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)
