mirror of
https://github.com/introlab/rtabmap.git
synced 2026-09-02 09:30:25 +08:00
Added Daisy descriptor. ORB: updated default parameters. Making ORBOctree using ORB parameters. Updated Vis/CorNNDR default from 0.6 to 0.8 (increase number of matches with binary descriptors, increase slightly feature matching time with float descriptors). Note that jfr2018 scripts have been updated to use old value 0.6. rtabmap-info: show descriptor dimension and type.
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matlabbe
@@ -61,6 +61,7 @@ typedef cv::gpu::FAST_GPU CV_FAST_GPU;
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namespace cv{
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namespace xfeatures2d {
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class FREAK;
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class DAISY;
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class BriefDescriptorExtractor;
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#if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 4 && CV_MINOR_VERSION <= 3) || (CV_MAJOR_VERSION == 3 && (CV_MINOR_VERSION < 4 || (CV_MINOR_VERSION==4 && CV_SUBMINOR_VERSION<11)))
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class SIFT;
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@@ -81,6 +82,7 @@ typedef cv::SIFT CV_SIFT; // SIFT is back in features2d since 4.4.0 / 3.4.11
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typedef cv::xfeatures2d::SURF CV_SURF;
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typedef cv::FastFeatureDetector CV_FAST;
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typedef cv::xfeatures2d::FREAK CV_FREAK;
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typedef cv::xfeatures2d::DAISY CV_DAISY;
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typedef cv::GFTTDetector CV_GFTT;
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typedef cv::xfeatures2d::BriefDescriptorExtractor CV_BRIEF;
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typedef cv::BRISK CV_BRISK;
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@@ -116,7 +118,10 @@ public:
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kFeatureKaze=9, //new 0.13.2
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kFeatureOrbOctree=10, //new 0.19.2
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kFeatureSuperPointTorch=11, //new 0.19.7
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kFeatureSurfFreak=12}; //new 0.20.4
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kFeatureSurfFreak=12, //new 0.20.4
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kFeatureGfttDaisy=13, //new 0.20.6
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kFeatureSurfDaisy=14}; //new 0.20.6
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static std::string typeName(Type type)
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{
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switch(type){
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@@ -146,6 +151,10 @@ public:
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return "SUPERPOINT";
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case kFeatureSurfFreak:
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return "SURF+Freak";
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case kFeatureGfttDaisy:
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return "GFTT+Daisy";
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case kFeatureSurfDaisy:
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return "SURF+Daisy";
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default:
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return "Unknown";
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}
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@@ -563,6 +572,8 @@ private:
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private:
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float scaleFactor_;
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int nLevels_;
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int patchSize_;
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int edgeThreshold_;
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int fastThreshold_;
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int fastMinThreshold_;
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@@ -593,6 +604,54 @@ private:
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bool cuda_;
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};
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//GFTT_DAISY
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class RTABMAP_EXP GFTT_DAISY : public GFTT
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{
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public:
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GFTT_DAISY(const ParametersMap & parameters = ParametersMap());
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virtual ~GFTT_DAISY();
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virtual void parseParameters(const ParametersMap & parameters);
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virtual Feature2D::Type getType() const {return kFeatureGfttDaisy;}
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private:
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const;
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private:
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bool orientationNormalized_;
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bool scaleNormalized_;
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float patternScale_;
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int nOctaves_;
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#if CV_MAJOR_VERSION > 2
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cv::Ptr<CV_DAISY> _daisy;
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#endif
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};
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//SURF_DAISY
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class RTABMAP_EXP SURF_DAISY : public SURF
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{
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public:
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SURF_DAISY(const ParametersMap & parameters = ParametersMap());
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virtual ~SURF_DAISY();
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virtual void parseParameters(const ParametersMap & parameters);
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virtual Feature2D::Type getType() const {return kFeatureSurfDaisy;}
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private:
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const;
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private:
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bool orientationNormalized_;
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bool scaleNormalized_;
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float patternScale_;
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int nOctaves_;
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#if CV_MAJOR_VERSION > 2
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cv::Ptr<CV_DAISY> _daisy;
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#endif
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};
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}
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#endif /* FEATURES2D_H_ */
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@@ -244,9 +244,9 @@ class RTABMAP_EXP Parameters
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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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#if CV_MAJOR_VERSION > 2 && !defined(HAVE_OPENCV_XFEATURES2D)
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// OpenCV>2 without xFeatures2D module doesn't have BRIEF
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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.");
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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 12=SURF/FREAK 13=GFTT/DAISY 14=SURF/DAISY");
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#else
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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.");
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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 12=SURF/FREAK 13=GFTT/DAISY 14=SURF/DAISY");
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#endif
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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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@@ -300,9 +300,9 @@ class RTABMAP_EXP Parameters
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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, 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, 8, "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, ScaleFactor, float, 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, 3, "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, 19, "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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@@ -587,9 +587,9 @@ class RTABMAP_EXP Parameters
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RTABMAP_PARAM(Vis, Iterations, int, 300, "Maximum iterations to compute the transform.");
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#if CV_MAJOR_VERSION > 2 && !defined(HAVE_OPENCV_XFEATURES2D)
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// OpenCV>2 without xFeatures2D module doesn't have BRIEF
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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.");
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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 12=SURF/FREAK 13=GFTT/DAISY 14=SURF/DAISY");
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#else
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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.");
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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 12=SURF/FREAK 13=GFTT/DAISY 14=SURF/DAISY");
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#endif
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RTABMAP_PARAM(Vis, MaxFeatures, int, 1000, "0 no limits.");
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RTABMAP_PARAM(Vis, MaxDepth, float, 0, "Max depth of the features (0 means no limit).");
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@@ -603,7 +603,7 @@ class RTABMAP_EXP Parameters
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RTABMAP_PARAM(Vis, GridCols, int, 1, uFormat("Number of columns of the grid used to extract uniformly \"%s / grid cells\" features from each cell.", kVisMaxFeatures().c_str()));
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RTABMAP_PARAM(Vis, CorType, int, 0, "Correspondences computation approach: 0=Features Matching, 1=Optical Flow");
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RTABMAP_PARAM(Vis, CorNNType, int, 1, uFormat("[%s=0] kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4, BruteForceCrossCheck=5, SuperGlue=6, GMS=7. Used for features matching approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorNNDR, float, 0.6, uFormat("[%s=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorNNDR, float, 0.8, uFormat("[%s=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorGuessWinSize, int, 20, uFormat("[%s=0] Matching window size (pixels) around projected points when a guess transform is provided to find correspondences. 0 means disabled.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorGuessMatchToProjection, bool, false, uFormat("[%s=0] Match frame's corners to source's projected points (when guess transform is provided) instead of projected points to frame's corners.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorFlowWinSize, int, 16, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()));
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