RTAB-Map 0.23.10
Real-Time Appearance-Based Mapping
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Parameter reference

Table of Contents

Every setting in RTAB-Map is a Group/Name string key with a string value, collected in a rtabmap::ParametersMap. The 615 parameters below are declared in Parameters.h; the same keys are used by rtabmap::Rtabmap and rtabmap::Odometry, by the applications, by the --Param Group/Name value command-line arguments of the tools and by the ROS wrappers, so a setting found here applies everywhere.

rtabmap::ParametersMap parameters;
parameters.insert(rtabmap::ParametersPair(rtabmap::Parameters::kMemSTMSize(), "20"));
rtabmap.init(parameters, "map.db");
rtabmap::Parameters::kMemSTMSize
static std::string kMemSTMSize()
Key of parameter Mem/STMSize : "Short-term memory size." Default value: 10 ( unsigned int ).
Definition Parameters.h:226
rtabmap::ParametersPair
std::pair< std::string, std::string > ParametersPair
A single parameter key/value pair, the entry type of ParametersMap.
Definition Parameters.h:46
rtabmap::ParametersMap
std::map< std::string, std::string > ParametersMap
Parameter keys mapped to their values, as used by every configurable class (see Parameters).
Definition Parameters.h:44

Each key links to its accessor on rtabmap::Parameters, which is also how the key is spelled in code (Parameters::kMemSTMSize() for Mem/STMSize). Defaults given with a condition depend on how RTAB-Map was built; call rtabmap::Parameters::getDefaultParameters() to read the values of the build in use.

Rtabmap

Top-level loop closure detection and map management.

Key Type Default Description
Rtabmap/PublishStats bool true Publishing statistics.
Rtabmap/PublishLastSignature bool true Publishing last signature.
Rtabmap/PublishPdf bool true Publishing pdf.
Rtabmap/PublishLikelihood bool true Publishing likelihood.
Rtabmap/PublishRAMUsage bool false Publishing RAM usage in statistics (may add a small overhead to get info from the system).
Rtabmap/ComputeRMSE bool true Compute root mean square error (RMSE) and publish it in statistics, if ground truth is provided.
Rtabmap/SaveWMState bool false Save working memory state after each update in statistics.
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/MemoryThr int 0 Maximum nodes in the Working Memory (0 means infinity). Similar to Rtabmap/TimeThr, 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.
Rtabmap/DetectionRate float 1 Detection rate (Hz). RTAB-Map will filter input images to satisfy this rate.
Rtabmap/ImageBufferSize unsigned int 1 Data buffer size (0 min inf).
Rtabmap/CreateIntermediateNodes bool false Create intermediate nodes between loop closure detection. Only used when Rtabmap/DetectionRate>0.
Rtabmap/WorkingDirectory string "" Working directory.
Rtabmap/MaxRetrieved unsigned int 2 Maximum nodes retrieved at the same time from LTM.
Rtabmap/MaxRepublished unsigned int 2 Maximum nodes republished when requesting missing data. When RGBD/Enabled=false, only loop closure data is republished, otherwise the closest nodes from the current localization are republished first. Ignored if Rtabmap/PublishLastSignature=false.
Rtabmap/StatisticLogsBufferedInRAM bool true Statistic logs buffered in RAM instead of written to hard drive after each iteration.
Rtabmap/StatisticLogged bool false Logging enabled.
Rtabmap/StatisticLoggedHeaders bool true Add column header description to log files.
Rtabmap/StartNewMapOnLoopClosure bool false Start a new map only if there is a global loop closure with a previous map.
Rtabmap/StartNewMapOnGoodSignature bool false Start a new map only if the first signature is not bad (i.e., has enough features, see Kp/BadSignRatio).
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/RectifyOnlyFeatures bool false If Rtabmap/ImagesAlreadyRectified 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.
Rtabmap/LoopThr float 0.11 Loop closing threshold.
Rtabmap/LoopRatio float 0 The loop closure hypothesis must be over LoopRatio x lastHypothesisValue.
Rtabmap/LoopGPS bool true Use GPS to filter likelihood (if GPS is recorded). Only locations inside the local radius RGBD/LocalRadius of the current GPS location are considered for loop closure detection.
Rtabmap/VirtualPlaceLikelihoodRatio int 0 Likelihood ratio for virtual place (for no loop closure hypothesis): 0=Mean / StdDev, 1=StdDev / (Max-Mean)

Mem

Memory management: what is kept in STM/WM, what is transferred to LTM.

Key Type Default Description
Mem/RehearsalSimilarity float 0.6 Rehearsal similarity.
Mem/ImageKept bool false Keep raw images in RAM.
Mem/BinDataKept bool true Keep binary data in db.
Mem/RawDescriptorsKept bool true Raw descriptors kept in memory.
Mem/LoadVisualLocalFeaturesOnInit bool true Load all local visual features (keypoints, descriptors and 3D points) in RAM when loading an existing database. This can add significant time to initialize the memory but the features will be already loaded before computing loop closure transforms. If false, the features are loaded on-demand from the database when a loop closure transformation should be estimated.
Mem/MapLabelsAdded bool true Create map labels. The first node of a map will be labeled as "map#" where # is the map ID.
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).
Mem/NotLinkedNodesKept bool true Keep not linked nodes in db (rehearsed nodes and deleted nodes).
Mem/IntermediateNodeDataKept bool false Keep intermediate node data in db.
Mem/ImageCompressionFormat string .jpg RGB image compression format. It should be ".jpg" or ".png".
Mem/DepthCompressionFormat string .rvl Depth image compression format for 16UC1 depth type. It should be ".png" or ".rvl". If depth type is 32FC1, ".png" is used.
Mem/STMSize unsigned int 10 Short-term memory size.
Mem/IncrementalMemory bool true SLAM mode, otherwise it is Localization mode.
Mem/LocalizationReadOnly bool false In localization mode, open the database in read-only mode (ignored if Mem/IncrementalMemory=true). Currrenty incompatible with memory management (Rtabmap/LoopThr and Rtabmap/MemoryThr cannot be used) and if there are disjoint sessions in working memory. Last localization pose won't be saved back in the database at the end of the session, so the robot will always restart to original last localization pose, unless RGBD/StartAtOrigin is used or an external initial pose is provided on initialization.
Mem/LocalizationDataSaved bool false Save localization data during localization session (when Mem/IncrementalMemory=false). When enabled, the database will then also grow in localization mode. This mode would be used only for debugging purpose.
Mem/ReduceGraph bool false Reduce graph. Merge nodes when loop closures are added (ignoring those with user data). Note that this approach assumes that 100%% of the loop closures accepted are good, so it is highly recommended to enable RGBD/OptimizeMaxError at the same time.
Mem/RecentWmRatio float 0.2 Ratio of locations after the last loop closure in WM that cannot be transferred.
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.
Mem/RehearsalIdUpdatedToNewOne bool false On merge, update to new id. When false, no copy. Keep this disable if Rtabmap/CreateIntermediateNodes=true.
Mem/RehearsalWeightIgnoredWhileMoving bool false When the robot is moving, weights are not updated on rehearsal.
Mem/GenerateIds bool true True=Generate location IDs, False=use input image IDs.
Mem/BadSignaturesIgnored bool false Bad signatures are ignored.
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.
Mem/DepthAsMask bool true Use depth image as mask when extracting features for vocabulary.
Mem/DepthMaskFloorThr float 0.0 Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled. Ignored if Mem/DepthAsMask is false.
Mem/StereoFromMotion bool false Triangulate features without depth using stereo from motion (odometry). It would be ignored if Mem/DepthAsMask is true and the feature detector used supports masking.
Mem/ImagePreDecimation unsigned int 1 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 Mem/DepthAsMask is true and if depth is smaller than decimated RGB, depth may be interpolated to match RGB size for feature detection.
Mem/ImagePostDecimation unsigned int 1 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 Mem/ImagePreDecimation, data already decimated is saved (no need to re-decimate the image).
Mem/CompressionParallelized bool true Compression of sensor data is multi-threaded.
Mem/LaserScanDownsampleStepSize int 1 If > 1, downsample the laser scans when creating a signature.
Mem/LaserScanVoxelSize float 0.0 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 Mem/LaserScanNormalK or Mem/LaserScanNormalRadius parameters.
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.
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.
Mem/UseOdomFeatures bool true Use odometry features instead of regenerating them.
Mem/UseOdomGravity bool false 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 Optimizer/GravitySigma is not zero.
Mem/CovOffDiagIgnored bool true Ignore off diagonal values of the covariance matrix.
Mem/GlobalDescriptorStrategy int 0 Extract global descriptor from sensor data. 0=disabled, 1=PyDescriptor
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.

Kp

Bag-of-words dictionary used for global loop closure detection.

Key Type Default Description
Kp/NNStrategy int 1 kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4
Kp/IncrementalDictionary bool true
Kp/IncrementalFlann bool true 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 Kp/FlannRebalancingFactor in size).
Kp/FlannRebalancingFactor float 2.0 Factor used when rebuilding the incremental FLANN index (see Kp/IncrementalFlann). Set <=1 to disable.
Kp/ByteToFloat bool false For Kp/NNStrategy=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.
Kp/MaxDepth float 0 Filter extracted keypoints by depth (0=inf).
Kp/MinDepth float 0 Filter extracted keypoints by depth.
Kp/MaxFeatures int 500 Maximum features extracted from the images (0 means not bounded, <0 means no extraction).
Kp/SSC bool false If true, SSC (Suppression via Square Covering) is applied to limit keypoints.
Kp/BadSignRatio float 0.5 Bad signature ratio. If Kp/MaxFeatures=0, the ratio is computed from the average number of words per signature (less than Ratio x AverageWordsPerImage = bad).
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.)
Kp/DetectorStrategy int 8 with CV_MAJOR_VERSION &gt; 2 && !defined(HAVE_OPENCV_XFEATURES2D)
6 otherwise 		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 16=SuperPoint-Rpautrat
Kp/TfIdfLikelihoodUsed bool true Use of the td-idf strategy to compute the likelihood.
Kp/Parallelized bool true If the dictionary update and signature creation were parallelized.
Kp/RoiRatios string 0.0 0.0 0.0 0.0 Region of interest ratios [left, right, top, bottom].
Kp/DictionaryPath string "" Path of the pre-computed dictionary
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).
Kp/FlannIndexSaved bool false Save FLANN index during localization session (when Mem/IncrementalMemory=false). The FLANN index will be saved to database after the first time localization mode is used, then on next sessions, the index is reloaded from the database instead of being rebuilt again. This can save significant loading time when the visual word dictionary is big (>1M words). Note that if the dictionary is modified (parameters or data), the index will be rebuilt and saved again on the next session. Ignored on initialization if Mem/InitWMWithAllNodes is enabled.
Kp/SerializeWithChecksum bool true On serialization of the FLANN index, compute checksum of the data used by the FLANN index. This adds a slight overhead on serialization/deserialization to make sure that the dictionary data correspond to same data used when the index was built.
Kp/SubPixWinSize int 3 See cv::cornerSubPix().
Kp/SubPixIterations int 0 See cv::cornerSubPix(). 0 disables sub pixel refining.
Kp/SubPixEps double 0.02 See cv::cornerSubPix().
Kp/GridRows int 1 Number of rows of the grid used to extract uniformly "@ref param_KpMaxFeatures "Kp/MaxFeatures" / grid cells" features from each cell.
Kp/GridCols int 1 Number of columns of the grid used to extract uniformly "@ref param_KpMaxFeatures "Kp/MaxFeatures" / grid cells" features from each cell.

DbSqlite3

Key Type Default Description
DbSqlite3/InMemory bool false Using database in the memory instead of a file on the hard disk.
DbSqlite3/CacheSize unsigned int 10000 PRAGMA cache_size: number of database pages kept in SQLite's page cache (approx. cacheSize * page_size bytes, often ~4 KiB per page). Larger values reduce disk I/O when the working set fits in RAM. SQLite built-in default is typically 2000 pages.
DbSqlite3/JournalMode int 3 PRAGMA journal_mode: rollback journal storage. See sqlite.org/pragma.html::pragma_journal_mode for more details. 0=DELETE (SQLite default): journal file deleted after each commit. 1=TRUNCATE: journal truncated to zero length. 2=PERSIST: journal file kept, header zeroed after commit. 3=MEMORY: journal in RAM only; faster, weaker crash safety. 4=OFF: no journal; fastest, risk of corruption on crash.
DbSqlite3/Synchronous int 0 PRAGMA synchronous: how aggressively SQLite syncs the database to disk. See sqlite.org/pragma.html::pragma_synchronous for more details. 0=OFF: no wait for persistent storage; fastest, corruption possible on power loss. 1=NORMAL: sync at critical moments (common SQLite default with WAL). 2=FULL (SQLite safest default): sync after every commit; slowest.
DbSqlite3/TempStore int 2 PRAGMA temp_store: where SQLite stores temporary tables and indices. See sqlite.org/pragma.html::pragma_temp_store for more details. 0=DEFAULT: SQLite compile-time default (often on-disk temp files). 1=FILE: temporary files in the system temp directory. 2=MEMORY: temporary data in RAM when possible.

Db

Database (long-term memory) storage.

Key Type Default Description
Db/TargetVersion string "" 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).

SURF

Key Type Default Description
SURF/Extended bool false Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).
SURF/HessianThreshold float 500 Threshold for hessian keypoint detector used in SURF.
SURF/Octaves int 4 Number of pyramid octaves the keypoint detector will use.
SURF/OctaveLayers int 2 Number of octave layers within each octave.
SURF/Upright bool false Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).
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.
SURF/GpuKeypointsRatio float 0.01 Used with SURF GPU.

SIFT

Key Type Default Description
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. Not used by CudaSift, the number of octaves is still computed automatically.
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. Not used by CudaSift (see SIFT/GaussianThreshold instead).
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).
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.
SIFT/PreciseUpscale bool false Whether to enable precise upscaling in the scale pyramid (OpenCV >= 4.8).
SIFT/RootSIFT bool false Apply RootSIFT normalization of the descriptors.
SIFT/Gpu bool false CudaSift: Use GPU version of SIFT. This option is enabled only if RTAB-Map is built with CudaSift dependency and GPUs are detected.
SIFT/GaussianThreshold float 2.0 CudaSift: Threshold on difference of Gaussians for feature pruning. The higher the threshold, the less features with low response/hessian are produced by the detector.
SIFT/MaxGaussianThreshold float 0.0 CudaSift: Maximum threshold on difference of Gaussians for feature pruning (ignored if smaller or equal than SIFT/GaussianThreshold). The lower the threshold, the less features with high response/hessian are produced by the detector.
SIFT/Upscale bool false CudaSift: Whether to enable upscaling.

BRIEF

Key Type Default Description
BRIEF/Bytes int 32 Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.

FAST

Key Type Default Description
FAST/Threshold int 20 Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.
FAST/NonmaxSuppression bool true If true, non-maximum suppression is applied to detected corners (keypoints).
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.
FAST/GpuKeypointsRatio double 0.05 Used with FAST GPU.
FAST/MinThreshold int 7 Minimum threshold. Used only when FAST/GridRows and FAST/GridCols are set.
FAST/MaxThreshold int 200 Maximum threshold. Used only when FAST/GridRows and FAST/GridCols are set.
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.
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.
FAST/CV int 0 Enable FastCV implementation if non-zero (and RTAB-Map is built with FastCV support). Values should be 9 and 10.

GFTT

Key Type Default Description
GFTT/QualityLevel double 0.001
GFTT/MinDistance double 7
GFTT/BlockSize int 3
GFTT/UseHarrisDetector bool false
GFTT/K double 0.04
GFTT/Gpu bool false GPU-GFTT: Use GPU version of GFTT. This option is enabled only if OpenCV>=3 is built with CUDA and GPUs are detected.

ORB

Key Type Default Description
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.
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).
ORB/EdgeThreshold int 19 This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
ORB/FirstLevel int 0 It should be 0 in the current implementation.
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).
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.
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.
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.

FREAK

Key Type Default Description
FREAK/OrientationNormalized bool true Enable orientation normalization.
FREAK/ScaleNormalized bool true Enable scale normalization.
FREAK/PatternScale float 22 Scaling of the description pattern.
FREAK/NOctaves int 4 Number of octaves covered by the detected keypoints.

BRISK

Key Type Default Description
BRISK/Thresh int 30 FAST/AGAST detection threshold score.
BRISK/Octaves int 3 Detection octaves. Use 0 to do single scale.
BRISK/PatternScale float 1 Apply this scale to the pattern used for sampling the neighbourhood of a keypoint.

KAZE

Key Type Default Description
KAZE/Extended bool false Set to enable extraction of extended (128-byte) descriptor.
KAZE/Upright bool false Set to enable use of upright descriptors (non rotation-invariant).
KAZE/Threshold float 0.001 Detector response threshold to accept keypoint.
KAZE/NOctaves int 4 Maximum octave evolution of the image.
KAZE/NOctaveLayers int 4 Default number of sublevels per scale level.
KAZE/Diffusivity int 1 Diffusivity type: 0=DIFF_PM_G1, 1=DIFF_PM_G2, 2=DIFF_WEICKERT or 3=DIFF_CHARBONNIER.

SuperPoint

Key Type Default Description
SuperPoint/ModelPath string "" [Required] Path to pre-trained weights Torch file of SuperPoint (*.pt).
SuperPoint/Threshold float 0.010 Detector response threshold to accept keypoint.
SuperPoint/NMS bool true If true, non-maximum suppression is applied to detected keypoints.
SuperPoint/NMSRadius int 4 [SuperPoint/NMS=true] Minimum distance (pixels) between keypoints.
SuperPoint/Cuda bool true Use Cuda device for Torch, otherwise CPU device is used by default.

SuperPointRpautrat

Key Type Default Description
SuperPointRpautrat/WeightsPath string "" [Required] SuperPoint weights file (*.pth).
SuperPointRpautrat/ModelPath string "" [Required] SuperPoint python model file (superpoint_pytorch.py).
SuperPointRpautrat/Threshold float 0.005 Detector response threshold to accept keypoint.
SuperPointRpautrat/NMS bool true If true, non-maximum suppression is applied to detected keypoints.
SuperPointRpautrat/NMSRadius int 4 [SuperPointRpautrat/NMS=true] Minimum distance (pixels) between keypoints.
SuperPointRpautrat/Cuda bool true Use Cuda device for Torch, otherwise CPU device is used by default.

PyDetector

Key Type Default Description
PyDetector/Path string "" Path to python script file (see available ones in rtabmap/corelib/src/python/*). See the header to see where the script should be copied.
PyDetector/Cuda bool true Use cuda.

Bayes

Bayes filter used for loop closure hypotheses.

Key Type Default Description
Bayes/VirtualPlacePriorThr float 0.9 Virtual place prior. Considering that we are at a new place, this is the prior probability to move again to a new place (unvisited location). The prior probability to move to a previously visited location is 1 - VirtualPlacePriorThr (split equally against all previously visited locations).
Bayes/PredictionLC string 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, ...}. Considering we are at a previously visited location, the first value is the probability to move to a new place (unvisited location), the second value is the probability to stay at the same location, the third value is the probability to move to a neighbor or loop closure at the first depth level, the fourth value is the probability to move to a neighbor or loop closure at the second depth level, etc. If the sum of the values is not 1, the difference is normalized against all remaining visited locations. Normally, the sum of these values should be 1.
Bayes/FullPredictionUpdate bool false Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).

VhEp

Key Type Default Description
VhEp/Enabled bool false Verify visual loop closure hypothesis by computing a fundamental matrix. This is done prior to transformation computation when RGBD/Enabled is enabled.
VhEp/MatchCountMin int 8 Minimum of matching visual words pairs to accept the loop hypothesis.
VhEp/RansacParam1 float 3 Fundamental matrix (see cvFindFundamentalMat()): Max distance (in pixels) from the epipolar line for a point to be inlier.
VhEp/RansacParam2 float 0.99 Fundamental matrix (see cvFindFundamentalMat()): Performance of RANSAC.

RGBD

Metric SLAM: graph, proximity detection and localization.

Key Type Default Description
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.
RGBD/LinearUpdate float 0.1 Minimum linear displacement (m) to update the map. Rehearsal is done prior to this, so weights are still updated. To update the map when not moving, both RGBD/LinearUpdate and RGBD/AngularUpdate should be set to 0.
RGBD/AngularUpdate float 0.1 Minimum angular displacement (rad) to update the map. Rehearsal is done prior to this, so weights are still updated. To update the map when not moving, both RGBD/LinearUpdate and RGBD/AngularUpdate should be set to 0.
RGBD/LinearSpeedUpdate float 0.0 Maximum linear speed (m/s) to update the map (0 means not limit).
RGBD/AngularSpeedUpdate float 0.0 Maximum angular speed (rad/s) to update the map (0 means not limit).
RGBD/AggressiveLoopThr float 0.05 Loop closure threshold used (overriding Rtabmap/LoopThr) 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 (RGBD/MaxOdomCacheSize). In all cases, the goal is to aggressively loop on a previous map in the database. Only used when RGBD/Enabled is enabled. Set 1 to disable.
RGBD/NewMapOdomChangeDistance float 0 A new map is created if a change of odometry translation greater than X m is detected (0 m = disabled).
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).
RGBD/OptimizeMaxError float 3.0 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. If used with Optimizer/Robust, the disabled loop closure links will be removed.
RGBD/OptimizeMaxErrorRepairRadius float 0.0 If two consecutive loop closures are rejected by RGBD/OptimizeMaxError on the same old loop closure link, we will remove that old link, and other old links under that radius if necessary, until optimization is accepted. When optimization is accepted, the old loop closure links are removed from the graph. This feature is useful to reject bad loop closures that were accepted previously. Set to 0 to disable this feature.
RGBD/MaxLoopClosureDistance float 0.0 Reject loop closures/localizations if the distance from the map is over this distance (0=disabled).
RGBD/ForceOdom3DoF bool true Force odometry pose to be 3DoF if Reg/Force3DoF=true.
RGBD/StartAtOrigin bool false 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 (Mem/IncrementalMemory=false).
RGBD/GoalReachedRadius float 0.5 Goal reached radius (m).
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.
RGBD/PlanLinearVelocity float 0 Linear velocity (m/sec) used to compute path weights.
RGBD/PlanAngularVelocity float 0 Angular velocity (rad/sec) used to compute path weights.
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:#".
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).
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.
RGBD/LocalImmunizationRatio float 0.25 Ratio of working memory for which local nodes are immunized from transfer.
RGBD/ScanMatchingIdsSavedInLinks bool true Save scan matching IDs from one-to-many proximity detection in link's user data.
RGBD/NeighborLinkRefining bool false 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 (Reg/Strategy).
RGBD/LoopClosureIdentityGuess bool false Use Identity matrix as guess when computing loop closure transform, otherwise no guess is used, thus assuming that registration strategy selected (Reg/Strategy) can deal with transformation estimation without guess.
RGBD/LoopClosureReextractFeatures bool false Extract features even if there are some already in the nodes. Raw features are not saved in database.
RGBD/LocalBundleOnLoopClosure bool false Do local bundle adjustment with neighborhood of the loop closure.
RGBD/InvertedReg bool false On loop closure, do registration from the target to reference instead of reference to target.
RGBD/CreateOccupancyGrid bool false Create local occupancy grid maps. See "Grid" group for parameters.
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.
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.
RGBD/MaxOdomCacheSize int 10 Maximum odometry cache size. Used only in localization mode (when Mem/IncrementalMemory=false). This is used to get smoother localizations and to verify localization transforms (when RGBD/OptimizeMaxError!=0) to make sure we don't teleport to a location very similar to one we previously localized on. Set 0 to disable caching.
RGBD/LocalizationSmoothing bool true Adjust localization constraints based on optimized odometry cache poses (when RGBD/MaxOdomCacheSize>0).
RGBD/LocalizationPriorError double 0.001 The corresponding variance (error x error) set to priors of the map's poses during localization (when RGBD/MaxOdomCacheSize>0).
RGBD/LocalizationSecondTryWithoutProximityLinks bool true When localization is rejected by graph optimization validation, try a second time without proximity links if landmark or loop closure links are also present in odometry cache (see RGBD/MaxOdomCacheSize). If it succeeds, the proximity links are removed. This assumes that global loop closure and landmark links are more accurate than proximity links.
RGBD/ProximityByTime bool false Detection over all locations in STM.
RGBD/ProximityBySpace bool true Detection over locations (in Working Memory) near in space.
RGBD/ProximityMaxGraphDepth int 50 Maximum depth from the current/last loop closure location and the local loop closure hypotheses. Set 0 to ignore.
RGBD/ProximityMaxPaths int 3 Maximum paths compared (from the most recent) for proximity detection. 0 means no limit.
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.
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).
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.
RGBD/ProximityAngle float 45 Maximum angle (degrees) for one-to-one proximity detection.
RGBD/ProximityOdomGuess bool false Use odometry as motion guess for one-to-one proximity detection.
RGBD/ProximityGlobalScanMap bool false 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 (Mem/IncrementalMemory=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.
RGBD/ProximityMergedScanCovFactor double 100.0 Covariance factor for one-to-many proximity detection (when RGBD/ProximityPathMaxNeighbors>0 and scans are used).

Optimizer

Graph optimization back-end.

Key Type Default Description
Optimizer/Strategy int 2 with defined(RTABMAP_GTSAM)
1 with defined(RTABMAP_G2O)
3 with defined(RTABMAP_CERES)
0 otherwise 		Graph optimization strategy: 0=TORO, 1=g2o, 2=GTSAM and 3=Ceres.
Optimizer/Iterations int 20 with defined(RTABMAP_GTSAM) or defined(RTABMAP_G2O) or defined(RTABMAP_CERES)
100 otherwise 		Optimization iterations.
Optimizer/Epsilon double 0.00001 with defined(RTABMAP_GTSAM)
0.0 with defined(RTABMAP_G2O)
0.000001 with defined(RTABMAP_CERES)
0.00001 otherwise 		Stop optimizing when the error improvement is less than this value.
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.
Optimizer/Robust bool false Robust graph optimization using Vertigo (only work for g2o and GTSAM optimization strategies).
Optimizer/PriorsIgnored bool true Ignore prior constraints (global pose or GPS) while optimizing. Currently only g2o and gtsam optimization supports this.
Optimizer/LandmarksIgnored bool false Ignore landmark constraints while optimizing. Currently only g2o and gtsam optimization supports this.
Optimizer/GravitySigma float 0.3 with defined(RTABMAP_G2O) \|\| defined(RTABMAP_GTSAM)
0.0 otherwise 		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 Optimizer/Strategy).
Optimizer/Baseline double 0.075 When doing bundle adjustment with RGB-D data (mono camera + depth), set a fake baseline (m) so the BA backend treats depth as stereo disparity. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Set to 0 to keep the problem mono (depth observations are ignored). For real stereo data the baseline in the calibration (Tx) is used directly.
Optimizer/PixelVariance double 1.0 Pixel variance used on the u/v axes of every bundle adjustment reprojection edge. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Should approximate the squared 1-sigma keypoint localization error in pixels. Set higher (e.g. 4-9) if features are noisy (low texture, motion blur, low light, or large detector scale). Set lower (e.g. 0.01-0.1) if features are sub-pixel refined (Lucas-Kanade tracking, parabolic peak interpolation). Intuition: the lower the pixel variance, the more the optimizer trusts the keypoint positions.
Optimizer/DisparityVariance double 1.0 Disparity variance used on the disparity axis (u - u_right) of stereo / RGB-D bundle adjustment edges. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Defaults to the same value as PixelVariance for backward compatibility. Set higher (e.g. 2-4) if your depth source is noisier than your feature detector's u/v precision (typical for stereo block matchers / SGM at long range). Set lower (e.g. 0.01-0.1) if your depth source is more accurate than the u/v detector (typical for ToF / LiDAR-fused depth where range is measured directly rather than triangulated). Intuition: the lower the disparity variance, the more the optimizer trusts the depth measurements. Geometric note: wider baseline and/or higher image resolution improve a block matcher's effective disparity precision (larger disparity magnitudes and finer sub-pixel refinement), so wide-baseline high-resolution stereo pairs can usually afford a lower disparity variance (e.g. 0.1-0.5); narrow-baseline low-resolution pairs should keep it higher (e.g. 1-4).
Optimizer/RobustKernelDelta double 8 Robust kernel delta used for bundle adjustment (0 means don't use robust kernel). Applies to all BA-capable backends (g2o, GTSAM, Ceres). Observations with chi2 over this threshold will be ignored in the second optimization pass.

g2o

Key Type Default Description
g2o/Solver int 3 with defined(RTABMAP_ORB_SLAM)
0 otherwise 		0=csparse 1=pcg 2=cholmod 3=Eigen
g2o/Optimizer int 0 0=Levenberg 1=GaussNewton

GTSAM

Key Type Default Description
GTSAM/Optimizer int 1 0=Levenberg 1=GaussNewton 2=Dogleg
GTSAM/Incremental bool false Do graph optimization incrementally (iSAM2) to increase optimization speed on loop closures. Note that only GaussNewton and Dogleg optimization algorithms are supported (GTSAM/Optimizer) in this mode.
GTSAM/IncRelinearizeThreshold double 0.01 Only relinearize variables whose linear delta magnitude is greater than this threshold. See GTSAM::ISAM2 doc for more info.
GTSAM/IncRelinearizeSkip int 1 Only relinearize any variables every X calls to ISAM2::update(). See GTSAM::ISAM2 doc for more info.

Odom

Odometry front-end shared settings.

Key Type Default Description
Odom/Strategy int 0 0=Frame-to-Map (F2M) 1=Frame-to-Frame (F2F) 2=Fovis 3=viso2 4=DVO-SLAM 5=ORB_SLAM 6=OKVIS 7=LOAM 8=MSCKF_VIO 9=VINS-Fusion 10=OpenVINS 11=FLOAM 12=Open3D 13=cuVSLAM 14=LIO-SAM
Odom/ResetCountdown int 0 Automatically reset odometry after X consecutive images where odometry cannot be computed (a value of 0 disables auto-reset). When a reset occurs, odometry resumes from the last successfully computed pose with large covariance to trigger a new map. If external odometry is used, it will also be reset based on the motion estimated relative to the last computed pose but no large covariance will be received, so that a new map won't be triggered.
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)).
Odom/FillInfoData bool true Fill info with data (inliers/outliers features).
Odom/ImageBufferSize unsigned int 1 Data buffer size (0 min inf).
Odom/FilteringStrategy int 0 0=No filtering 1=Kalman filtering 2=Particle filtering. This filter is used to smooth the odometry output.
Odom/ParticleSize unsigned int 400 Number of particles of the filter.
Odom/ParticleNoiseT float 0.002 Noise (m) of translation components (x,y,z).
Odom/ParticleLambdaT float 100 Lambda of translation components (x,y,z).
Odom/ParticleNoiseR float 0.002 Noise (rad) of rotational components (roll,pitch,yaw).
Odom/ParticleLambdaR float 100 Lambda of rotational components (roll,pitch,yaw).
Odom/KalmanProcessNoise float 0.001 Process noise covariance value.
Odom/KalmanMeasurementNoise float 0.01 Process measurement covariance value.
Odom/GuessMotion bool true Guess next transformation from the last motion computed.
Odom/GuessSmoothingDelay float 0 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 Odom/FilteringStrategy is set or the delay is below the odometry rate.
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.
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.
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.
Odom/ImageDecimation unsigned int 1 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 Vis/DepthAsMask is true and if depth is smaller than decimated RGB, depth may be interpolated to match RGB size for feature detection.
Odom/AlignWithGround bool false Align odometry with the ground on initialization.
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).

OdomF2M

Key Type Default Description
OdomF2M/MaxSize int 2000 [Visual] Local map size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words.
OdomF2M/MaxNewFeatures int 0 [Visual] Maximum features (sorted by keypoint response) added to local map from a new key-frame. 0 means no limit.
OdomF2M/InitDepthFactor float 0.05 [Visual] Depth factor used to initialize depth of features without depth. Depth = Factor * fx.
OdomF2M/FloorThreshold float 0.0 [Visual] Only track features in 3D feature map that are over this threshold (height in base frame). Can be useful to ignore reflections on the floor. 0 means disabled.
OdomF2M/ScanMaxSize int 2000 [Geometry] Maximum local scan map size.
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.
OdomF2M/ScanSubtractAngle float 45 [Geometry] Max angle (degrees) used to filter points of a new added scan to local map (when OdomF2M/ScanSubtractRadius>0). 0 means any angle.
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.
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.
OdomF2M/BundleAdjustment int 1 with defined(RTABMAP_G2O) \|\| defined(RTABMAP_ORB_SLAM)
0 otherwise 		Local bundle adjustment. Value matches the Optimizer/Strategy parameter: 0=disabled (TORO is not BA-capable), 1=g2o, 2=GTSAM, 3=Ceres, 4=cvsba.
OdomF2M/BundleAdjustmentMaxFrames int 10 Maximum frames used for bundle adjustment (0=inf or all current frames in the local map).
OdomF2M/BundleAdjustmentMinMotion float 0.0 To create a new keyframe with bundle adjustment, a minimum motion (in pixels) can be required. The motion is computed by the average distance between inliers of the previous keyframe and new frame.
OdomF2M/BundleAdjustmentMaxKeyFramesPerFeature int 0 Maximum keyframes per feature for bundle adjustment. 0 means not limit.
OdomF2M/BundleUpdateFeatureMapOnAllFrames bool false Update 3D local feature map on every frame with bundle adjustment. Recommended if Vis/DepthAsMask=false and Mem/UseOdomFeatures=true so that features without depth are better triangulated on every frame (not only on keyframes). If disabled, the feature map is updated only when a new keyframe is added (legacy approach).

OdomMono

Key Type Default Description
OdomMono/InitMinFlow float 100 Minimum optical flow required for the initialization step.
OdomMono/InitMinTranslation float 0.1 Minimum translation required for the initialization step.
OdomMono/MinTranslation float 0.02 Minimum translation to add new points to local map. On initialization, translation x 5 is used as the minimum.
OdomMono/MaxVariance float 0.01 Maximum variance to add new points to local map.

OdomFovis

Key Type Default Description
OdomFovis/FeatureWindowSize int 9 The size of the n x n image patch surrounding each feature, used for keypoint matching.
OdomFovis/MaxPyramidLevel int 3 The maximum Gaussian pyramid level to process the image at. Pyramid level 1 corresponds to the original image.
OdomFovis/MinPyramidLevel int 0 The minimum pyramid level.
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.
OdomFovis/FastThreshold int 20 FAST threshold.
OdomFovis/UseAdaptiveThreshold bool true Use FAST adaptive threshold.
OdomFovis/FastThresholdAdaptiveGain double 0.005 FAST threshold adaptive gain.
OdomFovis/UseHomographyInitialization bool true Use homography initialization.
OdomFovis/UseBucketing bool true
OdomFovis/BucketWidth int 80
OdomFovis/BucketHeight int 80
OdomFovis/MaxKeypointsPerBucket int 25
OdomFovis/UseImageNormalization bool false
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.
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.
OdomFovis/MinFeaturesForEstimate int 20 Minimum number of features in the inlier set for the motion estimate to be considered valid.
OdomFovis/MaxMeanReprojectionError double 10.0 Maximum mean reprojection error over the inlier feature matches for the motion estimate to be considered valid.
OdomFovis/UseSubpixelRefinement bool true Specifies whether or not to refine feature matches to subpixel resolution.
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.
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.
OdomFovis/StereoRequireMutualMatch bool true
OdomFovis/StereoMaxDistEpipolarLine double 1.5
OdomFovis/StereoMaxRefinementDisplacement double 1.0
OdomFovis/StereoMaxDisparity int 128

OdomViso2

Key Type Default Description
OdomViso2/RansacIters int 200 Number of RANSAC iterations.
OdomViso2/InlierThreshold double 2.0 Fundamental matrix inlier threshold.
OdomViso2/Reweighting bool true Lower border weights (more robust to calibration errors).
OdomViso2/MatchNmsN int 3 Non-max-suppression: min. distance between maxima (in pixels).
OdomViso2/MatchNmsTau int 50 Non-max-suppression: interest point peakiness threshold.
OdomViso2/MatchBinsize int 50 Matching bin width/height (affects efficiency only).
OdomViso2/MatchRadius int 200 Matching radius (du/dv in pixels).
OdomViso2/MatchDispTolerance int 2 Disparity tolerance for stereo matches (in pixels).
OdomViso2/MatchOutlierDispTolerance int 5 Outlier removal: disparity tolerance (in pixels).
OdomViso2/MatchOutlierFlowTolerance int 5 Outlier removal: flow tolerance (in pixels).
OdomViso2/MatchMultiStage bool true Multistage matching (denser and faster).
OdomViso2/MatchHalfResolution bool true Match at half resolution, refine at full resolution.
OdomViso2/MatchRefinement int 1 Refinement (0=none,1=pixel,2=subpixel).
OdomViso2/BucketMaxFeatures int 2 Maximal number of features per bucket.
OdomViso2/BucketWidth double 50 Width of bucket.
OdomViso2/BucketHeight double 50 Height of bucket.

OdomORBSLAM

Key Type Default Description
OdomORBSLAM/VocPath string "" Path to ORB vocabulary (*.txt).
OdomORBSLAM/Bf double 0.076 Fake IR projector baseline (m) used only when stereo is not used.
OdomORBSLAM/ThDepth double 40.0 Close/Far threshold. Baseline times.
OdomORBSLAM/Fps float 0.0 Camera FPS (0 to estimate from input data).
OdomORBSLAM/MaxFeatures int 1000 Maximum ORB features extracted per frame.
OdomORBSLAM/MapSize int 3000 Maximum size of the feature map (0 means infinite). Only supported with ORB_SLAM2.
OdomORBSLAM/Inertial bool false Enable IMU. Only supported with ORB_SLAM3.
OdomORBSLAM/GyroNoise double 0.01 IMU gyroscope "white noise".
OdomORBSLAM/AccNoise double 0.1 IMU accelerometer "white noise".
OdomORBSLAM/GyroWalk double 0.000001 IMU gyroscope "random walk".
OdomORBSLAM/AccWalk double 0.0001 IMU accelerometer "random walk".
OdomORBSLAM/SamplingRate double 0 IMU sampling rate (0 to estimate from input data).

OdomOKVIS

Key Type Default Description
OdomOKVIS/ConfigPath string "" Path of OKVIS config file.

OdomLOAM

Key Type Default Description
OdomLOAM/Sensor int 2 Velodyne sensor: 0=VLP-16, 1=HDL-32, 2=HDL-64E
OdomLOAM/ScanPeriod float 0.1 Scan period (s)
OdomLOAM/Resolution float 0.2 Map resolution
OdomLOAM/LinVar float 0.01 Linear output variance.
OdomLOAM/AngVar float 0.01 Angular output variance.
OdomLOAM/LocalMapping bool true Local mapping. It adds more time to compute odometry, but accuracy is significantly improved.

OdomMSCKF

Key Type Default Description
OdomMSCKF/GridRow int 4
OdomMSCKF/GridCol int 5
OdomMSCKF/GridMinFeatureNum int 3
OdomMSCKF/GridMaxFeatureNum int 4
OdomMSCKF/PyramidLevels int 3
OdomMSCKF/PatchSize int 15
OdomMSCKF/FastThreshold int 10
OdomMSCKF/MaxIteration int 30
OdomMSCKF/TrackPrecision double 0.01
OdomMSCKF/RansacThreshold double 3
OdomMSCKF/StereoThreshold double 5
OdomMSCKF/PositionStdThreshold double 8.0
OdomMSCKF/RotationThreshold double 0.2618
OdomMSCKF/TranslationThreshold double 0.4
OdomMSCKF/TrackingRateThreshold double 0.5
OdomMSCKF/OptTranslationThreshold double 0
OdomMSCKF/NoiseGyro double 0.005
OdomMSCKF/NoiseAcc double 0.05
OdomMSCKF/NoiseGyroBias double 0.001
OdomMSCKF/NoiseAccBias double 0.01
OdomMSCKF/NoiseFeature double 0.035
OdomMSCKF/InitCovVel double 0.25
OdomMSCKF/InitCovGyroBias double 0.01
OdomMSCKF/InitCovAccBias double 0.01
OdomMSCKF/InitCovExRot double 0.00030462
OdomMSCKF/InitCovExTrans double 0.000025
OdomMSCKF/MaxCamStateSize int 20

OdomVINSFusion

Key Type Default Description
OdomVINSFusion/ConfigPath string "" Path of VINS-Fusion config file.

OdomOpenVINS

Key Type Default Description
OdomOpenVINS/ConfigPath string "" Path of OpenVINS config file (*.yaml). Same format used than OpenVINS library. Note that any parameter from that config file will overwrite the same parameter in OdomOpenVINS group.
OdomOpenVINS/UseStereo bool true If we have more than 1 camera, if we should try to track stereo constraints between pairs.
OdomOpenVINS/UseKLT bool true If true we will use KLT, otherwise use a ORB descriptor + robust matching.
OdomOpenVINS/NumPts int 200 Number of points (per camera) we will extract and try to track.
OdomOpenVINS/MinPxDist int 15 Eistance between features (features near each other provide less information).
OdomOpenVINS/FiTriangulate1d bool false If we should perform 1d triangulation instead of 3d.
OdomOpenVINS/FiRefineFeatures bool true If we should perform Levenberg-Marquardt refinement.
OdomOpenVINS/FiMaxRuns int 5 Max runs for Levenberg-Marquardt.
OdomOpenVINS/FiMaxBaseline double 40 Max baseline ratio to accept triangulated features.
OdomOpenVINS/FiMaxCondNumber double 10000 Max condition number of linear triangulation matrix accept triangulated features.
OdomOpenVINS/UseFEJ bool true If first-estimate Jacobians should be used (enable for good consistency).
OdomOpenVINS/Integration int 1 0=discrete, 1=rk4, 2=analytical (if rk4 or analytical used then analytical covariance propagation is used).
OdomOpenVINS/CalibCamExtrinsics bool false Bool to determine whether or not to calibrate imu-to-camera pose.
OdomOpenVINS/CalibCamIntrinsics bool false Bool to determine whether or not to calibrate camera intrinsics.
OdomOpenVINS/CalibCamTimeoffset bool false Bool to determine whether or not to calibrate camera to IMU time offset.
OdomOpenVINS/CalibIMUIntrinsics bool false Bool to determine whether or not to calibrate the IMU intrinsics.
OdomOpenVINS/CalibIMUGSensitivity bool false Bool to determine whether or not to calibrate the Gravity sensitivity.
OdomOpenVINS/MaxClones int 11 Max clone size of sliding window.
OdomOpenVINS/MaxSLAM int 50 Max number of estimated SLAM features.
OdomOpenVINS/MaxSLAMInUpdate int 25 Max number of SLAM features we allow to be included in a single EKF update..
OdomOpenVINS/MaxMSCKFInUpdate int 50 Max number of MSCKF features we will use at a given image timestep..
OdomOpenVINS/FeatRepMSCKF int 0 What representation our features are in (msckf features).
OdomOpenVINS/FeatRepSLAM int 4 What representation our features are in (slam features).
OdomOpenVINS/DtSLAMDelay double 0.0 Delay, in seconds, that we should wait from init before we start estimating SLAM features.
OdomOpenVINS/GravityMag double 9.81 Gravity magnitude in the global frame (i.e. should be 9.81 typically).
OdomOpenVINS/LeftMaskPath string "" Mask for left image.
OdomOpenVINS/RightMaskPath string "" Mask for right image.
OdomOpenVINS/InitWindowTime double 2.0 Amount of time we will initialize over (seconds).
OdomOpenVINS/InitIMUThresh double 1.0 Variance threshold on our acceleration to be classified as moving.
OdomOpenVINS/InitMaxDisparity double 10.0 Max disparity to consider the platform stationary (dependent on resolution).
OdomOpenVINS/InitMaxFeatures int 50 How many features to track during initialization (saves on computation).
OdomOpenVINS/InitDynUse bool false If dynamic initialization should be used.
OdomOpenVINS/InitDynMLEOptCalib bool false If we should optimize calibration during intialization (not recommended).
OdomOpenVINS/InitDynMLEMaxIter int 50 How many iterations the MLE refinement should use (zero to skip the MLE).
OdomOpenVINS/InitDynMLEMaxTime double 0.05 How many seconds the MLE should be completed in.
OdomOpenVINS/InitDynMLEMaxThreads int 6 How many threads the MLE should use.
OdomOpenVINS/InitDynNumPose int 6 Number of poses to use within our window time (evenly spaced).
OdomOpenVINS/InitDynMinDeg double 10.0 Orientation change needed to try to init.
OdomOpenVINS/InitDynInflationOri double 10.0 What to inflate the recovered q_GtoI covariance by.
OdomOpenVINS/InitDynInflationVel double 100.0 What to inflate the recovered v_IinG covariance by.
OdomOpenVINS/InitDynInflationBg double 10.0 What to inflate the recovered bias_g covariance by.
OdomOpenVINS/InitDynInflationBa double 100.0 What to inflate the recovered bias_a covariance by.
OdomOpenVINS/InitDynMinRecCond double 1e-15 Reciprocal condition number thresh for info inversion.
OdomOpenVINS/TryZUPT bool true If we should try to use zero velocity update.
OdomOpenVINS/ZUPTChi2Multiplier double 0.0 Chi2 multiplier for zero velocity.
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).
OdomOpenVINS/ZUPTNoiseMultiplier double 10.0 Multiplier of our zupt measurement IMU noise matrix (default should be 1.0).
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).
OdomOpenVINS/ZUPTOnlyAtBeginning bool false If we should only use the zupt at the very beginning static initialization phase.
OdomOpenVINS/AccelerometerNoiseDensity double 0.01 [m/s^2/sqrt(Hz)] (accel "white noise").
OdomOpenVINS/AccelerometerRandomWalk double 0.001 [m/s^3/sqrt(Hz)] (accel bias diffusion).
OdomOpenVINS/GyroscopeNoiseDensity double 0.001 [rad/s/sqrt(Hz)] (gyro "white noise").
OdomOpenVINS/GyroscopeRandomWalk double 0.0001 [rad/s^2/sqrt(Hz)] (gyro bias diffusion).
OdomOpenVINS/UpMSCKFSigmaPx double 1.0 Pixel noise for MSCKF features.
OdomOpenVINS/UpMSCKFChi2Multiplier double 1.0 Chi2 multiplier for MSCKF features.
OdomOpenVINS/UpSLAMSigmaPx double 1.0 Pixel noise for SLAM features.
OdomOpenVINS/UpSLAMChi2Multiplier double 1.0 Chi2 multiplier for SLAM features.

OdomOpen3D

Key Type Default Description
OdomOpen3D/MaxDepth float 3.0 Maximum depth.
OdomOpen3D/Method int 0 Registration method: 0=PointToPlane, 1=Intensity, 2=Hybrid.

OdomCuVSLAM

Key Type Default Description
OdomCuVSLAM/MulticamMode int 0 cuVSLAM multicam_mode setting: 0=moderate, 1=performance, 2=precision.

OdomLIOSAM

Key Type Default Description
OdomLIOSAM/ConfigPath string "" Path to LIO-SAM params.yaml config file. When set, sensor/IMU/feature parameters are loaded from the file and the individual parameters below are ignored.
OdomLIOSAM/Sensor int 0 LiDAR sensor: 0=Velodyne, 1=Ouster, 2=Livox
OdomLIOSAM/NScan int 16 Number of LiDAR channels (16, 32, 64, 128).
OdomLIOSAM/HorizonScan int 1800 Horizontal resolution (Velodyne:1800, Ouster:512/1024/2048).
OdomLIOSAM/ImuAccNoise float 0.01 IMU accelerometer white noise.
OdomLIOSAM/ImuGyrNoise float 0.001 IMU gyroscope white noise.
OdomLIOSAM/ImuAccBiasN float 0.0002 IMU accelerometer bias noise.
OdomLIOSAM/ImuGyrBiasN float 0.00003 IMU gyroscope bias noise.
OdomLIOSAM/ImuGravity float 9.80511 Gravity magnitude.
OdomLIOSAM/EdgeThreshold float 1.0 Edge feature curvature threshold.
OdomLIOSAM/SurfThreshold float 0.1 Surface feature curvature threshold.
OdomLIOSAM/LinVar float 0.01 Linear output variance.
OdomLIOSAM/AngVar float 0.01 Angular output variance.

Reg

Registration strategy shared by loop closures and proximity detection.

Key Type Default Description
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.
Reg/Strategy int 0 0=Vis, 1=Icp, 2=VisIcp
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.

Vis

Visual registration (feature extraction, matching and PnP).

Key Type Default Description
Vis/EstimationType int 1 Motion estimation approach: 0:3D->3D, 1:3D->2D (PnP), 2:2D->2D (Epipolar Geometry)
Vis/InlierDistance float 0.1 [Vis/EstimationType = 0] Maximum distance for feature correspondences. Used by 3D->3D estimation approach.
Vis/RefineIterations int 5 [Vis/EstimationType = 0] Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.
Vis/PnPReprojError float 2 [Vis/EstimationType = 1] PnP reprojection error.
Vis/PnPFlags int 0 [Vis/EstimationType = 1] PnP flags: 0=Iterative, 1=EPNP, 2=P3P
Vis/PnPRefineIterations int 0 with defined(RTABMAP_G2O) \|\| defined(RTABMAP_ORB_SLAM)
1 otherwise 		[Vis/BundleAdjustment = 1] Refine iterations. Set to 0 if Vis/EstimationType is also used.
Vis/PnPVarianceMedianRatio int 4 [Vis/EstimationType = 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.
Vis/PnPMaxVariance float 0.0 [Vis/EstimationType = 1] Max linear variance between 3D point correspondences after PnP. 0 means disabled.
Vis/PnPSamplingPolicy unsigned int 1 [Vis/EstimationType = 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.
Vis/PnPSplitLinearCovComponents bool false [Vis/EstimationType = 1] Compute variance for each linear component instead of using the combined XYZ variance for all linear components.
Vis/EpipolarGeometryVar float 0.1 [Vis/EstimationType = 2] Epipolar geometry maximum variance to accept the transformation.
Vis/MinInliers int 20 Minimum feature correspondences to compute/accept the transformation.
Vis/MeanInliersDistance float 0.0 Maximum distance (m) of the mean distance of inliers from the camera to accept the transformation. 0 means disabled.
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.
Vis/Iterations int 300 Maximum iterations to compute the transform.
Vis/FeatureType int 8 with CV_MAJOR_VERSION &gt; 2 && !defined(HAVE_OPENCV_XFEATURES2D)
6 otherwise 		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 16=SuperPoint-Rpautrat
Vis/MaxFeatures int 1000 0 no limits.
Vis/SSC bool false If true, SSC (Suppression via Square Covering) is applied to limit keypoints.
Vis/MaxDepth float 0 Max depth of the features (0 means no limit).
Vis/MinDepth float 0 Min depth of the features (0 means no limit).
Vis/DepthAsMask bool true Use depth image as mask when extracting features.
Vis/DepthMaskFloorThr float 0.0 Filter floor from depth mask below specified threshold (m) before extracting features. 0 means disabled. Ignored if Vis/DepthAsMask is false.
Vis/RoiRatios string 0.0 0.0 0.0 0.0 Region of interest ratios [left, right, top, bottom].
Vis/SubPixWinSize int 3 See cv::cornerSubPix().
Vis/SubPixIterations int 0 See cv::cornerSubPix(). 0 disables sub pixel refining.
Vis/SubPixEps float 0.02 See cv::cornerSubPix().
Vis/GridRows int 1 Number of rows of the grid used to extract uniformly "@ref param_VisMaxFeatures "Vis/MaxFeatures" / grid cells" features from each cell.
Vis/GridCols int 1 Number of columns of the grid used to extract uniformly "@ref param_VisMaxFeatures "Vis/MaxFeatures" / grid cells" features from each cell.
Vis/CorType int 0 Correspondences computation approach: 0=Features Matching, 1=Optical Flow
Vis/CorNNType int 1 [Vis/CorType=0] kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4, BruteForceCrossCheck=5, SuperGlue=6, GMS=7. Used for features matching approach.
Vis/CorNNDR float 0.8 [Vis/CorType=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach.
Vis/CorGuessWinSize int 40 [Vis/CorType=0] Matching window size (pixels) around projected points when a guess transform is provided to find correspondences. 0 means disabled.
Vis/CorGuessMatchToProjection bool false [Vis/CorType=0] Match frame's corners to source's projected points (when guess transform is provided) instead of projected points to frame's corners.
Vis/CorFlowWinSize int 16 [Vis/CorType=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.
Vis/CorFlowIterations int 30 [Vis/CorType=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.
Vis/CorFlowEps float 0.01 [Vis/CorType=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.
Vis/CorFlowMaxLevel int 3 [Vis/CorType=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach.
Vis/CorFlowUseMinEigenVals bool true [Vis/CorType=1] See cv::calcOpticalFlowPyrLK(). Used for optical flow approach. Use minimum eigen values as an error measure, otherwise L1 distance between patches is used as an error measure.
Vis/CorFlowMinEigThreshold float 1e-4 [Vis/CorFlowUseMinEigenVals=true] If the minimum eigenvalue of a feature's spatial gradient matrix is less than this threshold, then the feature is filtered out.
Vis/CorFlowErrorThreshold float 20 [Vis/CorFlowUseMinEigenVals=false] Filter out features with error greater than this threshold.
Vis/CorFlowGpu bool false [Vis/CorType=1] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA). Note that Vis/CorFlowUseMinEigenVals is not used in the GPU implementation.
Vis/BundleAdjustment int 1 with defined(RTABMAP_G2O) \|\| defined(RTABMAP_ORB_SLAM)
0 otherwise 		Optimization with bundle adjustment. Value matches the Optimizer/Strategy parameter: 0=disabled (TORO is not BA-capable), 1=g2o, 2=GTSAM, 3=Ceres, 4=cvsba.

PyMatcher

Key Type Default Description
PyMatcher/Path string "" Path to python script file (see available ones in rtabmap/corelib/src/python/*). See the header to see where the script should be copied.
PyMatcher/Iterations int 20 Sinkhorn iterations. Used by SuperGlue.
PyMatcher/Threshold float 0.2 Used by SuperGlue.
PyMatcher/Cuda bool true Used by SuperGlue.
PyMatcher/Model string 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").

GMS

Key Type Default Description
GMS/WithRotation bool false Take rotation transformation into account.
GMS/WithScale bool false Take scale transformation into account.
GMS/ThresholdFactor double 6.0 The higher, the less matches.

PyDescriptor

Key Type Default Description
PyDescriptor/Path string "" Path to python script file (see available ones in rtabmap/corelib/src/pydescriptor/*). See the header to see where the script should be used.
PyDescriptor/Dim int 4096 Descriptor dimension.

Icp

Geometric registration by iterative closest point.

Key Type Default Description
Icp/Strategy int 1 with defined(RTABMAP_POINTMATCHER)
0 otherwise 		ICP implementation: 0=Point Cloud Library, 1=libpointmatcher, 2=CCCoreLib (CloudCompare).
Icp/MaxTranslation float 0.2 Maximum ICP translation correction accepted (m).
Icp/MaxRotation float 0.78 Maximum ICP rotation correction accepted (rad).
Icp/VoxelSize float 0.05 Uniform sampling voxel size (0=disabled).
Icp/DownsamplingStep int 1 Downsampling step size (1=no sampling). This is done before uniform sampling.
Icp/RangeMin float 0 Minimum range filtering (0=disabled).
Icp/RangeMax float 0 Maximum range filtering (0=disabled).
Icp/MaxCorrespondenceDistance float 0.1 with defined(RTABMAP_POINTMATCHER)
0.05 otherwise 		Max distance for point correspondences.
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.
Icp/Iterations int 30 Max iterations.
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.
Icp/CorrespondenceRatio float 0.1 Ratio of matching correspondences to accept the transform.
Icp/Force4DoF bool false Limit ICP to x, y, z and yaw DoF. Available if Icp/Strategy > 0.
Icp/FiltersEnabled int 3 Flag to enable filters: 1="from" cloud only, 2="to" cloud only, 3=both.
Icp/PointToPlane bool true with defined(RTABMAP_POINTMATCHER)
false otherwise 		Use point to plane ICP.
Icp/PointToPlaneK int 5 Number of neighbors to compute normals for point to plane if the cloud doesn't have already normals.
Icp/PointToPlaneRadius float 0.0 Search radius to compute normals for point to plane if the cloud doesn't have already normals.
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.
Icp/PointToPlaneMinComplexity float 0.02 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 Icp/PointToPlaneLowComplexityStrategy is used. This check is done only when Icp/PointToPlane=true.
Icp/PointToPlaneComplexityCentered bool false If false (default), the complexity metric uses the uncentered second-moment matrix (1/N) * sum(n_i * n_i^T), whose smallest eigenvalue directly measures how well the surface normals span R^N. If true, uses centered PCA (cv::PCA covariance) for backwards compatibility – but the centered metric is known to mis-classify perpendicular-surface scenes as degenerate when normals are consistently viewpoint-flipped (only N distinct directions in N-D collapse to rank N-1 after centering). For true degeneracies (parallel surfaces, e.g. corridors) the two metrics agree because the normal mean is zero. The Icp/PointToPlaneMinComplexity threshold of 0.02 works under either setting.
Icp/PointToPlaneLowComplexityStrategy int 1 If structural complexity is below Icp/PointToPlaneMinComplexity: set to 0 so that the transform is automatically rejected, set to 1 (default, legacy) to recompute the transform with PointToPoint and limit its 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 recompute the transform with PointToPoint and accept it "as is", set to 3 to keep the PointToPlane transform and apply the same axis-constrained projection as strategy 1.
Icp/OutlierRatio float 0.85 Outlier ratio. For libpointmatcher (Icp/Strategy=1), sets TrimmedDistOutlierFilter/ratio for convenience when configuration file is not set. For CCCoreLib (Icp/Strategy=2), sets "finalOverlapRatio". For PCL (Icp/Strategy=0), if 0<value<1, installs a RANSAC correspondence rejector with inlier threshold = value * Icp/MaxCorrespondenceDistance. The value should be between 0 and 1.
Icp/DebugExportFormat string "" 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.
Icp/PMConfig string "" Configuration file (*.yaml) used by libpointmatcher. Note that data filters set for libpointmatcher are done after filtering done by rtabmap (i.e., Icp/VoxelSize, Icp/DownsamplingStep), so make sure to disable those in rtabmap if you want to use only those from libpointmatcher. Parameters Icp/Iterations, Icp/Epsilon and Icp/MaxCorrespondenceDistance are also ignored if configuration file is set.
Icp/PMMatcherKnn int 1 KDTreeMatcher/knn: number of nearest neighbors to consider it the reference. For convenience when configuration file is not set.
Icp/PMMatcherEpsilon float 0.0 KDTreeMatcher/epsilon: approximation to use for the nearest-neighbor search. For convenience when configuration file is not set.
Icp/PMMatcherIntensity bool false KDTreeMatcher: among nearest neighbors, keep only the one with the most similar intensity. This only work with Icp/PMMatcherKnn>1.
Icp/CCSamplingLimit unsigned int 50000 Maximum number of points per cloud (they are randomly resampled below this limit otherwise).
Icp/CCFilterOutFarthestPoints bool false If true, the algorithm will automatically ignore farthest points from the reference, for better convergence.
Icp/CCMaxFinalRMS float 0.2 Maximum final RMS error.

Stereo

Stereo correspondence.

Key Type Default Description
Stereo/WinWidth int 15 Window width.
Stereo/WinHeight int 3 Window height.
Stereo/Iterations int 30 Maximum iterations.
Stereo/MaxLevel int 5 Maximum pyramid level.
Stereo/MinDisparity float 0.5 Minimum disparity.
Stereo/MaxDisparity float 128.0 Maximum disparity.
Stereo/OpticalFlow bool true Use optical flow to find stereo correspondences, otherwise a simple block matching approach is used.
Stereo/SSD bool true [Stereo/OpticalFlow=false] Use Sum of Squared Differences (SSD) window, otherwise Sum of Absolute Differences (SAD) window is used.
Stereo/Eps double 0.01 [Stereo/OpticalFlow=true] Epsilon stop criterion.
Stereo/UseMinEigenVals bool true [Stereo/OpticalFlow=true] Use minimum eigen values as an error measure, otherwise L1 distance between patches is used as an error measure.
Stereo/MinEigThreshold double 1e-4 [Stereo/UseMinEigenVals=true] If the minimum eigenvalue of a feature's spatial gradient matrix is less than this threshold, then the feature is filtered out.
Stereo/ErrorThreshold double 50 [Stereo/UseMinEigenVals=false] Filter out features with error greater than this threshold.
Stereo/Gpu bool false [Stereo/OpticalFlow=true] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA). Note that Stereo/UseMinEigenVals is not used in the GPU implementation.
Stereo/DenseStrategy int 0 0=cv::StereoBM, 1=cv::StereoSGBM

StereoBM

Key Type Default Description
StereoBM/BlockSize int 15 See cv::StereoBM
StereoBM/MinDisparity int 0 See cv::StereoBM
StereoBM/NumDisparities int 128 See cv::StereoBM
StereoBM/PreFilterSize int 9 See cv::StereoBM
StereoBM/PreFilterCap int 31 See cv::StereoBM
StereoBM/UniquenessRatio int 15 See cv::StereoBM
StereoBM/TextureThreshold int 10 See cv::StereoBM
StereoBM/SpeckleWindowSize int 100 See cv::StereoBM
StereoBM/SpeckleRange int 4 See cv::StereoBM
StereoBM/Disp12MaxDiff int -1 See cv::StereoBM

StereoSGBM

Key Type Default Description
StereoSGBM/BlockSize int 15 See cv::StereoSGBM
StereoSGBM/MinDisparity int 0 See cv::StereoSGBM
StereoSGBM/NumDisparities int 128 See cv::StereoSGBM
StereoSGBM/PreFilterCap int 31 See cv::StereoSGBM
StereoSGBM/UniquenessRatio int 20 See cv::StereoSGBM
StereoSGBM/SpeckleWindowSize int 100 See cv::StereoSGBM
StereoSGBM/SpeckleRange int 4 See cv::StereoSGBM
StereoSGBM/Disp12MaxDiff int 1 See cv::StereoSGBM
StereoSGBM/P1 int 2 See cv::StereoSGBM
StereoSGBM/P2 int 5 See cv::StereoSGBM
StereoSGBM/Mode int 0 with CV_MAJOR_VERSION &lt; 3
2 otherwise 		See cv::StereoSGBM

Grid

Local occupancy grid generation from each node.

Key Type Default Description
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).
Grid/DepthDecimation unsigned int 4 [Grid/DepthDecimation=true] Decimation of the depth image before creating cloud.
Grid/RangeMin float 0.0 Minimum range from sensor.
Grid/RangeMax float 5.0 Maximum range from sensor. 0=inf.
Grid/DepthRoiRatios string 0.0 0.0 0.0 0.0 [Grid/Sensor>=1] Region of interest ratios [left, right, top, bottom].
Grid/FootprintLength float 0.0 Footprint length used to filter points over the footprint of the robot.
Grid/FootprintWidth float 0.0 Footprint width used to filter points over the footprint of the robot. Footprint length should be set.
Grid/FootprintHeight float 0.0 Footprint height used to filter points over the footprint of the robot. Footprint length and width should be set.
Grid/ScanDecimation int 1 [Grid/Sensor=0 or 2] Decimation of the laser scan before creating cloud.
Grid/CellSize float 0.05 Resolution of the occupancy grid.
Grid/PreVoxelFiltering bool true Input cloud is downsampled by voxel filter (voxel size is Grid/CellSize) before doing segmentation of obstacles and ground.
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.
Grid/NormalsSegmentation bool true Segment ground from obstacles using point normals, otherwise a fast passthrough is used.
Grid/MaxObstacleHeight float 0.0 Maximum obstacles height (0=disabled).
Grid/MinGroundHeight float 0.0 Minimum ground height (0=disabled).
Grid/MaxGroundHeight float 0.0 Maximum ground height (0=disabled). Should be set if Grid/NormalsSegmentation is false.
Grid/MaxGroundAngle float 45 [Grid/NormalsSegmentation=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.
Grid/NormalK int 20 [Grid/NormalsSegmentation=true] K neighbors to compute normals.
Grid/ClusterRadius float 0.1 [Grid/NormalsSegmentation=true] Cluster maximum radius.
Grid/MinClusterSize int 10 [Grid/NormalsSegmentation=true] Minimum cluster size to project the points.
Grid/FlatObstacleDetected bool true [Grid/NormalsSegmentation=true] Flat obstacles detected.
Grid/3D bool true with defined(RTABMAP_OCTOMAP)
false otherwise 		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 Grid/Sensor is 0.
Grid/GroundIsObstacle bool false [Grid/3D=true] Ground segmentation (Grid/NormalsSegmentation) 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).
Grid/NoiseFilteringRadius float 0.0 Noise filtering radius (0=disabled). Done after segmentation.
Grid/NoiseFilteringMinNeighbors int 5 Noise filtering minimum neighbors.
Grid/Scan2dUnknownSpaceFilled bool false Unknown space filled. Only used with 2D laser scans. Use Grid/RangeMax to set maximum range if laser scan max range is to set.
Grid/RayTracing bool false Ray tracing is done for each occupied cell, filling unknown space between the sensor and occupied cells. If Grid/3D=true, RTAB-Map should be built with OctoMap support, otherwise 3D ray tracing is ignored.

GridGlobal

Assembly of the local grids into the global map.

Key Type Default Description
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.
GridGlobal/FootprintRadius float 0.0 Footprint radius (m) used to clear all obstacles under the graph.
GridGlobal/MinSize float 0.0 Minimum map size (m).
GridGlobal/Eroded bool false Erode obstacle cells.
GridGlobal/MaxNodes int 0 Maximum nodes assembled in the map starting from the last node (0=unlimited).
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).
GridGlobal/OccupancyThr float 0.5 Occupancy threshold (value between 0 and 1).
GridGlobal/ProbHit float 0.7 Probability of a hit (value between 0.5 and 1).
GridGlobal/ProbMiss float 0.4 Probability of a miss (value between 0 and 0.5).
GridGlobal/ProbClampingMin float 0.1192 Probability clamping minimum (value between 0 and 1).
GridGlobal/ProbClampingMax float 0.971 Probability clamping maximum (value between 0 and 1).
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.

Marker

Fiducial marker (ArUco/AprilTag) detection and landmarks.

Key Type Default Description
Marker/Strategy int 0 Marker detection implementation: 0=OpenCV, 1=AprilTag
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, DICT_ARUCO_MIP_36H12=21
Marker/Length float 0 The length (m) of the markers' side. Value <=0 means automatic marker length estimation using the depth image (the camera should look at the marker perpendicularly for initialization). If 0, the length is estimated only on the first marker detected, then re-used for all next detections (i.e., this assumes that markers have all the same length). With <0, the length is estimated once for each unique marker, then re-used for next detections with the same marker ID.
Marker/Lengths string "" List of markers to detect. Format is the marker's ID followed by its length (in meters), multiple markers are separated by a vertical line ("id1 length\|id2 length"). We can also define a range of markers with "id1:id2 length" (id2 included). If empty, all markers of the chosen dictionary can be detected and their length is set/estimated based on Marker/Length. For example, to detect markers 12 and 14 with lengths of 8 and 15 cm respectively, and all markers between 30 and 40 with a length of 10 cm, set "12 0.08\|14 0.15\|30:40 0.1".
Marker/MaxDepthError float 0.01 Maximum depth error between all corners of a marker when estimating the marker length (when Marker/Length is 0). The smaller it is, the more perpendicular the camera should be toward the marker to initialize the length.
Marker/VarianceLinear float 0.001 Linear variance to set on marker detections. If Marker/VarianceOrientationIgnored is enabled and Optimizer/Strategy=2 (GTSAM): it is the variance of the range factor, with 9999 to disable range factor and to do only bearing.
Marker/VarianceAngular float 0.01 Angular variance to set on marker detections. If Marker/VarianceOrientationIgnored is enabled, it is ignored with Optimizer/Strategy=1 (g2o) and it corresponds to bearing variance with Optimizer/Strategy=2 (GTSAM).
Marker/VarianceOrientationIgnored bool false When this setting is false, the landmark's orientation is optimized during graph optimization. When this setting is true, only the position of the landmark is optimized. This can be useful when the landmark's orientation estimation is not reliable. Note that for Optimizer/Strategy=1 (g2o), only Marker/VarianceLinear needs be set if we ignore orientation. For Optimizer/Strategy=2 (GTSAM), instead of optimizing the landmark's position directly, a bearing/range factor is used, with Marker/VarianceLinear as the variance of the range factor (with 9999 to optimize the position with only a bearing factor) and Marker/VarianceAngular as the variance of the bearing factor (pitch/yaw).
Marker/MaxRange float 0.0 Maximum range in which markers will be detected. <=0 for unlimited range.
Marker/MinRange float 0.0 Miniminum range in which markers will be detected. <=0 for unlimited range.
Marker/Priors string "" 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), multiple 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).
Marker/PriorsVarianceLinear float 0.001 Linear variance to set on marker priors.
Marker/PriorsVarianceAngular float 0.001 Angular variance to set on marker priors.

MarkerAprilTag

Key Type Default Description
MarkerAprilTag/NThreads int 1 How many threads should be used?
MarkerAprilTag/QuadDecimate float 1.0 Detection of quads can be done on a lower-resolution image, improving speed at a cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still done at full resolution.
MarkerAprilTag/QuadSigma float 0.0 What Gaussian blur should be applied to the segmented image (used for quad detection?) Parameter is the standard deviation in pixels. Very noisy images benefit from non-zero values (e.g. 0.8).
MarkerAprilTag/RefineEdges bool true When true, the edges of the each quad are adjusted to "snap to" strong gradients nearby. This is useful when decimation is employed, as it can increase the quality of the initial quad estimate substantially. Generally recommended to be on (true). Very computationally inexpensive. Option is ignored if MarkerAprilTag/QuadDecimate = 1.
MarkerAprilTag/DecodeSharpening double 0.25 How much sharpening should be done to decoded images? This can help decode small tags but may or may not help in odd lighting conditions or low light conditions.
MarkerAprilTag/Debug bool false When true, write a variety of debugging images to the working directory where the app started (not Rtabmap/WorkingDirectory) at various stages through the detection process. (Somewhat slow).

MarkerOpenCV

Key Type Default Description
MarkerOpenCV/CornerRefinementMethod int 0 Corner refinement method for OpenCV strategy (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.

ImuFilter

Key Type Default Description
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].
ImuFilter/MadgwickZeta double 0.0 Gyro drift gain (approx. rad/s), belongs in [-1, 1].
ImuFilter/ComplementaryGainAcc double 0.01 Gain parameter for the complementary filter, belongs in [0, 1].
ImuFilter/ComplementaryBiasAlpha double 0.01 Bias estimation gain parameter, belongs in [0, 1].
ImuFilter/ComplementaryDoBiasEstimation bool true Parameter whether to do bias estimation or not.
ImuFilter/ComplementaryDoAdpativeGain bool true Parameter whether to do adaptive gain or not.