gen.add("extract_data_to_external_training_of_the_network", bool_t, 0, "Extract features to external file to train with Matlab", False);
gen.add("dataset_filename_with_global_path", str_t, 0, "name of the file where dump the features for neural net training in matlab", "");
gen.add("neural_net_filename_with_global_path", str_t, 0, "name of the file containing the neural net weights", "");
gen.add("neural_net_number_of_features", int_t, 0, "Size of the input layer", 9, 1, 100);
gen.add("neural_net_number_of_neurons_in_hidden_layer", int_t, 0, "Size of the hidden layer", 25, 1, 100);
gen.add("neural_net_number_of_neurons_in_output_layer", int_t, 0, "Size of the output layer", 2, 1, 100);
# labeling parameters
gen.add("max_pred_std_dev_for_labelling", double_t, 0, "To give up trying to label ground points if we don't have enough confidence in our predictions", 0.3, 0.01, 1.0);
# 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
ROI_shadow_area:5.5,#6.0, #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),
ground_reference_search_resolution_deg:40.0,#20.0, #40 #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,#1.0 #0.5, ## This value is used to create the "elevation point cloud" which is a reduction of the original pointcloud, where
elevation_grid_resolution:2.1,#1.5, #2.1, #1.0 #0.5, ## 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
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:0.0,## 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
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
