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https://github.com/introlab/rtabmap.git
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merged master->branch
This commit is contained in:
matlabbe
@@ -134,7 +134,7 @@ public:
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public:
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// Mutex-protected methods of abstract versions below
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bool openConnection(const std::string & url, bool overwritten = false);
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bool openConnection(const std::string & url, bool overwritten = false, bool readOnly = false);
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void closeConnection(bool save = true, const std::string & outputUrl = "");
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bool isConnected() const;
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unsigned long getMemoryUsed() const; // In bytes
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@@ -193,7 +193,7 @@ public:
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protected:
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DBDriver(const ParametersMap & parameters = ParametersMap());
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virtual bool connectDatabaseQuery(const std::string & url, bool overwritten = false) = 0;
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virtual bool connectDatabaseQuery(const std::string & url, bool overwritten = false, bool readOnly = false) = 0;
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virtual void disconnectDatabaseQuery(bool save = true, const std::string & outputUrl = "") = 0;
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virtual bool isConnectedQuery() const = 0;
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virtual unsigned long getMemoryUsedQuery() const = 0; // In bytes
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@@ -51,7 +51,7 @@ public:
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void setTempStore(int tempStore);
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protected:
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virtual bool connectDatabaseQuery(const std::string & url, bool overwritten = false);
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virtual bool connectDatabaseQuery(const std::string & url, bool overwritten = false, bool readOnly = false);
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virtual void disconnectDatabaseQuery(bool save = true, const std::string & outputUrl = "");
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virtual bool isConnectedQuery() const;
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virtual unsigned long getMemoryUsedQuery() const; // In bytes
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@@ -59,6 +59,7 @@ public:
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int startMapId = 0,
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int stopMapId = -1,
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bool priorsIgnored = false,
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bool imuIgnored = false,
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const std::vector<Transform> & cameraLocalTransformOverrides = std::vector<Transform>());
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DBReader(const std::list<std::string> & databasePaths,
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float frameRate = 0.0f, // -1 = use Database stamps, 0 = inf
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@@ -74,6 +75,7 @@ public:
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int startMapId = 0,
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int stopMapId = -1,
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bool priorsIgnored = false,
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bool imuIgnored = false,
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const std::vector<Transform> & cameraLocalTransformOverrides = std::vector<Transform>());
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virtual ~DBReader();
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@@ -107,6 +109,7 @@ private:
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bool _landmarksIgnored;
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bool _featuresIgnored;
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bool _priorsIgnored;
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bool _imuIgnored;
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int _startMapId;
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int _stopMapId;
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std::vector<Transform> _cameraLocalTransformOverrides;
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@@ -309,7 +309,8 @@ private:
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bool preciseUpscale_;
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bool rootSIFT_;
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bool gpu_;
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float guaussianThreshold_;
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float gaussianThreshold_;
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float maxGaussianThreshold_;
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bool upscale_;
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cv::Ptr<CV_SIFT> sift_;
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@@ -277,7 +277,8 @@ std::list<std::pair<int, Transform> > RTABMAP_CORE_EXPORT computePath(
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bool lookInDatabase = true,
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bool updateNewCosts = false,
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float linearVelocity = 0.0f, // m/sec
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float angularVelocity = 0.0f); // rad/sec
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float angularVelocity = 0.0f, // rad/sec
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bool ignoreDirectLinks = false);
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/**
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* Find the nearest node of the target pose
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@@ -336,9 +337,7 @@ RTABMAP_DEPRECATED std::map<int, Transform> RTABMAP_CORE_EXPORT getPosesInRadius
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RTABMAP_DEPRECATED std::map<int, Transform> RTABMAP_CORE_EXPORT getPosesInRadius(const Transform & targetPose, const std::map<int, Transform> & nodes, float radius, float angle = 0.0f);
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float RTABMAP_CORE_EXPORT computePathLength(
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const std::vector<std::pair<int, Transform> > & path,
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unsigned int fromIndex = 0,
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unsigned int toIndex = 0);
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const std::vector<std::pair<int, Transform> > & path);
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// assuming they are all linked in map order
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float RTABMAP_CORE_EXPORT computePathLength(
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@@ -144,6 +144,7 @@ public:
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void saveLocationData(int locationId);
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void removeLink(int idA, int idB);
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void removeRawData(int id, bool image = true, bool scan = true, bool userData = true);
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int reduceNode(int id, float maxDistance = 0.0f, bool keepLinkedInDb = false, int direction = 0);
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//getters
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const std::map<int, double> & getWorkingMem() const {return _workingMem;}
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@@ -211,6 +212,7 @@ public:
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std::set<int> getAllSignatureIds(bool ignoreChildren = true) const;
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bool memoryChanged() const {return _memoryChanged;}
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bool isIncremental() const {return _incrementalMemory;}
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bool isReadOnly() const {return !_incrementalMemory && _localizationReadOnly;}
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bool isLocalizationDataSaved() const {return _localizationDataSaved;}
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const Signature * getSignature(int id) const;
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bool isInSTM(int signatureId) const {return _stMem.find(signatureId) != _stMem.end();}
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@@ -276,6 +278,7 @@ private:
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void initCountId();
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void rehearsal(Signature * signature, Statistics * stats = 0);
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bool rehearsalMerge(int oldId, int newId);
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bool canBeReduced(const Link & link, float maxDistance, int direction);
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const std::map<int, Signature*> & getSignatures() const {return _signatures;}
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@@ -307,6 +310,7 @@ private:
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std::string _rgbCompressionFormat;
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std::string _depthCompressionFormat;
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bool _incrementalMemory;
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bool _localizationReadOnly;
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bool _localizationDataSaved;
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bool _flannIndexSaved;
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bool _reduceGraph;
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@@ -213,6 +213,7 @@ class RTABMAP_CORE_EXPORT Parameters
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RTABMAP_PARAM_STR(Mem, DepthCompressionFormat, ".rvl", "Depth image compression format for 16UC1 depth type. It should be \".png\" or \".rvl\". If depth type is 32FC1, \".png\" is used.");
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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, otherwise it is Localization mode.");
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RTABMAP_PARAM(Mem, LocalizationReadOnly, bool, false, uFormat("In localization mode, open the database in read-only mode (ignored if %s=true). Currrenty incompatible with memory management (%s and %s cannot be used) and if there are disjoint sessions in working memory. Last localization pose won't be saved back in the database at the end of the session, so the robot will always restart to original last localization pose, unless %s is used or an external initial pose is provided on initialization.", kMemIncrementalMemory().c_str(), kRtabmapLoopThr().c_str(), kRtabmapMemoryThr().c_str(), kRGBDStartAtOrigin().c_str()).c_str());
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RTABMAP_PARAM(Mem, LocalizationDataSaved, bool, false, uFormat("Save localization data during localization session (when %s=false). When enabled, the database will then also grow in localization mode. This mode would be used only for debugging purpose.", kMemIncrementalMemory().c_str()).c_str());
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RTABMAP_PARAM(Mem, ReduceGraph, bool, false, uFormat("Reduce graph. Merge nodes when loop closures are added (ignoring those with user data). Note that this approach assumes that 100%% of the loop closures accepted are good, so it is highly recommended to enable \"%s\" at the same time.", kRGBDOptimizeMaxError().c_str()));
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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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@@ -223,7 +224,7 @@ class RTABMAP_CORE_EXPORT Parameters
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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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RTABMAP_PARAM(Mem, DepthAsMask, bool, true, "Use depth image as mask when extracting features for vocabulary.");
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RTABMAP_PARAM(Mem, DepthMaskFloorThr, float, 0.0, uFormat("Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled, negative means remove all objects above the floor threshold instead. Ignored if %s is false.", kMemDepthAsMask().c_str()));
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RTABMAP_PARAM(Mem, DepthMaskFloorThr, float, 0.0, uFormat("Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled. Ignored if %s is false.", kMemDepthAsMask().c_str()));
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RTABMAP_PARAM(Mem, StereoFromMotion, bool, false, uFormat("Triangulate features without depth using stereo from motion (odometry). It would be ignored if %s is true and the feature detector used supports masking.", kMemDepthAsMask().c_str()));
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RTABMAP_PARAM(Mem, ImagePreDecimation, unsigned int, 1, uFormat("Decimation of the RGB image before visual feature detection. If depth size is larger than decimated RGB size, depth is decimated to be always at most equal to RGB size. If %s is true and if depth is smaller than decimated RGB, depth may be interpolated to match RGB size for feature detection.",kMemDepthAsMask().c_str()));
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RTABMAP_PARAM(Mem, ImagePostDecimation, unsigned int, 1, uFormat("Decimation of the RGB image before saving it to database. If depth size is larger than decimated RGB size, depth is decimated to be always at most equal to RGB size. Decimation is done from the original image. If set to same value than %s, data already decimated is saved (no need to re-decimate the image).", kMemImagePreDecimation().c_str()));
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@@ -261,7 +262,7 @@ class RTABMAP_CORE_EXPORT Parameters
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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, FlannIndexSaved, bool, false, uFormat("Save FLANN index during localization session (when %s=false). The FLANN index will be saved to database after the first time localization mode is used, then on next sessions, the index is reloaded from the database instead of being rebuilt again. This can save significant loading time when the visual word dictionary is big (>1M words). Note that if the dictionary is modified (parameters or data), the index will be rebuilt and saved again on the next session.", kMemIncrementalMemory().c_str()).c_str());
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RTABMAP_PARAM(Kp, FlannIndexSaved, bool, false, uFormat("Save FLANN index during localization session (when %s=false). The FLANN index will be saved to database after the first time localization mode is used, then on next sessions, the index is reloaded from the database instead of being rebuilt again. This can save significant loading time when the visual word dictionary is big (>1M words). Note that if the dictionary is modified (parameters or data), the index will be rebuilt and saved again on the next session. Ignored on initialization if %s is enabled.", kMemIncrementalMemory().c_str(), kMemInitWMWithAllNodes().c_str()).c_str());
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RTABMAP_PARAM(Kp, SerializeWithChecksum, bool, true, "On serialization of the FLANN index, compute checksum of the data used by the FLANN index. This adds a slight overhead on serialization/deserialization to make sure that the dictionary data correspond to same data used when the index was built.");
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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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@@ -293,7 +294,8 @@ class RTABMAP_CORE_EXPORT Parameters
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RTABMAP_PARAM(SIFT, PreciseUpscale, bool, false, "Whether to enable precise upscaling in the scale pyramid (OpenCV >= 4.8).");
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RTABMAP_PARAM(SIFT, RootSIFT, bool, false, "Apply RootSIFT normalization of the descriptors.");
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RTABMAP_PARAM(SIFT, Gpu, bool, false, "CudaSift: Use GPU version of SIFT. This option is enabled only if RTAB-Map is built with CudaSift dependency and GPUs are detected.");
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RTABMAP_PARAM(SIFT, GaussianThreshold, float, 2.0, "CudaSift: Threshold on difference of Gaussians for feature pruning. The higher the threshold, the less features are produced by the detector.");
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RTABMAP_PARAM(SIFT, GaussianThreshold, float, 2.0, "CudaSift: Threshold on difference of Gaussians for feature pruning. The higher the threshold, the less features with low response/hessian are produced by the detector.");
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RTABMAP_PARAM(SIFT, MaxGaussianThreshold, float, 0.0, uFormat("CudaSift: Maximum threshold on difference of Gaussians for feature pruning (ignored if smaller or equal than %s). The lower the threshold, the less features with high response/hessian are produced by the detector.", kSIFTGaussianThreshold().c_str()));
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RTABMAP_PARAM(SIFT, Upscale, bool, false, "CudaSift: Whether to enable upscaling.");
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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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@@ -723,7 +725,7 @@ class RTABMAP_CORE_EXPORT Parameters
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RTABMAP_PARAM(Vis, MaxDepth, float, 0, "Max depth of the features (0 means no limit).");
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RTABMAP_PARAM(Vis, MinDepth, float, 0, "Min depth of the features (0 means no limit).");
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RTABMAP_PARAM(Vis, DepthAsMask, bool, true, "Use depth image as mask when extracting features.");
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RTABMAP_PARAM(Vis, DepthMaskFloorThr, float, 0.0, uFormat("Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled, negative means remove all objects above the floor threshold instead. Ignored if %s is false.", kVisDepthAsMask().c_str()));
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RTABMAP_PARAM(Vis, DepthMaskFloorThr, float, 0.0, uFormat("Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled. Ignored if %s is false.", kVisDepthAsMask().c_str()));
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RTABMAP_PARAM_STR(Vis, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].");
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RTABMAP_PARAM(Vis, SubPixWinSize, int, 3, "See cv::cornerSubPix().");
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RTABMAP_PARAM(Vis, SubPixIterations, int, 0, "See cv::cornerSubPix(). 0 disables sub pixel refining.");
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@@ -739,8 +741,11 @@ class RTABMAP_CORE_EXPORT Parameters
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RTABMAP_PARAM(Vis, CorFlowIterations, int, 30, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorFlowEps, float, 0.01, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorFlowMaxLevel, int, 3, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorFlowGpu, bool, false, uFormat("[%s=1] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA).", kVisCorType().c_str()));
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#if defined(RTABMAP_G2O) || defined(RTABMAP_ORB_SLAM)
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RTABMAP_PARAM(Vis, CorFlowUseMinEigenVals, bool, true, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach. Use minimum eigen values as an error measure, otherwise L1 distance between patches is used as an error measure.", kVisCorType().c_str()));
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RTABMAP_PARAM(Vis, CorFlowMinEigThreshold, float, 1e-4, uFormat("[%s=true] If the minimum eigenvalue of a feature's spatial gradient matrix is less than this threshold, then the feature is filtered out.", kVisCorFlowUseMinEigenVals().c_str()));
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RTABMAP_PARAM(Vis, CorFlowErrorThreshold, float, 20, uFormat("[%s=false] Filter out features with error greater than this threshold.", kVisCorFlowUseMinEigenVals().c_str()));
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RTABMAP_PARAM(Vis, CorFlowGpu, bool, false, uFormat("[%s=1] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA). Note that %s is not used in the GPU implementation.", kVisCorType().c_str(), kVisCorFlowUseMinEigenVals().c_str()));
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#if defined(RTABMAP_G2O) || defined(RTABMAP_ORB_SLAM)
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RTABMAP_PARAM(Vis, BundleAdjustment, int, 1, "Optimization with bundle adjustment: 0=disabled, 1=g2o, 2=cvsba, 3=Ceres.");
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#else
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RTABMAP_PARAM(Vis, BundleAdjustment, int, 0, "Optimization with bundle adjustment: 0=disabled, 1=g2o, 2=cvsba, 3=Ceres.");
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@@ -817,7 +822,10 @@ class RTABMAP_CORE_EXPORT Parameters
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RTABMAP_PARAM(Stereo, OpticalFlow, bool, true, "Use optical flow to find stereo correspondences, otherwise a simple block matching approach is used.");
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RTABMAP_PARAM(Stereo, SSD, bool, true, uFormat("[%s=false] Use Sum of Squared Differences (SSD) window, otherwise Sum of Absolute Differences (SAD) window is used.", kStereoOpticalFlow().c_str()));
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RTABMAP_PARAM(Stereo, Eps, double, 0.01, uFormat("[%s=true] Epsilon stop criterion.", kStereoOpticalFlow().c_str()));
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RTABMAP_PARAM(Stereo, Gpu, bool, false, uFormat("[%s=true] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA).", kStereoOpticalFlow().c_str()));
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RTABMAP_PARAM(Stereo, UseMinEigenVals, bool, true, uFormat("[%s=true] Use minimum eigen values as an error measure, otherwise L1 distance between patches is used as an error measure.", kStereoOpticalFlow().c_str()));
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RTABMAP_PARAM(Stereo, MinEigThreshold, double, 1e-4, uFormat("[%s=true] If the minimum eigenvalue of a feature's spatial gradient matrix is less than this threshold, then the feature is filtered out.", kStereoUseMinEigenVals().c_str()));
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RTABMAP_PARAM(Stereo, ErrorThreshold, double, 50, uFormat("[%s=false] Filter out features with error greater than this threshold.", kStereoUseMinEigenVals().c_str()));
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RTABMAP_PARAM(Stereo, Gpu, bool, false, uFormat("[%s=true] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA). Note that %s is not used in the GPU implementation.", kStereoOpticalFlow().c_str(), kStereoUseMinEigenVals().c_str()));
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RTABMAP_PARAM(Stereo, DenseStrategy, int, 0, "0=cv::StereoBM, 1=cv::StereoSGBM");
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@@ -41,6 +41,7 @@ public:
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inliersMeanDistance(0.0f),
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inliersDistribution(0.0f),
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matches(0),
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variance(0.0f),
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icpInliersRatio(0),
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icpTranslation(0.0f),
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icpRotation(0.0f),
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@@ -64,6 +65,7 @@ public:
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output.inliersDistribution = inliersDistribution;
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output.matches = matches;
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output.matchesPerCam = matchesPerCam;
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output.variance = variance;
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output.icpInliersRatio = icpInliersRatio;
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output.icpTranslation = icpTranslation;
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output.icpRotation = icpRotation;
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@@ -85,6 +87,7 @@ public:
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float inliersDistribution;
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std::vector<int> inliersIDs;
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int matches;
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float variance;
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std::vector<int> matchesIDs;
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std::vector<int> projectedIDs; // "From" IDs
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std::vector<int> inliersPerCam;
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@@ -91,6 +91,9 @@ private:
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float _flowEps;
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int _flowMaxLevel;
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bool _flowGpu;
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bool _flowUseMinEigenVals;
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float _flowMinEigThreshold;
|
||||
float _flowErrorThreshold;
|
||||
float _nndr;
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int _nnType;
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||||
bool _gmsWithRotation;
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@@ -209,7 +209,8 @@ public:
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bool intraSession = true,
|
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bool interSession = true,
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const ProgressState * state = 0,
|
||||
float clusterRadiusMin = 0.0f);
|
||||
float clusterRadiusMin = 0.0f,
|
||||
int toFromMapId = -1);
|
||||
bool globalBundleAdjustment(
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int optimizerType = 1 /*g2o*/,
|
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bool rematchFeatures = true,
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||||
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||||
@@ -117,6 +117,7 @@ class RTABMAP_CORE_EXPORT Statistics
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RTABMAP_STATS(Loop, Visual_inliers,);
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RTABMAP_STATS(Loop, Visual_inliers_ratio,);
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RTABMAP_STATS(Loop, Visual_matches,);
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RTABMAP_STATS(Loop, Visual_variance,);
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RTABMAP_STATS(Loop, Distance_since_last_loc, m);
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RTABMAP_STATS(Loop, Last_id,);
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RTABMAP_STATS(Loop, Optimization_max_error, m);
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@@ -311,6 +311,10 @@ public:
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*/
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virtual bool isGpuEnabled() const;
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bool usingMinEigenVals() const {return useMinEigenVals_;}
|
||||
float minEigThreshold() const {return minEigThreshold_;}
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||||
float errorThreshold() const {return errorThreshold_;}
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||||
private:
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||||
/**
|
||||
* @brief Update status vector based on disparity constraints
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||||
@@ -329,10 +333,14 @@ private:
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||||
void updateStatus(
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||||
const std::vector<cv::Point2f> & leftCorners,
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||||
const std::vector<cv::Point2f> & rightCorners,
|
||||
std::vector<unsigned char> & status) const;
|
||||
std::vector<unsigned char> & status,
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||||
std::vector<float> err = {}) const;
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||||
|
||||
private:
|
||||
float epsilon_; ///< Convergence threshold for optical flow (default: from Parameters::defaultStereoEps())
|
||||
bool useMinEigenVals_;
|
||||
float minEigThreshold_;
|
||||
float errorThreshold_;
|
||||
bool gpu_; ///< Enable GPU acceleration (default: from Parameters::defaultStereoGpu(), requires OpenCV CUDA)
|
||||
};
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||||
|
||||
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||||
@@ -187,11 +187,12 @@ public:
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* @brief Add a reference from a visual word to a signature
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||||
* @param wordId ID of the visual word
|
||||
* @param signatureId ID of the signature (image)
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||||
* @return true if the word exists in the dictionary and the reference has been added, false otherwise
|
||||
*
|
||||
* Tracks which signatures use which visual words. If the word was unused,
|
||||
* it is removed from the unused words list.
|
||||
*/
|
||||
void addWordRef(int wordId, int signatureId);
|
||||
bool addWordRef(int wordId, int signatureId);
|
||||
|
||||
/**
|
||||
* @brief Remove all references from a visual word to a signature
|
||||
|
||||
@@ -102,10 +102,11 @@ public:
|
||||
}
|
||||
|
||||
// 0=Raw, 1=RGBD-SLAM motion capture (10=without change of coordinate frame, 11=10+ID), 2=KITTI, 3=TORO, 4=g2o, 5=NewCollege(t,x,y), 6=Malaga Urban GPS, 7=St Lucia INS, 8=Karlsruhe, 9=EuRoC MAV, 12=rgbd_bonn
|
||||
void setGroundTruthPath(const std::string & filePath, int format = 0)
|
||||
void setGroundTruthPath(const std::string & filePath, int format = 0, const Transform & localTransform = Transform::getIdentity())
|
||||
{
|
||||
_groundTruthPath = filePath;
|
||||
_groundTruthFormat = format;
|
||||
_groundTruthLocalTransform = localTransform;
|
||||
}
|
||||
|
||||
void setMaxPoseTimeDiff(double diff) {_maxPoseTimeDiff = diff;}
|
||||
@@ -164,6 +165,7 @@ private:
|
||||
int _odometryFormat;
|
||||
std::string _groundTruthPath;
|
||||
int _groundTruthFormat;
|
||||
Transform _groundTruthLocalTransform;
|
||||
double _maxPoseTimeDiff;
|
||||
|
||||
std::list<double> _stamps;
|
||||
|
||||
@@ -146,6 +146,7 @@ private:
|
||||
Transform dualExtrinsics_;
|
||||
std::string jsonConfig_;
|
||||
bool closing_;
|
||||
bool playback_;
|
||||
|
||||
static Transform realsense2PoseRotation_;
|
||||
static Transform realsense2PoseRotationInv_;
|
||||
|
||||
@@ -57,7 +57,6 @@ protected:
|
||||
private:
|
||||
cv::Mat map_;
|
||||
cv::Mat mapInfo_;
|
||||
std::map<int, std::pair<int, int> > cellCount_; //<node Id, cells>
|
||||
|
||||
float minMapSize_;
|
||||
bool erode_;
|
||||
|
||||
@@ -160,6 +160,7 @@ std::map<int, cv::Point3f> RTABMAP_CORE_EXPORT generateWords3DMono(
|
||||
Transform & cameraTransform,
|
||||
float ransacReprojThreshold = 3.0f,
|
||||
float ransacConfidence = 0.99f,
|
||||
int varianceMedianRatio = 4,
|
||||
const std::map<int, cv::Point3f> & refGuess3D = std::map<int, cv::Point3f>(),
|
||||
double * variance = 0,
|
||||
std::vector<int> * matchesOut = 0);
|
||||
|
||||
Reference in New Issue
Block a user