rtabmap::Parameters Member List

This is the complete list of members for rtabmap::Parameters, including all inherited members.

backwardCompatibilityMap_rtabmap::Parametersprivatestatic
createDefaultWorkingDirectory()rtabmap::Parametersstatic
descriptions_rtabmap::Parametersprivatestatic
deserialize(const std::string &parameters)rtabmap::Parametersstatic
filterParameters(const ParametersMap &parameters, const std::string &group, bool remove=false)rtabmap::Parametersstatic
getBackwardCompatibilityMap()rtabmap::Parametersstatic
getDefaultDatabaseName()rtabmap::Parametersstatic
getDefaultOdometryParameters(bool stereo=false, bool vis=true, bool icp=false)rtabmap::Parametersstatic
getDefaultParameters()rtabmap::Parametersinlinestatic
getDefaultParameters(const std::string &group)rtabmap::Parametersstatic
getDescription(const std::string &paramKey)rtabmap::Parametersstatic
getRemovedParameters()rtabmap::Parametersstatic
getType(const std::string &paramKey)rtabmap::Parametersstatic
getVersion()rtabmap::Parametersstatic
instance_rtabmap::Parametersprivatestatic
isFeatureParameter(const std::string &param)rtabmap::Parametersstatic
Parameters()rtabmap::Parametersprivate
parameters_rtabmap::Parametersprivatestatic
parametersType_rtabmap::Parametersprivatestatic
parse(const ParametersMap &parameters, const std::string &key, bool &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, const std::string &key, int &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, const std::string &key, unsigned int &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, const std::string &key, float &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, const std::string &key, double &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, const std::string &key, std::string &value)rtabmap::Parametersstatic
parse(const ParametersMap &parameters, ParametersMap &parametersOut)rtabmap::Parametersstatic
parseArguments(int argc, char *argv[], bool onlyParameters=false)rtabmap::Parametersstatic
readINI(const std::string &configFile, ParametersMap &parameters, bool modifiedOnly=false)rtabmap::Parametersstatic
readINIStr(const std::string &configContent, ParametersMap &parameters, bool modifiedOnly=false)rtabmap::Parametersstatic
removedParameters_rtabmap::Parametersprivatestatic
RTABMAP_PARAM(Rtabmap, PublishStats, bool, true, "Publishing statistics.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, PublishLastSignature, bool, true, "Publishing last signature.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, PublishPdf, bool, true, "Publishing pdf.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, PublishLikelihood, bool, true, "Publishing likelihood.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, PublishRAMUsage, bool, false, "Publishing RAM usage in statistics (may add a small overhead to get info from the system).")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, ComputeRMSE, bool, true, "Compute root mean square error (RMSE) and publish it in statistics, if ground truth is provided.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, SaveWMState, bool, false, "Save working memory state after each update in statistics.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, TimeThr, float, 0, "Maximum time allowed for map update (ms) (0 means infinity). When map update time exceeds this fixed time threshold, some nodes in Working Memory (WM) are transferred to Long-Term Memory to limit the size of the WM and decrease the update time.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, MemoryThr, int, 0, uFormat("Maximum nodes in the Working Memory (0 means infinity). Similar to \"%s\", when the number of nodes in Working Memory (WM) exceeds this treshold, some nodes are transferred to Long-Term Memory to keep WM size fixed.", kRtabmapTimeThr().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, DetectionRate, float, 1, "Detection rate (Hz). RTAB-Map will filter input images to satisfy this rate.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, ImageBufferSize, unsigned int, 1, "Data buffer size (0 min inf).")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, CreateIntermediateNodes, bool, false, uFormat("Create intermediate nodes between loop closure detection. Only used when %s>0.", kRtabmapDetectionRate().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, MaxRetrieved, unsigned int, 2, "Maximum nodes retrieved at the same time from LTM.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, MaxRepublished, unsigned int, 2, uFormat("Maximum nodes republished when requesting missing data. When %s=false, only loop closure data is republished, otherwise the closest nodes from the current localization are republished first. Ignored if %s=false.", kRGBDEnabled().c_str(), kRtabmapPublishLastSignature().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, StatisticLogsBufferedInRAM, bool, true, "Statistic logs buffered in RAM instead of written to hard drive after each iteration.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, StatisticLogged, bool, false, "Logging enabled.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, StatisticLoggedHeaders, bool, true, "Add column header description to log files.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, StartNewMapOnLoopClosure, bool, false, "Start a new map only if there is a global loop closure with a previous map.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, StartNewMapOnGoodSignature, bool, false, uFormat("Start a new map only if the first signature is not bad (i.e., has enough features, see %s).", kKpBadSignRatio().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, ImagesAlreadyRectified, bool, true, "Images are already rectified. By default RTAB-Map assumes that received images are rectified. If they are not, they can be rectified by RTAB-Map if this parameter is false.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, RectifyOnlyFeatures, bool, false, uFormat("If \"%s\" is false and this parameter is true, the whole RGB image will not be rectified, only the features. Warning: As projection of RGB-D image to point cloud is assuming that images are rectified, the generated point cloud map will have wrong colors if this parameter is true.", kRtabmapImagesAlreadyRectified().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, LoopThr, float, 0.11, "Loop closing threshold.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, LoopRatio, float, 0, "The loop closure hypothesis must be over LoopRatio x lastHypothesisValue.")rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, LoopGPS, bool, true, uFormat("Use GPS to filter likelihood (if GPS is recorded). Only locations inside the local radius \"%s\" of the current GPS location are considered for loop closure detection.", kRGBDLocalRadius().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Rtabmap, VirtualPlaceLikelihoodRatio, int, 0, "Likelihood ratio for virtual place (for no loop closure hypothesis): 0=Mean / StdDev, 1=StdDev / (Max-Mean)")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RehearsalSimilarity, float, 0.6, "Rehearsal similarity.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, ImageKept, bool, false, "Keep raw images in RAM.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, BinDataKept, bool, true, "Keep binary data in db.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RawDescriptorsKept, bool, true, "Raw descriptors kept in memory.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, MapLabelsAdded, bool, true, "Create map labels. The first node of a map will be labeled as \"map#\" where # is the map ID.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, SaveDepth16Format, bool, false, "Save depth image into 16 bits format to reduce memory used. Warning: values over ~65 meters are ignored (maximum 65535 millimeters).")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, NotLinkedNodesKept, bool, true, "Keep not linked nodes in db (rehearsed nodes and deleted nodes).")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, IntermediateNodeDataKept, bool, false, "Keep intermediate node data in db.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, STMSize, unsigned int, 10, "Short-term memory size.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, IncrementalMemory, bool, true, "SLAM mode, otherwise it is Localization mode.")rtabmap::Parametersprivate
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())rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, ReduceGraph, bool, false, "Reduce graph. Merge nodes when loop closures are added (ignoring those with user data set).")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RecentWmRatio, float, 0.2, "Ratio of locations after the last loop closure in WM that cannot be transferred.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, TransferSortingByWeightId, bool, false, "On transfer, signatures are sorted by weight->ID only (i.e. the oldest of the lowest weighted signatures are transferred first). If false, the signatures are sorted by weight->Age->ID (i.e. the oldest inserted in WM of the lowest weighted signatures are transferred first). Note that retrieval updates the age, not the ID.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RehearsalIdUpdatedToNewOne, bool, false, "On merge, update to new id. When false, no copy.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RehearsalWeightIgnoredWhileMoving, bool, false, "When the robot is moving, weights are not updated on rehearsal.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, GenerateIds, bool, true, "True=Generate location IDs, False=use input image IDs.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, BadSignaturesIgnored, bool, false, "Bad signatures are ignored.")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, DepthAsMask, bool, true, "Use depth image as mask when extracting features for vocabulary.")rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, CompressionParallelized, bool, true, "Compression of sensor data is multi-threaded.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, LaserScanDownsampleStepSize, int, 1, "If > 1, downsample the laser scans when creating a signature.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, LaserScanVoxelSize, float, 0.0, uFormat("If > 0 m, voxel filtering is done on laser scans when creating a signature. If the laser scan had normals, they will be removed. To recompute the normals, make sure to use \"%s\" or \"%s\" parameters.", kMemLaserScanNormalK().c_str(), kMemLaserScanNormalRadius().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, LaserScanNormalK, int, 0, "If > 0 and laser scans don't have normals, normals will be computed with K search neighbors when creating a signature.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, LaserScanNormalRadius, float, 0.0, "If > 0 m and laser scans don't have normals, normals will be computed with radius search neighbors when creating a signature.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, UseOdomFeatures, bool, true, "Use odometry features instead of regenerating them.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, UseOdomGravity, bool, false, uFormat("Use odometry instead of IMU orientation to add gravity links to new nodes created. We assume that odometry is already aligned with gravity (e.g., we are using a VIO approach). Gravity constraints are used by graph optimization only if \"%s\" is not zero.", kOptimizerGravitySigma().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, CovOffDiagIgnored, bool, true, "Ignore off diagonal values of the covariance matrix.")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, GlobalDescriptorStrategy, int, 0, "Extract global descriptor from sensor data. 0=disabled, 1=PyDescriptor")rtabmap::Parametersprivate
RTABMAP_PARAM(Mem, RotateImagesUpsideUp, bool, false, "Rotate images so that upside is up if they are not already. This can be useful in case the robots don't have all same camera orientation but are using the same map, so that not rotation-invariant visual features can still be used across the fleet.")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, NNStrategy, int, 1, "kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, IncrementalDictionary, bool, true, "")rtabmap::Parametersprivate
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::Parametersprivate
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::Parametersprivate
RTABMAP_PARAM(Kp, ByteToFloat, bool, false, 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::Parametersprivate
RTABMAP_PARAM(Kp, MaxDepth, float, 0, "Filter extracted keypoints by depth (0=inf).")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, MinDepth, float, 0, "Filter extracted keypoints by depth.")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, MaxFeatures, int, 500, "Maximum features extracted from the images (0 means not bounded, <0 means no extraction).")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, SSC, bool, false, "If true, SSC (Suppression via Square Covering) is applied to limit keypoints.")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, BadSignRatio, float, 0.5, "Bad signature ratio (less than Ratio x AverageWordsPerImage = bad).")rtabmap::Parametersprivate
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.)")rtabmap::Parametersprivate
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 15=PyDetector")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, TfIdfLikelihoodUsed, bool, true, "Use of the td-idf strategy to compute the likelihood.")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, Parallelized, bool, true, "If the dictionary update and signature creation were parallelized.")rtabmap::Parametersprivate
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).")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, SubPixWinSize, int, 3, "See cv::cornerSubPix().")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, SubPixIterations, int, 0, "See cv::cornerSubPix(). 0 disables sub pixel refining.")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, SubPixEps, double, 0.02, "See cv::cornerSubPix().")rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, GridRows, int, 1, uFormat("Number of rows of the grid used to extract uniformly \"%s / grid cells\" features from each cell.", kKpMaxFeatures().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Kp, GridCols, int, 1, uFormat("Number of columns of the grid used to extract uniformly \"%s / grid cells\" features from each cell.", kKpMaxFeatures().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(DbSqlite3, InMemory, bool, false, "Using database in the memory instead of a file on the hard disk.")rtabmap::Parametersprivate
RTABMAP_PARAM(DbSqlite3, CacheSize, unsigned int, 10000, "Sqlite cache size (default is 2000).")rtabmap::Parametersprivate
RTABMAP_PARAM(DbSqlite3, JournalMode, int, 3, "0=DELETE, 1=TRUNCATE, 2=PERSIST, 3=MEMORY, 4=OFF (see sqlite3 doc : \"PRAGMA journal_mode\")")rtabmap::Parametersprivate
RTABMAP_PARAM(DbSqlite3, Synchronous, int, 0, "0=OFF, 1=NORMAL, 2=FULL (see sqlite3 doc : \"PRAGMA synchronous\")")rtabmap::Parametersprivate
RTABMAP_PARAM(DbSqlite3, TempStore, int, 2, "0=DEFAULT, 1=FILE, 2=MEMORY (see sqlite3 doc : \"PRAGMA temp_store\")")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, Extended, bool, false, "Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, HessianThreshold, float, 500, "Threshold for hessian keypoint detector used in SURF.")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, Octaves, int, 4, "Number of pyramid octaves the keypoint detector will use.")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, OctaveLayers, int, 2, "Number of octave layers within each octave.")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, Upright, bool, false, "Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(SURF, GpuKeypointsRatio, float, 0.01, "Used with SURF GPU.")rtabmap::Parametersprivate
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).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(SIFT, EdgeThreshold, double, 10, "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).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(SIFT, RootSIFT, bool, false, "Apply RootSIFT normalization of the descriptors.")rtabmap::Parametersprivate
RTABMAP_PARAM(BRIEF, Bytes, int, 32, "Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, Threshold, int, 20, "Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, NonmaxSuppression, bool, true, "If true, non-maximum suppression is applied to detected corners (keypoints).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, GpuKeypointsRatio, double, 0.05, "Used with FAST GPU.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, MinThreshold, int, 7, "Minimum threshold. Used only when FAST/GridRows and FAST/GridCols are set.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, MaxThreshold, int, 200, "Maximum threshold. Used only when FAST/GridRows and FAST/GridCols are set.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, GridRows, int, 0, "Grid rows (0 to disable). Adapts the detector to partition the source image into a grid and detect points in each cell.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, GridCols, int, 0, "Grid cols (0 to disable). Adapts the detector to partition the source image into a grid and detect points in each cell.")rtabmap::Parametersprivate
RTABMAP_PARAM(FAST, CV, int, 0, "Enable FastCV implementation if non-zero (and RTAB-Map is built with FastCV support). Values should be 9 and 10.")rtabmap::Parametersprivate
RTABMAP_PARAM(GFTT, QualityLevel, double, 0.001, "")rtabmap::Parametersprivate
RTABMAP_PARAM(GFTT, MinDistance, double, 7, "")rtabmap::Parametersprivate
RTABMAP_PARAM(GFTT, BlockSize, int, 3, "")rtabmap::Parametersprivate
RTABMAP_PARAM(GFTT, UseHarrisDetector, bool, false, "")rtabmap::Parametersprivate
RTABMAP_PARAM(GFTT, K, double, 0.04, "")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
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).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(ORB, FirstLevel, int, 0, "It should be 0 in the current implementation.")rtabmap::Parametersprivate
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).")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
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.")rtabmap::Parametersprivate
RTABMAP_PARAM(FREAK, OrientationNormalized, bool, true, "Enable orientation normalization.")rtabmap::Parametersprivate
RTABMAP_PARAM(FREAK, ScaleNormalized, bool, true, "Enable scale normalization.")rtabmap::Parametersprivate
RTABMAP_PARAM(FREAK, PatternScale, float, 22, "Scaling of the description pattern.")rtabmap::Parametersprivate
RTABMAP_PARAM(FREAK, NOctaves, int, 4, "Number of octaves covered by the detected keypoints.")rtabmap::Parametersprivate
RTABMAP_PARAM(BRISK, Thresh, int, 30, "FAST/AGAST detection threshold score.")rtabmap::Parametersprivate
RTABMAP_PARAM(BRISK, Octaves, int, 3, "Detection octaves. Use 0 to do single scale.")rtabmap::Parametersprivate
RTABMAP_PARAM(BRISK, PatternScale, float, 1,"Apply this scale to the pattern used for sampling the neighbourhood of a keypoint.")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, Extended, bool, false, "Set to enable extraction of extended (128-byte) descriptor.")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, Upright, bool, false, "Set to enable use of upright descriptors (non rotation-invariant).")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, Threshold, float, 0.001, "Detector response threshold to accept keypoint.")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, NOctaves, int, 4, "Maximum octave evolution of the image.")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, NOctaveLayers, int, 4, "Default number of sublevels per scale level.")rtabmap::Parametersprivate
RTABMAP_PARAM(KAZE, Diffusivity, int, 1, "Diffusivity type: 0=DIFF_PM_G1, 1=DIFF_PM_G2, 2=DIFF_WEICKERT or 3=DIFF_CHARBONNIER.")rtabmap::Parametersprivate
RTABMAP_PARAM(SuperPoint, Threshold, float, 0.010, "Detector response threshold to accept keypoint.")rtabmap::Parametersprivate
RTABMAP_PARAM(SuperPoint, NMS, bool, true, "If true, non-maximum suppression is applied to detected keypoints.")rtabmap::Parametersprivate
RTABMAP_PARAM(SuperPoint, NMSRadius, int, 4, uFormat("[%s=true] Minimum distance (pixels) between keypoints.", kSuperPointNMS().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(SuperPoint, Cuda, bool, true, "Use Cuda device for Torch, otherwise CPU device is used by default.")rtabmap::Parametersprivate
RTABMAP_PARAM(PyDetector, Cuda, bool, true, "Use cuda.")rtabmap::Parametersprivate
RTABMAP_PARAM(Bayes, VirtualPlacePriorThr, float, 0.9, "Virtual place prior")rtabmap::Parametersprivate
RTABMAP_PARAM(Bayes, FullPredictionUpdate, bool, false, "Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).")rtabmap::Parametersprivate
RTABMAP_PARAM(VhEp, Enabled, bool, false, uFormat("Verify visual loop closure hypothesis by computing a fundamental matrix. This is done prior to transformation computation when %s is enabled.", kRGBDEnabled().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(VhEp, MatchCountMin, int, 8, "Minimum of matching visual words pairs to accept the loop hypothesis.")rtabmap::Parametersprivate
RTABMAP_PARAM(VhEp, RansacParam1, float, 3, "Fundamental matrix (see cvFindFundamentalMat()): Max distance (in pixels) from the epipolar line for a point to be inlier.")rtabmap::Parametersprivate
RTABMAP_PARAM(VhEp, RansacParam2, float, 0.99, "Fundamental matrix (see cvFindFundamentalMat()): Performance of RANSAC.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, Enabled, bool, true, "Activate metric SLAM. If set to false, classic RTAB-Map loop closure detection is done using only images and without any metric information.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LinearUpdate, float, 0.1, "Minimum linear displacement (m) to update the map. Rehearsal is done prior to this, so weights are still updated.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, AngularUpdate, float, 0.1, "Minimum angular displacement (rad) to update the map. Rehearsal is done prior to this, so weights are still updated.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LinearSpeedUpdate, float, 0.0, "Maximum linear speed (m/s) to update the map (0 means not limit).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, AngularSpeedUpdate, float, 0.0, "Maximum angular speed (rad/s) to update the map (0 means not limit).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, AggressiveLoopThr, float, 0.05, uFormat("Loop closure threshold used (overriding %s) when a new mapping session is not yet linked to a map of the highest loop closure hypothesis. In localization mode, this threshold is used when there are no loop closure constraints with any map in the cache (%s). In all cases, the goal is to aggressively loop on a previous map in the database. Only used when %s is enabled. Set 1 to disable.", kRtabmapLoopThr().c_str(), kRGBDMaxOdomCacheSize().c_str(), kRGBDEnabled().c_str()))rtabmap::Parametersprivate
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).")rtabmap::Parametersprivate
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 node 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).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, OptimizeMaxError, float, 3.0, uFormat("Reject loop closures if optimization error ratio is greater than this value (0=disabled). Ratio is computed as absolute error over standard deviation of each link. This will help to detect when a wrong loop closure is added to the graph. Not compatible with \"%s\" if enabled.", kOptimizerRobust().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, MaxLoopClosureDistance, float, 0.0, "Reject loop closures/localizations if the distance from the map is over this distance (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ForceOdom3DoF, bool, true, uFormat("Force odometry pose to be 3DoF if %s=true.", kRegForce3DoF().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, StartAtOrigin, bool, false, uFormat("If true, rtabmap will assume the robot is starting from origin of the map. If false, rtabmap will assume the robot is restarting from the last saved localization pose from previous session (the place where it shut down previously). Used only in localization mode (%s=false).", kMemIncrementalMemory().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, GoalReachedRadius, float, 0.5, "Goal reached radius (m).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, PlanStuckIterations, int, 0, "Mark the current goal node on the path as unreachable if it is not updated after X iterations (0=disabled). If all upcoming nodes on the path are unreachabled, the plan fails.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, PlanLinearVelocity, float, 0, "Linear velocity (m/sec) used to compute path weights.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, PlanAngularVelocity, float, 0, "Angular velocity (rad/sec) used to compute path weights.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, GoalsSavedInUserData, bool, false, "When a goal is received and processed with success, it is saved in user data of the location with this format: \"GOAL:#\".")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, MaxLocalRetrieved, unsigned int, 2, "Maximum local locations retrieved (0=disabled) near the current pose in the local map or on the current planned path (those on the planned path have priority).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LocalRadius, float, 10, "Local radius (m) for nodes selection in the local map. This parameter is used in some approaches about the local map management.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LocalImmunizationRatio, float, 0.25, "Ratio of working memory for which local nodes are immunized from transfer.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ScanMatchingIdsSavedInLinks, bool, true, "Save scan matching IDs from one-to-many proximity detection in link's user data.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, NeighborLinkRefining, bool, false, uFormat("When a new node is added to the graph, the transformation of its neighbor link to the previous node is refined using registration approach selected (%s).", kRegStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LoopClosureIdentityGuess, bool, false, uFormat("Use Identity matrix as guess when computing loop closure transform, otherwise no guess is used, thus assuming that registration strategy selected (%s) can deal with transformation estimation without guess.", kRegStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LoopClosureReextractFeatures, bool, false, "Extract features even if there are some already in the nodes. Raw features are not saved in database.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LocalBundleOnLoopClosure, bool, false, "Do local bundle adjustment with neighborhood of the loop closure.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, InvertedReg, bool, false, "On loop closure, do registration from the target to reference instead of reference to target.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, CreateOccupancyGrid, bool, false, "Create local occupancy grid maps. See \"Grid\" group for parameters.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, MarkerDetection, bool, false, "Detect static markers to be added as landmarks for graph optimization. If input data have already landmarks, this will be ignored. See \"Marker\" group for parameters.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LoopCovLimited, bool, false, "Limit covariance of non-neighbor links to minimum covariance of neighbor links. In other words, if covariance of a loop closure link is smaller than the minimum covariance of odometry links, its covariance is set to minimum covariance of odometry links.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, MaxOdomCacheSize, int, 10, uFormat("Maximum odometry cache size. Used only in localization mode (when %s=false). This is used to get smoother localizations and to verify localization transforms (when %s!=0) to make sure we don't teleport to a location very similar to one we previously localized on. Set 0 to disable caching.", kMemIncrementalMemory().c_str(), kRGBDOptimizeMaxError().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LocalizationSmoothing, bool, true, uFormat("Adjust localization constraints based on optimized odometry cache poses (when %s>0).", kRGBDMaxOdomCacheSize().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, LocalizationPriorError, double, 0.001, uFormat("The corresponding variance (error x error) set to priors of the map's poses during localization (when %s>0).", kRGBDMaxOdomCacheSize().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityByTime, bool, false, "Detection over all locations in STM.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityBySpace, bool, true, "Detection over locations (in Working Memory) near in space.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityMaxGraphDepth, int, 50, "Maximum depth from the current/last loop closure location and the local loop closure hypotheses. Set 0 to ignore.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityMaxPaths, int, 3, "Maximum paths compared (from the most recent) for proximity detection. 0 means no limit.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityPathFilteringRadius, float, 1, "Path filtering radius to reduce the number of nodes to compare in a path in one-to-many proximity detection. The nearest node in a path should be inside that radius to be considered for one-to-one proximity detection.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityPathMaxNeighbors, int, 0, "Maximum neighbor nodes compared on each path for one-to-many proximity detection. Set to 0 to disable one-to-many proximity detection (by merging the laser scans).")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityPathRawPosesUsed, bool, true, "When comparing to a local path for one-to-many proximity detection, merge the scans using the odometry poses (with neighbor link optimizations) instead of the ones in the optimized local graph.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityAngle, float, 45, "Maximum angle (degrees) for one-to-one proximity detection.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityOdomGuess, bool, false, "Use odometry as motion guess for one-to-one proximity detection.")rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityGlobalScanMap, bool, false, uFormat("Create a global assembled map from laser scans for one-to-many proximity detection, replacing the original one-to-many proximity detection (i.e., detection against local paths). Only used in localization mode (%s=false), otherwise original one-to-many proximity detection is done. Note also that if graph is modified (i.e., memory management is enabled or robot jumps from one disjoint session to another in same database), the global scan map is cleared and one-to-many proximity detection is reverted to original approach.", kMemIncrementalMemory().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(RGBD, ProximityMergedScanCovFactor, double, 100.0, uFormat("Covariance factor for one-to-many proximity detection (when %s>0 and scans are used).", kRGBDProximityPathMaxNeighbors().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, Strategy, int, 0, "Graph optimization strategy: 0=TORO, 1=g2o, 2=GTSAM and 3=Ceres.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, Iterations, int, 100, "Optimization iterations.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, Epsilon, double, 0.00001, "Stop optimizing when the error improvement is less than this value.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, VarianceIgnored, bool, false, "Ignore constraints' variance. If checked, identity information matrix is used for each constraint. Otherwise, an information matrix is generated from the variance saved in the links.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, Robust, bool, false, uFormat("Robust graph optimization using Vertigo (only work for g2o and GTSAM optimization strategies). Not compatible with \"%s\" if enabled.", kRGBDOptimizeMaxError().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, PriorsIgnored, bool, true, "Ignore prior constraints (global pose or GPS) while optimizing. Currently only g2o and gtsam optimization supports this.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, LandmarksIgnored, bool, false, "Ignore landmark constraints while optimizing. Currently only g2o and gtsam optimization supports this.")rtabmap::Parametersprivate
RTABMAP_PARAM(Optimizer, GravitySigma, float, 0.0, uFormat("Gravity sigma value (>=0, typically between 0.1 and 0.3). Optimization is done while preserving gravity orientation of the poses. This should be used only with visual/lidar inertial odometry approaches, for which we assume that all odometry poses are aligned with gravity. Set to 0 to disable gravity constraints. Currently supported only with g2o and GTSAM optimization strategies (see %s).", kOptimizerStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(g2o, Solver, int, 0, "0=csparse 1=pcg 2=cholmod 3=Eigen")rtabmap::Parametersprivate
RTABMAP_PARAM(g2o, Optimizer, int, 0, "0=Levenberg 1=GaussNewton")rtabmap::Parametersprivate
RTABMAP_PARAM(g2o, PixelVariance, double, 1.0, "Pixel variance used for bundle adjustment.")rtabmap::Parametersprivate
RTABMAP_PARAM(g2o, RobustKernelDelta, double, 8, "Robust kernel delta used for bundle adjustment (0 means don't use robust kernel). Observations with chi2 over this threshold will be ignored in the second optimization pass.")rtabmap::Parametersprivate
RTABMAP_PARAM(g2o, Baseline, double, 0.075, "When doing bundle adjustment with RGB-D data, we can set a fake baseline (m) to do stereo bundle adjustment (if 0, mono bundle adjustment is done). For stereo data, the baseline in the calibration is used directly.")rtabmap::Parametersprivate
RTABMAP_PARAM(GTSAM, Optimizer, int, 1, "0=Levenberg 1=GaussNewton 2=Dogleg")rtabmap::Parametersprivate
RTABMAP_PARAM(GTSAM, Incremental, bool, false, uFormat("Do graph optimization incrementally (iSAM2) to increase optimization speed on loop closures. Note that only GaussNewton and Dogleg optimization algorithms are supported (%s) in this mode.", kGTSAMOptimizer().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(GTSAM, IncRelinearizeThreshold, double, 0.01, "Only relinearize variables whose linear delta magnitude is greater than this threshold. See GTSAM::ISAM2 doc for more info.")rtabmap::Parametersprivate
RTABMAP_PARAM(GTSAM, IncRelinearizeSkip, int, 1, "Only relinearize any variables every X calls to ISAM2::update(). See GTSAM::ISAM2 doc for more info.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, Strategy, int, 0, "0=Frame-to-Map (F2M) 1=Frame-to-Frame (F2F) 2=Fovis 3=viso2 4=DVO-SLAM 5=ORB_SLAM2 6=OKVIS 7=LOAM 8=MSCKF_VIO 9=VINS-Fusion 10=OpenVINS 11=FLOAM 12=Open3D")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ResetCountdown, int, 0, "Automatically reset odometry after X consecutive images on which odometry cannot be computed (value=0 disables auto-reset).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, Holonomic, bool, true, "If the robot is holonomic (strafing commands can be issued). If not, y value will be estimated from x and yaw values (y=x*tan(yaw)).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, FillInfoData, bool, true, "Fill info with data (inliers/outliers features).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ImageBufferSize, unsigned int, 1, "Data buffer size (0 min inf).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, FilteringStrategy, int, 0, "0=No filtering 1=Kalman filtering 2=Particle filtering. This filter is used to smooth the odometry output.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ParticleSize, unsigned int, 400, "Number of particles of the filter.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ParticleNoiseT, float, 0.002, "Noise (m) of translation components (x,y,z).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ParticleLambdaT, float, 100, "Lambda of translation components (x,y,z).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ParticleNoiseR, float, 0.002, "Noise (rad) of rotational components (roll,pitch,yaw).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ParticleLambdaR, float, 100, "Lambda of rotational components (roll,pitch,yaw).")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, KalmanProcessNoise, float, 0.001, "Process noise covariance value.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, KalmanMeasurementNoise, float, 0.01, "Process measurement covariance value.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, GuessMotion, bool, true, "Guess next transformation from the last motion computed.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, GuessSmoothingDelay, float, 0, uFormat("Guess smoothing delay (s). Estimated velocity is averaged based on last transforms up to this maximum delay. This can help to get smoother velocity prediction. Last velocity computed is used directly if \"%s\" is set or the delay is below the odometry rate.", kOdomFilteringStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, KeyFrameThr, float, 0.3, "[Visual] Create a new keyframe when the number of inliers drops under this ratio of features in last frame. Setting the value to 0 means that a keyframe is created for each processed frame.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, VisKeyFrameThr, int, 150, "[Visual] Create a new keyframe when the number of inliers drops under this threshold. Setting the value to 0 means that a keyframe is created for each processed frame.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ScanKeyFrameThr, float, 0.9, "[Geometry] Create a new keyframe when the number of ICP inliers drops under this ratio of points in last frame's scan. Setting the value to 0 means that a keyframe is created for each processed frame.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, ImageDecimation, unsigned int, 1, uFormat("Decimation of the RGB image before registration. 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.", kVisDepthAsMask().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, AlignWithGround, bool, false, "Align odometry with the ground on initialization.")rtabmap::Parametersprivate
RTABMAP_PARAM(Odom, Deskewing, bool, true, "Lidar deskewing. If input lidar has time channel, it will be deskewed with a constant motion model (with IMU orientation and/or guess if provided).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, MaxSize, int, 2000, "[Visual] Local map size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, MaxNewFeatures, int, 0, "[Visual] Maximum features (sorted by keypoint response) added to local map from a new key-frame. 0 means no limit.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, ScanMaxSize, int, 2000, "[Geometry] Maximum local scan map size.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, ScanSubtractRadius, float, 0.05, "[Geometry] Radius used to filter points of a new added scan to local map. This could match the voxel size of the scans.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, ScanSubtractAngle, float, 45, uFormat("[Geometry] Max angle (degrees) used to filter points of a new added scan to local map (when \"%s\">0). 0 means any angle.", kOdomF2MScanSubtractRadius().c_str()).c_str())rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, ScanRange, float, 0, "[Geometry] Distance Range used to filter points of local map (when > 0). 0 means local map is updated using time and not range.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, ValidDepthRatio, float, 0.75, "If a new frame has points without valid depth, they are added to local feature map only if points with valid depth on total points is over this ratio. Setting to 1 means no points without valid depth are added to local feature map.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, BundleAdjustment, int, 0, "Local bundle adjustment: 0=disabled, 1=g2o, 2=cvsba, 3=Ceres.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomF2M, BundleAdjustmentMaxFrames, int, 10, "Maximum frames used for bundle adjustment (0=inf or all current frames in the local map).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMono, InitMinFlow, float, 100, "Minimum optical flow required for the initialization step.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMono, InitMinTranslation, float, 0.1, "Minimum translation required for the initialization step.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMono, MinTranslation, float, 0.02, "Minimum translation to add new points to local map. On initialization, translation x 5 is used as the minimum.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMono, MaxVariance, float, 0.01, "Maximum variance to add new points to local map.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, FeatureWindowSize, int, 9, "The size of the n x n image patch surrounding each feature, used for keypoint matching.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, MaxPyramidLevel, int, 3, "The maximum Gaussian pyramid level to process the image at. Pyramid level 1 corresponds to the original image.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, MinPyramidLevel, int, 0, "The minimum pyramid level.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, TargetPixelsPerFeature, int, 250, "Specifies the desired feature density as a ratio of input image pixels per feature detected. This number is used to control the adaptive feature thresholding.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, FastThreshold, int, 20, "FAST threshold.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UseAdaptiveThreshold, bool, true, "Use FAST adaptive threshold.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, FastThresholdAdaptiveGain, double, 0.005, "FAST threshold adaptive gain.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UseHomographyInitialization, bool, true, "Use homography initialization.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UseBucketing, bool, true, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, BucketWidth, int, 80, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, BucketHeight, int, 80, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, MaxKeypointsPerBucket, int, 25, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UseImageNormalization, bool, false, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, InlierMaxReprojectionError, double, 1.5, "The maximum image-space reprojection error (in pixels) a feature match is allowed to have and still be considered an inlier in the set of features used for motion estimation.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, CliqueInlierThreshold, double, 0.1, "See Howard's greedy max-clique algorithm for determining the maximum set of mutually consisten feature matches. This specifies the compatibility threshold, in meters.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, MinFeaturesForEstimate, int, 20, "Minimum number of features in the inlier set for the motion estimate to be considered valid.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, MaxMeanReprojectionError, double, 10.0, "Maximum mean reprojection error over the inlier feature matches for the motion estimate to be considered valid.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UseSubpixelRefinement, bool, true, "Specifies whether or not to refine feature matches to subpixel resolution.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, FeatureSearchWindow, int, 25, "Specifies the size of the search window to apply when searching for feature matches across time frames. The search is conducted around the feature location predicted by the initial rotation estimate.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, UpdateTargetFeaturesWithRefined, bool, false, "When subpixel refinement is enabled, the refined feature locations can be saved over the original feature locations. This has a slightly negative impact on frame-to-frame visual odometry, but is likely better when using this library as part of a visual SLAM algorithm.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, StereoRequireMutualMatch, bool, true, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, StereoMaxDistEpipolarLine, double, 1.5, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, StereoMaxRefinementDisplacement, double, 1.0, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomFovis, StereoMaxDisparity, int, 128, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, RansacIters, int, 200, "Number of RANSAC iterations.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, InlierThreshold, double, 2.0, "Fundamental matrix inlier threshold.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, Reweighting, bool, true, "Lower border weights (more robust to calibration errors).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchNmsN, int, 3, "Non-max-suppression: min. distance between maxima (in pixels).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchNmsTau, int, 50, "Non-max-suppression: interest point peakiness threshold.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchBinsize, int, 50, "Matching bin width/height (affects efficiency only).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchRadius, int, 200, "Matching radius (du/dv in pixels).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchDispTolerance, int, 2, "Disparity tolerance for stereo matches (in pixels).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchOutlierDispTolerance, int, 5, "Outlier removal: disparity tolerance (in pixels).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchOutlierFlowTolerance, int, 5, "Outlier removal: flow tolerance (in pixels).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchMultiStage, bool, true, "Multistage matching (denser and faster).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchHalfResolution, bool, true, "Match at half resolution, refine at full resolution.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, MatchRefinement, int, 1, "Refinement (0=none,1=pixel,2=subpixel).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, BucketMaxFeatures, int, 2, "Maximal number of features per bucket.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, BucketWidth, double, 50, "Width of bucket.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomViso2, BucketHeight, double, 50, "Height of bucket.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, Bf, double, 0.076, "Fake IR projector baseline (m) used only when stereo is not used.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, ThDepth, double, 40.0, "Close/Far threshold. Baseline times.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, Fps, float, 0.0, "Camera FPS (0 to estimate from input data).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, MaxFeatures, int, 1000, "Maximum ORB features extracted per frame.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, MapSize, int, 3000, "Maximum size of the feature map (0 means infinite). Only supported with ORB_SLAM2.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, Inertial, bool, false, "Enable IMU. Only supported with ORB_SLAM3.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, GyroNoise, double, 0.01, "IMU gyroscope \"white noise\".")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, AccNoise, double, 0.1, "IMU accelerometer \"white noise\".")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, GyroWalk, double, 0.000001, "IMU gyroscope \"random walk\".")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, AccWalk, double, 0.0001, "IMU accelerometer \"random walk\".")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomORBSLAM, SamplingRate, double, 0, "IMU sampling rate (0 to estimate from input data).")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, Sensor, int, 2, "Velodyne sensor: 0=VLP-16, 1=HDL-32, 2=HDL-64E")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, ScanPeriod, float, 0.1, "Scan period (s)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, Resolution, float, 0.2, "Map resolution")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, LinVar, float, 0.01, "Linear output variance.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, AngVar, float, 0.01, "Angular output variance.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomLOAM, LocalMapping, bool, true, "Local mapping. It adds more time to compute odometry, but accuracy is significantly improved.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, GridRow, int, 4, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, GridCol, int, 5, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, GridMinFeatureNum, int, 3, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, GridMaxFeatureNum, int, 4, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, PyramidLevels, int, 3, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, PatchSize, int, 15, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, FastThreshold, int, 10, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, MaxIteration, int, 30, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, TrackPrecision, double, 0.01, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, RansacThreshold, double, 3, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, StereoThreshold, double, 5, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, PositionStdThreshold, double, 8.0, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, RotationThreshold, double, 0.2618, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, TranslationThreshold, double, 0.4, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, TrackingRateThreshold, double, 0.5, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, OptTranslationThreshold, double, 0, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, NoiseGyro, double, 0.005, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, NoiseAcc, double, 0.05, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, NoiseGyroBias, double, 0.001, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, NoiseAccBias, double, 0.01, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, NoiseFeature, double, 0.035, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, InitCovVel, double, 0.25, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, InitCovGyroBias, double, 0.01, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, InitCovAccBias, double, 0.01, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, InitCovExRot, double, 0.00030462, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, InitCovExTrans, double, 0.000025, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomMSCKF, MaxCamStateSize, int, 20, "")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UseStereo, bool, true, "If we have more than 1 camera, if we should try to track stereo constraints between pairs")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UseKLT, bool, true, "If true we will use KLT, otherwise use a ORB descriptor + robust matching")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, NumPts, int, 200, "Number of points (per camera) we will extract and try to track")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, MinPxDist, int, 15, "Eistance between features (features near each other provide less information)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FiTriangulate1d, bool, false, "If we should perform 1d triangulation instead of 3d")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FiRefineFeatures, bool, true, "If we should perform Levenberg-Marquardt refinement")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FiMaxRuns, int, 5, "Max runs for Levenberg-Marquardt")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FiMaxBaseline, double, 40, "Max baseline ratio to accept triangulated features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FiMaxCondNumber, double, 10000, "Max condition number of linear triangulation matrix accept triangulated features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UseFEJ, bool, true, "If first-estimate Jacobians should be used (enable for good consistency)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, Integration, int, 1, "0=discrete, 1=rk4, 2=analytical (if rk4 or analytical used then analytical covariance propagation is used)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, CalibCamExtrinsics, bool, false, "Bool to determine whether or not to calibrate imu-to-camera pose")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, CalibCamIntrinsics, bool, false, "Bool to determine whether or not to calibrate camera intrinsics")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, CalibCamTimeoffset, bool, false, "Bool to determine whether or not to calibrate camera to IMU time offset")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, CalibIMUIntrinsics, bool, false, "Bool to determine whether or not to calibrate the IMU intrinsics")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, CalibIMUGSensitivity, bool, false, "Bool to determine whether or not to calibrate the Gravity sensitivity")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, MaxClones, int, 11, "Max clone size of sliding window")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, MaxSLAM, int, 50, "Max number of estimated SLAM features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, MaxSLAMInUpdate, int, 25, "Max number of SLAM features we allow to be included in a single EKF update.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, MaxMSCKFInUpdate, int, 50, "Max number of MSCKF features we will use at a given image timestep.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FeatRepMSCKF, int, 0, "What representation our features are in (msckf features)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, FeatRepSLAM, int, 4, "What representation our features are in (slam features)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, DtSLAMDelay, double, 0.0, "Delay, in seconds, that we should wait from init before we start estimating SLAM features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, GravityMag, double, 9.81, "Gravity magnitude in the global frame (i.e. should be 9.81 typically)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitWindowTime, double, 2.0, "Amount of time we will initialize over (seconds)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitIMUThresh, double, 1.0, "Variance threshold on our acceleration to be classified as moving")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitMaxDisparity, double, 10.0, "Max disparity to consider the platform stationary (dependent on resolution)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitMaxFeatures, int, 50, "How many features to track during initialization (saves on computation)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynUse, bool, false, "If dynamic initialization should be used")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMLEOptCalib, bool, false, "If we should optimize calibration during intialization (not recommended)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMLEMaxIter, int, 50, "How many iterations the MLE refinement should use (zero to skip the MLE)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMLEMaxTime, double, 0.05, "How many seconds the MLE should be completed in")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMLEMaxThreads, int, 6, "How many threads the MLE should use")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynNumPose, int, 6, "Number of poses to use within our window time (evenly spaced)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMinDeg, double, 10.0, "Orientation change needed to try to init")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynInflationOri, double, 10.0, "What to inflate the recovered q_GtoI covariance by")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynInflationVel, double, 100.0, "What to inflate the recovered v_IinG covariance by")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynInflationBg, double, 10.0, "What to inflate the recovered bias_g covariance by")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynInflationBa, double, 100.0, "What to inflate the recovered bias_a covariance by")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, InitDynMinRecCond, double, 1e-15, "Reciprocal condition number thresh for info inversion")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, TryZUPT, bool, true, "If we should try to use zero velocity update")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, ZUPTChi2Multiplier, double, 0.0, "Chi2 multiplier for zero velocity")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, ZUPTMaxVelodicy, double, 0.1, "Max velocity we will consider to try to do a zupt (i.e. if above this, don't do zupt)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, ZUPTNoiseMultiplier, double, 10.0, "Multiplier of our zupt measurement IMU noise matrix (default should be 1.0)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, ZUPTMaxDisparity, double, 0.5, "Max disparity we will consider to try to do a zupt (i.e. if above this, don't do zupt)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, ZUPTOnlyAtBeginning, bool, false, "If we should only use the zupt at the very beginning static initialization phase")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, AccelerometerNoiseDensity, double, 0.01, "[m/s^2/sqrt(Hz)] (accel \"white noise\")")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, AccelerometerRandomWalk, double, 0.001, "[m/s^3/sqrt(Hz)] (accel bias diffusion)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, GyroscopeNoiseDensity, double, 0.001, "[rad/s/sqrt(Hz)] (gyro \"white noise\")")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, GyroscopeRandomWalk, double, 0.0001, "[rad/s^2/sqrt(Hz)] (gyro bias diffusion)")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UpMSCKFSigmaPx, double, 1.0, "Pixel noise for MSCKF features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UpMSCKFChi2Multiplier, double, 1.0, "Chi2 multiplier for MSCKF features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UpSLAMSigmaPx, double, 1.0, "Pixel noise for SLAM features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpenVINS, UpSLAMChi2Multiplier, double, 1.0, "Chi2 multiplier for SLAM features")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpen3D, MaxDepth, float, 3.0, "Maximum depth.")rtabmap::Parametersprivate
RTABMAP_PARAM(OdomOpen3D, Method, int, 0, "Registration method: 0=PointToPlane, 1=Intensity, 2=Hybrid.")rtabmap::Parametersprivate
RTABMAP_PARAM(Reg, RepeatOnce, bool, true, "Do a second registration with the output of the first registration as guess. Only done if no guess was provided for the first registration (like on loop closure). It can be useful if the registration approach used can use a guess to get better matches.")rtabmap::Parametersprivate
RTABMAP_PARAM(Reg, Strategy, int, 0, "0=Vis, 1=Icp, 2=VisIcp")rtabmap::Parametersprivate
RTABMAP_PARAM(Reg, Force3DoF, bool, false, "Force 3 degrees-of-freedom transform (3Dof: x,y and yaw). Parameters z, roll and pitch will be set to 0.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, EstimationType, int, 1, "Motion estimation approach: 0:3D->3D, 1:3D->2D (PnP), 2:2D->2D (Epipolar Geometry)")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, ForwardEstOnly, bool, true, "Forward estimation only (A->B). If false, a transformation is also computed in backward direction (B->A), then the two resulting transforms are merged (middle interpolation between the transforms).")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, InlierDistance, float, 0.1, uFormat("[%s = 0] Maximum distance for feature correspondences. Used by 3D->3D estimation approach.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, RefineIterations, int, 5, uFormat("[%s = 0] Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPReprojError, float, 2, uFormat("[%s = 1] PnP reprojection error.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPFlags, int, 0, uFormat("[%s = 1] PnP flags: 0=Iterative, 1=EPNP, 2=P3P", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPRefineIterations, int, 1, uFormat("[%s = 1] Refine iterations. Set to 0 if \"%s\" is also used.", kVisEstimationType().c_str(), kVisBundleAdjustment().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPVarianceMedianRatio, int, 4, uFormat("[%s = 1] Ratio used to compute variance of the estimated transformation if 3D correspondences are provided (should be > 1). The higher it is, the smaller the covariance will be. With accurate depth estimation, this could be set to 2. For depth estimated by stereo, 4 or more maybe used to ignore large errors of very far points.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPMaxVariance, float, 0.0, uFormat("[%s = 1] Max linear variance between 3D point correspondences after PnP. 0 means disabled.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPSamplingPolicy, unsigned int, 1, uFormat("[%s = 1] Multi-camera random sampling policy: 0=AUTO, 1=ANY, 2=HOMOGENEOUS. With HOMOGENEOUS policy, RANSAC will be done uniformly against all cameras, so at least 2 matches per camera are required. With ANY policy, RANSAC is not constraint to sample on all cameras at the same time. AUTO policy will use HOMOGENEOUS if there are at least 2 matches per camera, otherwise it will fallback to ANY policy.", kVisEstimationType().c_str()).c_str())rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, PnPSplitLinearCovComponents, bool, false, uFormat("[%s = 1] Compute variance for each linear component instead of using the combined XYZ variance for all linear components.", kVisEstimationType().c_str()).c_str())rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, EpipolarGeometryVar, float, 0.1, uFormat("[%s = 2] Epipolar geometry maximum variance to accept the transformation.", kVisEstimationType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MinInliers, int, 20, "Minimum feature correspondences to compute/accept the transformation.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MeanInliersDistance, float, 0.0, "Maximum distance (m) of the mean distance of inliers from the camera to accept the transformation. 0 means disabled.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MinInliersDistribution, float, 0.0, "Minimum distribution value of the inliers in the image to accept the transformation. The distribution is the second eigen value of the PCA (Principal Component Analysis) on the keypoints of the normalized image [-0.5, 0.5]. The value would be between 0 and 0.5. 0 means disabled.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, Iterations, int, 300, "Maximum iterations to compute the transform.")rtabmap::Parametersprivate
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 15=PyDetector")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MaxFeatures, int, 1000, "0 no limits.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, SSC, bool, false, "If true, SSC (Suppression via Square Covering) is applied to limit keypoints.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MaxDepth, float, 0, "Max depth of the features (0 means no limit).")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, MinDepth, float, 0, "Min depth of the features (0 means no limit).")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, DepthAsMask, bool, true, "Use depth image as mask when extracting features.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, SubPixWinSize, int, 3, "See cv::cornerSubPix().")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, SubPixIterations, int, 0, "See cv::cornerSubPix(). 0 disables sub pixel refining.")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, SubPixEps, float, 0.02, "See cv::cornerSubPix().")rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, GridRows, int, 1, uFormat("Number of rows of the grid used to extract uniformly \"%s / grid cells\" features from each cell.", kVisMaxFeatures().c_str()))rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorType, int, 0, "Correspondences computation approach: 0=Features Matching, 1=Optical Flow")rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorNNDR, float, 0.8, uFormat("[%s=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach.", kVisCorType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorGuessWinSize, int, 40, 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()))rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorFlowWinSize, int, 16, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorFlowIterations, int, 30, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorFlowEps, float, 0.01, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, CorFlowMaxLevel, int, 3, uFormat("[%s=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.", kVisCorType().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Vis, BundleAdjustment, int, 0, "Optimization with bundle adjustment: 0=disabled, 1=g2o, 2=cvsba, 3=Ceres.")rtabmap::Parametersprivate
RTABMAP_PARAM(PyMatcher, Iterations, int, 20, "Sinkhorn iterations. Used by SuperGlue.")rtabmap::Parametersprivate
RTABMAP_PARAM(PyMatcher, Threshold, float, 0.2, "Used by SuperGlue.")rtabmap::Parametersprivate
RTABMAP_PARAM(PyMatcher, Cuda, bool, true, "Used by SuperGlue.")rtabmap::Parametersprivate
RTABMAP_PARAM(GMS, WithRotation, bool, false, "Take rotation transformation into account.")rtabmap::Parametersprivate
RTABMAP_PARAM(GMS, WithScale, bool, false, "Take scale transformation into account.")rtabmap::Parametersprivate
RTABMAP_PARAM(GMS, ThresholdFactor, double, 6.0, "The higher, the less matches.")rtabmap::Parametersprivate
RTABMAP_PARAM(PyDescriptor, Dim, int, 4096, "Descriptor dimension.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, Strategy, int, 0, "ICP implementation: 0=Point Cloud Library, 1=libpointmatcher, 2=CCCoreLib (CloudCompare).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, MaxTranslation, float, 0.2, "Maximum ICP translation correction accepted (m).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, MaxRotation, float, 0.78, "Maximum ICP rotation correction accepted (rad).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, VoxelSize, float, 0.05, "Uniform sampling voxel size (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, DownsamplingStep, int, 1, "Downsampling step size (1=no sampling). This is done before uniform sampling.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, RangeMin, float, 0, "Minimum range filtering (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, RangeMax, float, 0, "Maximum range filtering (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, MaxCorrespondenceDistance, float, 0.05, "Max distance for point correspondences.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, ReciprocalCorrespondences, bool, true, "To be a valid correspondence, the corresponding point in target cloud to point in source cloud should be both their closest closest correspondence.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, Iterations, int, 30, "Max iterations.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, Epsilon, float, 0, "Set the transformation epsilon (maximum allowable difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, CorrespondenceRatio, float, 0.1, "Ratio of matching correspondences to accept the transform.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, Force4DoF, bool, false, uFormat("Limit ICP to x, y, z and yaw DoF. Available if %s > 0.", kIcpStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, FiltersEnabled, int, 3, "Flag to enable filters: 1=\"from\" cloud only, 2=\"to\" cloud only, 3=both.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlane, bool, false, "Use point to plane ICP.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlaneK, int, 5, "Number of neighbors to compute normals for point to plane if the cloud doesn't have already normals.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlaneRadius, float, 0.0, "Search radius to compute normals for point to plane if the cloud doesn't have already normals.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlaneGroundNormalsUp, float, 0.0, "Invert normals on ground if they are pointing down (useful for ring-like 3D LiDARs). 0 means disabled, 1 means only normals perfectly aligned with -z axis. This is only done with 3D scans.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlaneMinComplexity, float, 0.02, uFormat("Minimum structural complexity (0.0=low, 1.0=high) of the scan to do PointToPlane registration, otherwise PointToPoint registration is done instead and strategy from %s is used. This check is done only when %s=true.", kIcpPointToPlaneLowComplexityStrategy().c_str(), kIcpPointToPlane().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PointToPlaneLowComplexityStrategy, int, 1, uFormat("If structural complexity is below %s: set to 0 to so that the transform is automatically rejected, set to 1 to limit ICP correction in axes with most constraints (e.g., for a corridor-like environment, the resulting transform will be limited in y and yaw, x will taken from the guess), set to 2 to accept \"as is\" the transform computed by PointToPoint.", kIcpPointToPlaneMinComplexity().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, OutlierRatio, float, 0.85, uFormat("Outlier ratio used with %s>0. For libpointmatcher, this parameter set TrimmedDistOutlierFilter/ratio for convenience when configuration file is not set. For CCCoreLib, this parameter set the \"finalOverlapRatio\". The value should be between 0 and 1.", kIcpStrategy().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PMMatcherKnn, int, 1, "KDTreeMatcher/knn: number of nearest neighbors to consider it the reference. For convenience when configuration file is not set.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PMMatcherEpsilon, float, 0.0, "KDTreeMatcher/epsilon: approximation to use for the nearest-neighbor search. For convenience when configuration file is not set.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, PMMatcherIntensity, bool, false, uFormat("KDTreeMatcher: among nearest neighbors, keep only the one with the most similar intensity. This only work with %s>1.", kIcpPMMatcherKnn().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, CCSamplingLimit, unsigned int, 50000, "Maximum number of points per cloud (they are randomly resampled below this limit otherwise).")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, CCFilterOutFarthestPoints, bool, false, "If true, the algorithm will automatically ignore farthest points from the reference, for better convergence.")rtabmap::Parametersprivate
RTABMAP_PARAM(Icp, CCMaxFinalRMS, float, 0.2, "Maximum final RMS error.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, WinWidth, int, 15, "Window width.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, WinHeight, int, 3, "Window height.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, Iterations, int, 30, "Maximum iterations.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, MaxLevel, int, 5, "Maximum pyramid level.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, MinDisparity, float, 0.5, "Minimum disparity.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, MaxDisparity, float, 128.0, "Maximum disparity.")rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, OpticalFlow, bool, true, "Use optical flow to find stereo correspondences, otherwise a simple block matching approach is used.")rtabmap::Parametersprivate
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()))rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, Eps, double, 0.01, uFormat("[%s=true] Epsilon stop criterion.", kStereoOpticalFlow().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Stereo, DenseStrategy, int, 0, "0=cv::StereoBM, 1=cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, BlockSize, int, 15, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, MinDisparity, int, 0, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, NumDisparities, int, 128, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, PreFilterSize, int, 9, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, PreFilterCap, int, 31, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, UniquenessRatio, int, 15, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, TextureThreshold, int, 10, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, SpeckleWindowSize, int, 100, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, SpeckleRange, int, 4, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoBM, Disp12MaxDiff, int, -1, "See cv::StereoBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, BlockSize, int, 15, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, MinDisparity, int, 0, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, NumDisparities, int, 128, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, PreFilterCap, int, 31, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, UniquenessRatio, int, 20, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, SpeckleWindowSize, int, 100, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, SpeckleRange, int, 4, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, Disp12MaxDiff, int, 1, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, P1, int, 2, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, P2, int, 5, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(StereoSGBM, Mode, int, 0, "See cv::StereoSGBM")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, Sensor, int, 1, "Create occupancy grid from selected sensor: 0=laser scan, 1=depth image(s) or 2=both laser scan and depth image(s).")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, DepthDecimation, unsigned int, 4, uFormat("[%s=true] Decimation of the depth image before creating cloud.", kGridDepthDecimation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, RangeMin, float, 0.0, "Minimum range from sensor.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, RangeMax, float, 5.0, "Maximum range from sensor. 0=inf.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, FootprintLength, float, 0.0, "Footprint length used to filter points over the footprint of the robot.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, FootprintWidth, float, 0.0, "Footprint width used to filter points over the footprint of the robot. Footprint length should be set.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, FootprintHeight, float, 0.0, "Footprint height used to filter points over the footprint of the robot. Footprint length and width should be set.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, ScanDecimation, int, 1, uFormat("[%s=0 or 2] Decimation of the laser scan before creating cloud.", kGridSensor().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, CellSize, float, 0.05, "Resolution of the occupancy grid.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, PreVoxelFiltering, bool, true, uFormat("Input cloud is downsampled by voxel filter (voxel size is \"%s\") before doing segmentation of obstacles and ground.", kGridCellSize().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MapFrameProjection, bool, false, "Projection in map frame. On a 3D terrain and a fixed local camera transform (the cloud is created relative to ground), you may want to disable this to do the projection in robot frame instead.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, NormalsSegmentation, bool, true, "Segment ground from obstacles using point normals, otherwise a fast passthrough is used.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MaxObstacleHeight, float, 0.0, "Maximum obstacles height (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MinGroundHeight, float, 0.0, "Minimum ground height (0=disabled).")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MaxGroundHeight, float, 0.0, uFormat("Maximum ground height (0=disabled). Should be set if \"%s\" is false.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MaxGroundAngle, float, 45, uFormat("[%s=true] Maximum angle (degrees) between point's normal to ground's normal to label it as ground. Points with higher angle difference are considered as obstacles.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, NormalK, int, 20, uFormat("[%s=true] K neighbors to compute normals.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, ClusterRadius, float, 0.1, uFormat("[%s=true] Cluster maximum radius.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, MinClusterSize, int, 10, uFormat("[%s=true] Minimum cluster size to project the points.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, FlatObstacleDetected, bool, true, uFormat("[%s=true] Flat obstacles detected.", kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, 3D, bool, false, uFormat("A 3D occupancy grid is required if you want an OctoMap (3D ray tracing). Set to false if you want only a 2D map, the cloud will be projected on xy plane. A 2D map can be still generated if checked, but it requires more memory and time to generate it. Ignored if laser scan is 2D and \"%s\" is 0.", kGridSensor().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, GroundIsObstacle, bool, false, uFormat("[%s=true] Ground segmentation (%s) is ignored, all points are obstacles. Use this only if you want an OctoMap with ground identified as an obstacle (e.g., with an UAV).", kGrid3D().c_str(), kGridNormalsSegmentation().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, NoiseFilteringRadius, float, 0.0, "Noise filtering radius (0=disabled). Done after segmentation.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, NoiseFilteringMinNeighbors, int, 5, "Noise filtering minimum neighbors.")rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, Scan2dUnknownSpaceFilled, bool, false, uFormat("Unknown space filled. Only used with 2D laser scans. Use %s to set maximum range if laser scan max range is to set.", kGridRangeMax().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Grid, RayTracing, bool, false, uFormat("Ray tracing is done for each occupied cell, filling unknown space between the sensor and occupied cells. If %s=true, RTAB-Map should be built with OctoMap support, otherwise 3D ray tracing is ignored.", kGrid3D().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, UpdateError, float, 0.01, "Graph changed detection error (m). Update map only if poses in new optimized graph have moved more than this value.")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, FootprintRadius, float, 0.0, "Footprint radius (m) used to clear all obstacles under the graph.")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, MinSize, float, 0.0, "Minimum map size (m).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, Eroded, bool, false, "Erode obstacle cells.")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, MaxNodes, int, 0, "Maximum nodes assembled in the map starting from the last node (0=unlimited).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, AltitudeDelta, float, 0, "Assemble only nodes that have the same altitude of +-delta meters of the current pose (0=disabled). This is used to generate 2D occupancy grid based on the current altitude (e.g., multi-floor building).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, OccupancyThr, float, 0.5, "Occupancy threshold (value between 0 and 1).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, ProbHit, float, 0.7, "Probability of a hit (value between 0.5 and 1).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, ProbMiss, float, 0.4, "Probability of a miss (value between 0 and 0.5).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, ProbClampingMin, float, 0.1192, "Probability clamping minimum (value between 0 and 1).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, ProbClampingMax, float, 0.971, "Probability clamping maximum (value between 0 and 1).")rtabmap::Parametersprivate
RTABMAP_PARAM(GridGlobal, FloodFillDepth, unsigned int, 0, "Flood fill filter (0=disabled), used to remove empty cells outside the map. The flood fill is done at the specified depth (between 1 and 16) of the OctoMap.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, Dictionary, int, 0, "Dictionary to use: DICT_ARUCO_4X4_50=0, DICT_ARUCO_4X4_100=1, DICT_ARUCO_4X4_250=2, DICT_ARUCO_4X4_1000=3, DICT_ARUCO_5X5_50=4, DICT_ARUCO_5X5_100=5, DICT_ARUCO_5X5_250=6, DICT_ARUCO_5X5_1000=7, DICT_ARUCO_6X6_50=8, DICT_ARUCO_6X6_100=9, DICT_ARUCO_6X6_250=10, DICT_ARUCO_6X6_1000=11, DICT_ARUCO_7X7_50=12, DICT_ARUCO_7X7_100=13, DICT_ARUCO_7X7_250=14, DICT_ARUCO_7X7_1000=15, DICT_ARUCO_ORIGINAL = 16, DICT_APRILTAG_16h5=17, DICT_APRILTAG_25h9=18, DICT_APRILTAG_36h10=19, DICT_APRILTAG_36h11=20")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, Length, float, 0, "The length (m) of the markers' side. 0 means automatic marker length estimation using the depth image (the camera should look at the marker perpendicularly for initialization).")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, MaxDepthError, float, 0.01, uFormat("Maximum depth error between all corners of a marker when estimating the marker length (when %s is 0). The smaller it is, the more perpendicular the camera should be toward the marker to initialize the length.", kMarkerLength().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, VarianceLinear, float, 0.001, "Linear variance to set on marker detections.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, VarianceAngular, float, 0.01, "Angular variance to set on marker detections. Set to >=9999 to use only position (xyz) constraint in graph optimization.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, CornerRefinementMethod, int, 0, "Corner refinement method (0: None, 1: Subpixel, 2:contour, 3: AprilTag2). For OpenCV <3.3.0, this is \"doCornerRefinement\" parameter: set 0 for false and 1 for true.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, MaxRange, float, 0.0, "Maximum range in which markers will be detected. <=0 for unlimited range.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, MinRange, float, 0.0, "Miniminum range in which markers will be detected. <=0 for unlimited range.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, PriorsVarianceLinear, float, 0.001, "Linear variance to set on marker priors.")rtabmap::Parametersprivate
RTABMAP_PARAM(Marker, PriorsVarianceAngular, float, 0.001, "Angular variance to set on marker priors.")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, MadgwickGain, double, 0.1, "Gain of the filter. Higher values lead to faster convergence but more noise. Lower values lead to slower convergence but smoother signal, belongs in [0, 1].")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, MadgwickZeta, double, 0.0, "Gyro drift gain (approx. rad/s), belongs in [-1, 1].")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, ComplementaryGainAcc, double, 0.01, "Gain parameter for the complementary filter, belongs in [0, 1].")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, ComplementaryBiasAlpha, double, 0.01, "Bias estimation gain parameter, belongs in [0, 1].")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, ComplementaryDoBiasEstimation, bool, true, "Parameter whether to do bias estimation or not.")rtabmap::Parametersprivate
RTABMAP_PARAM(ImuFilter, ComplementaryDoAdpativeGain, bool, true, "Parameter whether to do adaptive gain or not.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Rtabmap, WorkingDirectory, "", "Working directory.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Mem, ImageCompressionFormat, ".jpg", "RGB image compression format. It should be \".jpg\" or \".png\".")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Kp, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Kp, DictionaryPath, "", "Path of the pre-computed dictionary")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Db, TargetVersion, "", "Target database version for backward compatibility purpose. Only Major and minor versions are used and should be set (e.g., 0.19 vs 0.20 or 1.0 vs 2.0). Patch version is ignored (e.g., 0.20.1 and 0.20.3 will generate a 0.20 database).")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(SuperPoint, ModelPath, "", "[Required] Path to pre-trained weights Torch file of SuperPoint (*.pt).")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(PyDetector, Path, "", "Path to python script file (see available ones in rtabmap/corelib/src/python/*). See the header to see where the script should be copied.")rtabmap::Parametersprivate
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, ...}.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(OdomORBSLAM, VocPath, "", "Path to ORB vocabulary (*.txt).")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(OdomOKVIS, ConfigPath, "", "Path of OKVIS config file.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(OdomVINS, ConfigPath, "", "Path of VINS config file.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(OdomOpenVINS, LeftMaskPath, "", "Mask for left image")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(OdomOpenVINS, RightMaskPath, "", "Mask for right image")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Vis, RoiRatios, "0.0 0.0 0.0 0.0", "Region of interest ratios [left, right, top, bottom].")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(PyMatcher, Path, "", "Path to python script file (see available ones in rtabmap/corelib/src/python/*). See the header to see where the script should be copied.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(PyMatcher, Model, "indoor", "For SuperGlue, set only \"indoor\" or \"outdoor\". For OANet, set path to one of the pth file (e.g., \"OANet/model/gl3d/sift-4000/model_best.pth\").")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(PyDescriptor, Path, "", "Path to python script file (see available ones in rtabmap/corelib/src/pydescriptor/*). See the header to see where the script should be used.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Icp, DebugExportFormat, "", "Export scans used for ICP in the specified format (a warning on terminal will be shown with the file paths used). Supported formats are \"pcd\", \"ply\" or \"vtk\". If logger level is debug, from and to scans will stamped, so previous files won't be overwritten.")rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Icp, PMConfig, "", uFormat("Configuration file (*.yaml) used by libpointmatcher. Note that data filters set for libpointmatcher are done after filtering done by rtabmap (i.e., %s, %s), so make sure to disable those in rtabmap if you want to use only those from libpointmatcher. Parameters %s, %s and %s are also ignored if configuration file is set.", kIcpVoxelSize().c_str(), kIcpDownsamplingStep().c_str(), kIcpIterations().c_str(), kIcpEpsilon().c_str(), kIcpMaxCorrespondenceDistance().c_str()).c_str())rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Grid, DepthRoiRatios, "0.0 0.0 0.0 0.0", uFormat("[%s>=1] Region of interest ratios [left, right, top, bottom].", kGridSensor().c_str()))rtabmap::Parametersprivate
RTABMAP_PARAM_STR(Marker, Priors, "", "World prior locations of the markers. The map will be transformed in marker's world frame when a tag is detected. Format is the marker's ID followed by its position (angles in rad), markers are separated by vertical line (\"id1 x y z roll pitch yaw|id2 x y z roll pitch yaw\"). Example: \"1 0 0 1 0 0 0|2 1 0 1 0 0 1.57\" (marker 2 is 1 meter forward than marker 1 with 90 deg yaw rotation).")rtabmap::Parametersprivate
serialize(const ParametersMap &parameters)rtabmap::Parametersstatic
showUsage()rtabmap::Parametersstatic
writeINI(const std::string &configFile, const ParametersMap &parameters)rtabmap::Parametersstatic
~Parameters()rtabmap::Parametersvirtual

rtabmap
Author(s): Mathieu Labbe
autogenerated on Thu Jul 25 2024 02:50:28