diff --git a/params/iri_ground_segmentation_no_random_forest.yaml b/params/iri_ground_segmentation_no_random_forest.yaml
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+iri_ground_segmentation: {
+  rate: 10.00,
+  
+  # Robot related geometric parameters
+  sensor_height: 1.73,      ## Ground is expected to be at z = -1*sensor_height
+  robot_height: 2.00,       ## All obstacle points above this value (w.r.t the z ground predicted by the algorithm) are treated as overhanging obstacles 
+
+  # Parameters affecting the exploration of the pointcloud
+  ROI_delta_x_and_y: 3.0,    ## This value sets the size of the ROIs: ROI_size = (2*ROI_delta_x_and_y)^2
+  ROI_shadow_area: 5.5,     ## This value is the same that the above, but only used in the root vertex to overcome the shadow area
+
+  ground_reference_search_resolution_deg: 40.0, #12.00, ## It is the angular resolution when generating new ground references (graph nodes),
+                                                        ## it will affect the number of nodes in the graph: lower values generate more nodes
+
+  elevation_grid_resolution: 2.1, #0.333, ## This value is used to create the "elevation point cloud" which is a reduction of the original pointcloud, where
+                                          ## only the lowest z values in each cell are stored (along with a vector of indexes pointing to the remaining points
+                                          ## in the cell, so that the original pointcloud can be recovered or labeled using the info in the "elevation cloud").
+                                          ## Big values can speed up the algorithm but generates a lower resolution ground model, on the other hand, small values
+                                          ## can produce outliers because we would like to have only ground points in the elevation cloud (however these outliers
+                                          ## are usually succesfully rejected by the mahalanobis threshold and the "prior" information)
+
+  # Kalman filter noise parameters
+  ## initial uncertainties
+  z_initial_std_dev: 0.05,          ## Uncertainty in z = -1*sensor_height
+  initial_angular_std_dev_deg: 1.5, ## Used to initialize x_slope and y_slope variances
+
+  ## propagation additive noises
+  propagation_z_additive_noise_per_meter:   0.01, ## Uncertainty added to var_z as a function of the distance with the parent vertex
+  propagation_additive_noise_deg_per_meter: 0.4,  ## Uncertainty added to x_slope and y_slope as a function of the distance with the parent vertex 
+                                                  ## (it is expressed in degrees per meter, but inside the code it is converted to slope per meter)
+  # measurement noise
+  z_observation_std_dev: 0.15,              ## Uncertainty in the Lidar observations
+
+  # threshold for outlier rejection and classification
+  mahalanobis_threshold: 2.7,       ## To classify points as obstacles or ground (a small value will cause false obstacle points, 
+                                    ## and a big value will increase the number of false ground points)
+
+  # labeling parameters
+  number_of_references_used_for_labelling: 0,            ## used to evaluate each elevation point cloud from different POVs // NEW!! value of zero enables fast labelling mode!! \m/
+  max_pred_std_dev_for_labelling: 0.5,                   ## ONLY IN USE TO GIVE COLOUR TO DENSE RECONSTRUCTION
+  score_threshold: 0.0,                                  ## for assigning ground class label: one means maha. dist. equal to zero, zero means mahalanobis dist equal to maha. thres
+  classify_not_labeled_points_as_obstacles: true,        ## when a point has no reference satisfying the max_pred_std_dev_for_labelling threshold we can leave as unknown or label it as obstacle
+  ground_threshold_in_not_analyzed_areas: 0.1,           ## when it is not possible to make a local analysis of the data, we will use the lowest point (the one in elevation_cloud) as ground height, and
+                                                         ## all the points above it and below this threshold will be classified as ground 
+  discard_not_connected_references_for_labelling: false, ## NOT IN USE
+
+  # visualization and debug parameters
+  measure_performance: false,            ## (feature still not debugged) Flag to measure number of execution and execution times of the different functions of the algorithm 
+  show_dense_reconstruction: false,      ## To show a dense ground surface reconstruction using the predictions of the ground mode (colored using the std_dev of z coordinate)   
+                                         ## or alternatively the "elevation point cloud" (useful for parameter tunning) 
+}