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(Kp,BadSignRatio,float,0.2,"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(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(SURF,Extended,bool,false,"Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).");
RTABMAP_PARAM(SURF,HessianThreshold,float,150.0,"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,10.0,"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(BRIEF,Bytes,int,32,"Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.");
RTABMAP_PARAM(FAST,Threshold,int,30,"Threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.");
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(ORB,NFeatures,int,500,"The maximum number of features to retain.");
RTABMAP_PARAM(ORB,ScaleFactor,float,1.2,"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,8,"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,31,"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(Bayes,FullPredictionUpdate,bool,true,"Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).");
RTABMAP_PARAM(VhEp,MatchCountMin,int,8,"Minimum of matching visual words pairs to accept the loop hypothesis.");
RTABMAP_PARAM(VhEp,RansacParam1,float,3.0,"Fundamental matrix (see cvFindFundamentalMat()): Max distance (in pixels) from the epipolar line for a point to be inlier.");
RTABMAP_PARAM(VhEp,RansacParam2,float,0.99,"Fundamental matrix (see cvFindFundamentalMat()): Performance of the RANSAC.");
// RGB-D SLAM
RTABMAP_PARAM(RGBD,Enabled,bool,true,"");
RTABMAP_PARAM(RGBD,ScanMatchingSize,int,0,"Laser scan matching history for odometry correction (laser scans are required). Set to 0 to disable odometry correction.");
RTABMAP_PARAM(RGBD,LinearUpdate,float,0.0,"Min linear displacement to update the map. Rehearsal is done prior to this, so weights are still updated.");
RTABMAP_PARAM(RGBD,AngularUpdate,float,0.0,"Min angular displacement to update the map. Rehearsal is done prior to this, so weights are still updated.");
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 mode 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,LocalLoopDetectionMaxDiffID,int,0,"Maximum ID difference between the current/last loop closure location and the local loop closure hypotheses. Set 0 to ignore.")
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,RefineIterations,int,5,"Number of iterations used to refine the transformation found by RANSAC. 0 means that the transformation is not refined.");
RTABMAP_PARAM(Odom,FeaturesRatio,float,0.5,"Minimum ratio of keypoints between the current image and the last image to compute odometry.");
// Odometry Bag-of-words
RTABMAP_PARAM(OdomBow,LocalHistorySize,int,1000,"Local history size: If > 0 (example 5000), the odometry will maintain a local map of X maximum words.");
