with kdtree and binary descriptors). Memory/Sqlite3: Setting weight to -9 for invalid nodes (to make sure they are not reloaded from database, to fix a graph reduced issue). Added 12-SURF/FREAK detector approach. rtabmap-info: added number of nodes in each sessions.

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This commit is contained in:
matlabbe
2020-09-28 10:29:14 -04:00
parent c5158cade5
commit 933ac736f1
13 changed files with 257 additions and 82 deletions
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27
corelib/include/rtabmap/core/Features2d.h
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@@ -115,7 +115,8 @@ public:
kFeatureGfttOrb=8, //new 0.10.11
kFeatureKaze=9, //new 0.13.2
kFeatureOrbOctree=10, //new 0.19.2
kFeatureSuperPointTorch=11}; //new 0.19.7
kFeatureSuperPointTorch=11, //new 0.19.7
kFeatureSurfFreak=12}; //new 0.20.4
static std::string typeName(Type type)
{
switch(type){
@@ -143,6 +144,8 @@ public:
return "ORB-OCTREE";
case kFeatureSuperPointTorch:
return "SUPERPOINT";
case kFeatureSurfFreak:
return "SURF+Freak";
default:
return "Unknown";
}
@@ -455,6 +458,28 @@ private:
cv::Ptr<CV_FREAK> _freak;
};
//SURF_FREAK
class RTABMAP_EXP SURF_FREAK : public SURF
{
public:
SURF_FREAK(const ParametersMap & parameters = ParametersMap());
virtual ~SURF_FREAK();
virtual void parseParameters(const ParametersMap & parameters);
virtual Feature2D::Type getType() const {return kFeatureSurfFreak;}
private:
virtual cv::Mat generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const;
private:
bool orientationNormalized_;
bool scaleNormalized_;
float patternScale_;
int nOctaves_;
cv::Ptr<CV_FREAK> _freak;
};
//GFTT_ORB
class RTABMAP_EXP GFTT_ORB : public GFTT
{

9
corelib/include/rtabmap/core/Parameters.h
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@@ -236,6 +236,7 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(Kp, IncrementalDictionary, bool, true, "");
RTABMAP_PARAM(Kp, IncrementalFlann, bool, true, uFormat("When using FLANN based strategy, add/remove points to its index without always rebuilding the index (the index is built only when the dictionary increases of the factor \"%s\" in size).", kKpFlannRebalancingFactor().c_str()));
RTABMAP_PARAM(Kp, FlannRebalancingFactor, float, 2.0, uFormat("Factor used when rebuilding the incremental FLANN index (see \"%s\"). Set <=1 to disable.", kKpIncrementalFlann().c_str()));
RTABMAP_PARAM(Kp, ByteToFloat, bool, true, uFormat("For %s=1, binary descriptors are converted to float by converting each byte to float instead of converting each bit to float. When converting bytes instead of bits, less memory is used and search is faster at the cost of slightly less accurate matching.", kKpNNStrategy().c_str()));
RTABMAP_PARAM(Kp, MaxDepth, float, 0, "Filter extracted keypoints by depth (0=inf).");
RTABMAP_PARAM(Kp, MinDepth, float, 0, "Filter extracted keypoints by depth.");
RTABMAP_PARAM(Kp, MaxFeatures, int, 500, "Maximum features extracted from the images (0 means not bounded, <0 means no extraction).");
@@ -243,9 +244,9 @@ class RTABMAP_EXP Parameters
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.)");
#if CV_MAJOR_VERSION > 2 && !defined(HAVE_OPENCV_XFEATURES2D)
// OpenCV>2 without xFeatures2D module doesn't have BRIEF
RTABMAP_PARAM(Kp, DetectorStrategy, int, 8, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch.");
RTABMAP_PARAM(Kp, DetectorStrategy, int, 8, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch 12=SURF/FREAK.");
#else
RTABMAP_PARAM(Kp, DetectorStrategy, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch.");
RTABMAP_PARAM(Kp, DetectorStrategy, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch 12=SURF/FREAK.");
#endif
RTABMAP_PARAM(Kp, TfIdfLikelihoodUsed, bool, true, "Use of the td-idf strategy to compute the likelihood.");
RTABMAP_PARAM(Kp, Parallelized, bool, true, "If the dictionary update and signature creation were parallelized.");
@@ -586,9 +587,9 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(Vis, Iterations, int, 300, "Maximum iterations to compute the transform.");
#if CV_MAJOR_VERSION > 2 && !defined(HAVE_OPENCV_XFEATURES2D)
// OpenCV>2 without xFeatures2D module doesn't have BRIEF
RTABMAP_PARAM(Vis, FeatureType, int, 8, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch.");
RTABMAP_PARAM(Vis, FeatureType, int, 8, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch 12=SURF/FREAK.");
#else
RTABMAP_PARAM(Vis, FeatureType, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch.");
RTABMAP_PARAM(Vis, FeatureType, int, 6, "0=SURF 1=SIFT 2=ORB 3=FAST/FREAK 4=FAST/BRIEF 5=GFTT/FREAK 6=GFTT/BRIEF 7=BRISK 8=GFTT/ORB 9=KAZE 10=ORB-OCTREE 11=SuperPoint Torch 12=SURF/FREAK.");
#endif
RTABMAP_PARAM(Vis, MaxFeatures, int, 1000, "0 no limits.");
RTABMAP_PARAM(Vis, MaxDepth, float, 0, "Max depth of the features (0 means no limit).");

7
corelib/include/rtabmap/core/VWDictionary.h
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@@ -101,7 +101,7 @@ public:
int getTotalActiveReferences() const {return _totalActiveReferences;}
unsigned int getIndexedWordsCount() const;
unsigned int getIndexMemoryUsed() const;
void setNNStrategy(NNStrategy strategy);
bool setNNStrategy(NNStrategy strategy); // Return true if the search tree has been re-initialized
bool isIncremental() const {return _incrementalDictionary;}
bool isIncrementalFlann() const {return _incrementalFlann;}
void setIncrementalDictionary();
@@ -117,8 +117,8 @@ public:
void deleteUnusedWords();
public:
static cv::Mat convertBinTo32F(const cv::Mat & descriptorsIn);
static cv::Mat convert32FToBin(const cv::Mat & descriptorsIn);
static cv::Mat convertBinTo32F(const cv::Mat & descriptorsIn, bool byteToFloat = true);
static cv::Mat convert32FToBin(const cv::Mat & descriptorsIn, bool byteToFloat = true);
protected:
int getNextId();
@@ -131,6 +131,7 @@ private:
bool _incrementalDictionary;
bool _incrementalFlann;
float _rebalancingFactor;
bool _byteToFloat;
float _nndrRatio;
std::string _dictionaryPath; // a pre-computed dictionary (.txt or .db)
std::string _newDictionaryPath; // a pre-computed dictionary (.txt or .db)