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_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_PARAM(Rtabmap,DetectionRate,float,1,"Detection rate (Hz). RTAB-Map will filter input images to satisfy this rate.");
RTABMAP_PARAM(Rtabmap,ImageBufferSize,unsignedint,1,"Data buffer size (0 min inf).");
RTABMAP_PARAM(Rtabmap,CreateIntermediateNodes,bool,false,uFormat("Create intermediate nodes between loop closure detection. Only used when %s>0.",kRtabmapDetectionRate().c_str()));
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_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_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_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_PARAM(Mem,ImageKept,bool,false,"Keep raw images in RAM.");
RTABMAP_PARAM(Mem,BinDataKept,bool,true,"Keep binary data in db.");
RTABMAP_PARAM(Mem,RawDescriptorsKept,bool,true,"Raw descriptors kept in memory.");
RTABMAP_PARAM(Mem,MapLabelsAdded,bool,true,"Create map labels. The first node of a map will be labelled as \"map#\" where # is the map ID.");
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_PARAM(Mem,NotLinkedNodesKept,bool,true,"Keep not linked nodes in db (rehearsed nodes and deleted nodes).");
RTABMAP_PARAM(Mem,IntermediateNodeDataKept,bool,false,"Keep intermediate node data in db.");
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_PARAM(Mem,ReduceGraph,bool,false,"Reduce graph. Merge nodes when loop closures are added (ignoring those with user data set).");
RTABMAP_PARAM(Mem,RecentWmRatio,float,0.2,"Ratio of locations after the last loop closure in WM that cannot be transferred.");
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_PARAM(Mem,RehearsalIdUpdatedToNewOne,bool,false,"On merge, update to new id. When false, no copy.");
RTABMAP_PARAM(Mem,RehearsalWeightIgnoredWhileMoving,bool,false,"When the robot is moving, weights are not updated on rehearsal.");
RTABMAP_PARAM(Mem,BadSignaturesIgnored,bool,false,"Bad signatures are ignored.");
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_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_PARAM(Mem,ImagePreDecimation,int,1,"Image decimation (>=1) before features extraction.");
RTABMAP_PARAM(Mem,ImagePostDecimation,int,1,"Image decimation (>=1) of saved data in created signatures (after features extraction). Decimation is done from the original image.");
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_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_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_PARAM(Mem,UseOdomFeatures,bool,true,"Use odometry features instead of regenerating them.");
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_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_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_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_PARAM(Kp,BadSignRatio,float,0.5,"Bad signature ratio (less than Ratio x AverageWordsPerImage = bad).");
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_PARAM_STR(Kp,DictionaryPath,"","Path of the pre-computed dictionary");
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_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_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_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_PARAM(SURF,Extended,bool,false,"Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).");
RTABMAP_PARAM(SURF,HessianThreshold,float,500,"Threshold for hessian keypoint detector used in SURF.");
RTABMAP_PARAM(SURF,Octaves,int,4,"Number of pyramid octaves the keypoint detector will use.");
RTABMAP_PARAM(SURF,OctaveLayers,int,2,"Number of octave layers within each octave.");
RTABMAP_PARAM(SURF,Upright,bool,false,"Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).");
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_PARAM(SURF,GpuKeypointsRatio,float,0.01,"Used with SURF GPU.");
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_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_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_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_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_PARAM(FAST,NonmaxSuppression,bool,true,"If true, non-maximum suppression is applied to detected corners (keypoints).");
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_PARAM(FAST,GpuKeypointsRatio,double,0.05,"Used with FAST GPU.");
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_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_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_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_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_PARAM(ORB,FirstLevel,int,0,"It should be 0 in the current implementation.");
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_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_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_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_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_PARAM(Bayes,FullPredictionUpdate,bool,false,"Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).");
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_PARAM(VhEp,MatchCountMin,int,8,"Minimum of matching visual words pairs to accept the loop hypothesis.");
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_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_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_PARAM(RGBD,LinearSpeedUpdate,float,0.0,"Maximum linear speed (m/s) to update the map (0 means not limit).");
RTABMAP_PARAM(RGBD,AngularSpeedUpdate,float,0.0,"Maximum angular speed (rad/s) to update the map (0 means not limit).");
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_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_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_PARAM(RGBD,MaxLoopClosureDistance,float,0.0,"Reject loop closures/localizations if the distance from the map is over this distance (0=disabled).");
RTABMAP_PARAM(RGBD,SavedLocalizationIgnored,bool,false,uFormat("Ignore last saved localization pose from previous session. If true, RTAB-Map won't assume it is restarting from the same place than where it shut down previously. In localization mode (%s=true), the robot will assume that it is starting from the origin of the map.",kMemIncrementalMemory().c_str()));
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_PARAM(RGBD,PlanLinearVelocity,float,0,"Linear velocity (m/sec) used to compute path weights.");
RTABMAP_PARAM(RGBD,PlanAngularVelocity,float,0,"Angular velocity (rad/sec) used to compute path weights.");
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_PARAM(RGBD,MaxLocalRetrieved,unsignedint,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_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_PARAM(RGBD,LocalImmunizationRatio,float,0.25,"Ratio of working memory for which local nodes are immunized from transfer.");
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_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_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_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_PARAM(RGBD,MaxOdomCacheSize,int,0,uFormat("Maximum odometry cache size. Used only in localization mode (when %s=false) and when %s!=0. This is used to verify localization transforms to make sure we don't teleport to a location very similar to one we previously localized on. When the cache is full, the whole cache is cleared and the next localization is automatically accepted without verification. Set 0 to disable caching.",kMemIncrementalMemory().c_str(),kRGBDOptimizeMaxError().c_str()));
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_PARAM(RGBD,ProximityMaxPaths,int,3,"Maximum paths compared (from the most recent) for proximity detection. 0 means no limit.");
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_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_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_PARAM(RGBD,ProximityAngle,float,45,"Maximum angle (degrees) for one-to-one proximity detection.");
RTABMAP_PARAM(RGBD,ProximityOdomGuess,bool,false,"Use odometry as motion guess for one-to-one proximity detection.");
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_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_PARAM(Optimizer,PriorsIgnored,bool,true,"Ignore prior constraints (global pose or GPS) while optimizing. Currently only g2o and gtsam optimization supports this.");
RTABMAP_PARAM(Optimizer,LandmarksIgnored,bool,false,"Ignore landmark constraints while optimizing. Currently only g2o and gtsam optimization supports this.");
RTABMAP_PARAM(Optimizer,GravitySigma,float,0.3,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_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_PARAM(g2o,PixelVariance,double,1.0,"Pixel variance used for bundle adjustment.");
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_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_PARAM(Odom,ResetCountdown,int,0,"Automatically reset odometry after X consecutive images on which odometry cannot be computed (value=0 disables auto-reset).");
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_PARAM(Odom,FillInfoData,bool,true,"Fill info with data (inliers/outliers features).");
RTABMAP_PARAM(Odom,ImageBufferSize,unsignedint,1,"Data buffer size (0 min inf).");
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_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_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_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_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_PARAM(Odom,ImageDecimation,int,1,"Decimation of the images before registration. Negative decimation is done from RGB size instead of depth size (if depth is smaller than RGB, it may be interpolated depending of the decimation value).");
RTABMAP_PARAM(Odom,AlignWithGround,bool,false,"Align odometry with the ground on initialization.");
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_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_PARAM(OdomF2M,ScanMaxSize,int,2000,"[Geometry] Maximum local scan map size.");
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_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_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_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_PARAM(OdomMono,InitMinFlow,float,100,"Minimum optical flow required for the initialization step.");
RTABMAP_PARAM(OdomMono,InitMinTranslation,float,0.1,"Minimum translation required for the initialization step.");
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_PARAM(OdomMono,MaxVariance,float,0.01,"Maximum variance to add new points to local map.");
RTABMAP_PARAM(OdomFovis,FeatureWindowSize,int,9,"The size of the n x n image patch surrounding each feature, used for keypoint matching.");
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_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_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_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_PARAM(OdomFovis,MaxMeanReprojectionError,double,10.0,"Maximum mean reprojection error over the inlier feature matches for the motion estimate to be considered valid.");
RTABMAP_PARAM(OdomFovis,UseSubpixelRefinement,bool,true,"Specifies whether or not to refine feature matches to subpixel resolution.");
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_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_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_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_PARAM(Vis,InlierDistance,float,0.1,uFormat("[%s = 0] Maximum distance for feature correspondences. Used by 3D->3D estimation approach.",kVisEstimationType().c_str()));
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_PARAM(Vis,PnPRefineIterations,int,0,uFormat("[%s = 1] Refine iterations. Set to 0 if \"%s\" is also used.",kVisEstimationType().c_str(),kVisBundleAdjustment().c_str()));
#else
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_PARAM(Vis,EpipolarGeometryVar,float,0.1,uFormat("[%s = 2] Epipolar geometry maximum variance to accept the transformation.",kVisEstimationType().c_str()));
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_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_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_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_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_PARAM(Vis,CorNNDR,float,0.8,uFormat("[%s=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach.",kVisCorType().c_str()));
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_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_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_PARAM(PyMatcher,Iterations,int,20,"Sinkhorn iterations. Used by SuperGlue.");
RTABMAP_PARAM(PyMatcher,Threshold,float,0.2,"Used by SuperGlue.");
RTABMAP_PARAM(PyMatcher,Cuda,bool,true,"Used by SuperGlue.");
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_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_PARAM(Icp,PointToPlaneK,int,5,"Number of neighbors to compute normals for point to plane if the cloud doesn't have already normals.");
RTABMAP_PARAM(Icp,PointToPlaneRadius,float,1.0,"Search radius to compute normals for point to plane if the cloud doesn't have already normals.");
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_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_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_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_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_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_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_PARAM(Icp,PMOutlierRatio,float,0.85,"TrimmedDistOutlierFilter/ratio: For convenience when configuration file is not set. For kinect-like point cloud, use 0.65.");
RTABMAP_PARAM(Stereo,OpticalFlow,bool,true,"Use optical flow to find stereo correspondences, otherwise a simple block matching approach is used.");
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_PARAM(Grid,FromDepth,bool,true,"Create occupancy grid from depth image(s), otherwise it is created from laser scan.");
RTABMAP_PARAM(Grid,DepthDecimation,int,4,uFormat("[%s=true] Decimation of the depth image before creating cloud. Negative decimation is done from RGB size instead of depth size (if depth is smaller than RGB, it may be interpolated depending of the decimation value).",kGridDepthDecimation().c_str()));
RTABMAP_PARAM_STR(Grid,DepthRoiRatios,"0.0 0.0 0.0 0.0",uFormat("[%s=true] Region of interest ratios [left, right, top, bottom].",kGridFromDepth().c_str()));
RTABMAP_PARAM(Grid,FootprintLength,float,0.0,"Footprint length used to filter points over the footprint of the robot.");
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_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_PARAM(Grid,ScanDecimation,int,1,uFormat("[%s=false] Decimation of the laser scan before creating cloud.",kGridFromDepth().c_str()));
RTABMAP_PARAM(Grid,CellSize,float,0.05,"Resolution of the occupancy grid.");
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_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_PARAM(Grid,NormalsSegmentation,bool,true,"Segment ground from obstacles using point normals, otherwise a fast passthrough is used.");
RTABMAP_PARAM(Grid,MaxGroundHeight,float,0.0,uFormat("Maximum ground height (0=disabled). Should be set if \"%s\" is false.",kGridNormalsSegmentation().c_str()));
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_PARAM(Grid,NormalK,int,20,uFormat("[%s=true] K neighbors to compute normals.",kGridNormalsSegmentation().c_str()));
RTABMAP_PARAM(Grid,ClusterRadius,float,0.1,uFormat("[%s=true] Cluster maximum radius.",kGridNormalsSegmentation().c_str()));
RTABMAP_PARAM(Grid,MinClusterSize,int,10,uFormat("[%s=true] Minimum cluster size to project the points.",kGridNormalsSegmentation().c_str()));
RTABMAP_PARAM(Grid,3D,bool,true,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 false.",kGridFromDepth().c_str()));
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 false.",kGridFromDepth().c_str()));
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_PARAM(Grid,NoiseFilteringRadius,float,0.0,"Noise filtering radius (0=disabled). Done after segmentation.");
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_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_PARAM(GridGlobal,FullUpdate,bool,true,"When the graph is changed, the whole map will be reconstructed instead of moving individually each cells of the map. Also, data added to cache won't be released after updating the map. This process is longer but more robust to drift that would erase some parts of the map when it should not.");
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_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_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_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_PARAM(Marker,VarianceLinear,float,0.001,"Linear variance to set on marker detections.");
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_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_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_PARAM(ImuFilter,MadgwickZeta,double,0.0,"Gyro drift gain (approx. rad/s), belongs in [-1, 1].");
RTABMAP_PARAM(ImuFilter,ComplementaryGainAcc,double,0.01,"Gain parameter for the complementary filter, belongs in [0, 1].");
RTABMAP_PARAM(ImuFilter,ComplementaryBiasAlpha,double,0.01,"Bias estimation gain parameter, belongs in [0, 1].");
RTABMAP_PARAM(ImuFilter,ComplementaryDoBiasEstimation,bool,true,"Parameter whether to do bias estimation or not.");
RTABMAP_PARAM(ImuFilter,ComplementaryDoAdpativeGain,bool,true,"Parameter whether to do adaptive gain or not.");
