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0.19.7: added SuperPoint Torch feature support. RegVis: keep Feature2D detectors as class members instead of recreating them at each registration.

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matlabbe
2020-04-16 17:59:45 -04:00
parent 2ff582f06f
commit f575652456
16 changed files with 939 additions and 140 deletions
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26
corelib/include/rtabmap/core/Features2d.h
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@@ -87,6 +87,7 @@ typedef cv::cuda::FastFeatureDetector CV_FAST_GPU;
namespace rtabmap {
class ORBextractor;
class SPDetector;
class Stereo;
#if CV_MAJOR_VERSION < 3
@@ -107,7 +108,8 @@ public:
kFeatureBrisk=7,
kFeatureGfttOrb=8, //new 0.10.11
kFeatureKaze=9, //new 0.13.2
kFeatureOrbOctree=10}; //new 0.19.2
kFeatureOrbOctree=10, //new 0.19.2
kFeatureSuperPointTorch=11}; //new 0.19.7
static Feature2D * create(const ParametersMap & parameters = ParametersMap());
static Feature2D * create(Feature2D::Type type, const ParametersMap & parameters = ParametersMap()); // for convenience
@@ -496,6 +498,28 @@ private:
cv::Mat descriptors_;
};
//SuperPointTorch
class RTABMAP_EXP SuperPointTorch : public Feature2D
{
public:
SuperPointTorch(const ParametersMap & parameters = ParametersMap());
virtual ~SuperPointTorch();
virtual void parseParameters(const ParametersMap & parameters);
virtual Feature2D::Type getType() const { return kFeatureSuperPointTorch; }
private:
virtual std::vector<cv::KeyPoint> generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask = cv::Mat());
virtual cv::Mat generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const;
cv::Ptr<SPDetector> superPoint_;
std::string path_;
float threshold_;
bool nms_;
int minDistance_;
bool cuda_;
};
}

8
corelib/include/rtabmap/core/Parameters.h
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@@ -319,11 +319,17 @@ class RTABMAP_EXP Parameters
RTABMAP_PARAM(KAZE, Extended, bool, false, "Set to enable extraction of extended (128-byte) descriptor.");
RTABMAP_PARAM(KAZE, Upright, bool, false, "Set to enable use of upright descriptors (non rotation-invariant).");
RTABMAP_PARAM(KAZE, Threshold, float, 0.001, "Detector response threshold to accept point.");
RTABMAP_PARAM(KAZE, Threshold, float, 0.001, "Detector response threshold to accept keypoint.");
RTABMAP_PARAM(KAZE, NOctaves, int, 4, "Maximum octave evolution of the image.");
RTABMAP_PARAM(KAZE, NOctaveLayers, int, 4, "Default number of sublevels per scale level.");
RTABMAP_PARAM(KAZE, Diffusivity, int, 1, "Diffusivity type: 0=DIFF_PM_G1, 1=DIFF_PM_G2, 2=DIFF_WEICKERT or 3=DIFF_CHARBONNIER.");
RTABMAP_PARAM_STR(SPTorch, ModelPath, "", "[Required] Path to pre-trained weights Torch file of SuperPoint (*.pt).");
RTABMAP_PARAM(SPTorch, Threshold, float, 0.2, "Detector response threshold to accept keypoint.");
RTABMAP_PARAM(SPTorch, NMS, bool, true, "If true, non-maximum suppression is applied to detected keypoints.");
RTABMAP_PARAM(SPTorch, MinDistance, int, 4, uFormat("[%s=true] Minimum distance (pixels) between keypoints.", kSPTorchNMS().c_str()));
RTABMAP_PARAM(SPTorch, Cuda, bool, false, "Use Cuda device for Torch, otherwise CPU device is used by default.");
// BayesFilter
RTABMAP_PARAM(Bayes, VirtualPlacePriorThr, float, 0.9, "Virtual place prior");
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, ...}.");

5
corelib/include/rtabmap/core/RegistrationVis.h
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@@ -51,8 +51,6 @@ public:
int getIterations() const {return _iterations;}
int getMinInliers() const {return _minInliers;}
Feature2D * createFeatureDetector() const; // for convenience
protected:
virtual Transform computeTransformationImpl(
Signature & from,
@@ -90,6 +88,9 @@ private:
ParametersMap _featureParameters;
ParametersMap _bundleParameters;
Feature2D * _detectorFrom;
Feature2D * _detectorTo;
};
}