diff --git a/cfg/GroundSegmentation.cfg b/cfg/GroundSegmentation.cfg
index b074ada0832caaeb941852e0c686657c1a80f027..6505d99ad9a14d68729b4a94ddec1e317fb56a22 100644
--- a/cfg/GroundSegmentation.cfg
+++ b/cfg/GroundSegmentation.cfg
@@ -68,6 +68,9 @@ gen.add("use_neural_network", bool_t, 0, "uses neural network to segmentate grou
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);
diff --git a/params/iri_ground_segmentation.yaml b/params/iri_ground_segmentation.yaml
index 333ac40bd2f199fcae8de5811def8bf8833aafaa..d70c1265058182369b61b554499b80dd1da5b535 100644
--- a/params/iri_ground_segmentation.yaml
+++ b/params/iri_ground_segmentation.yaml
@@ -7,12 +7,12 @@ iri_ground_segmentation: {
# 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
@@ -38,14 +38,17 @@ iri_ground_segmentation: {
# Neural Network related parameters
use_neural_network: true,
extract_data_to_external_training_of_the_network: false,
- dataset_filename_with_global_path: '/home/idelpino/Documentos/dataset.txt',
- neural_net_filename_with_global_path: '/media/sf_virtual_box_shared/ten_neurons_sse.csv',
+ dataset_filename_with_global_path: '/home/idelpino/Documentos/dataset_rgb_hsv_olbp_10_frame_inc.csv',
+ neural_net_filename_with_global_path: '/media/sf_virtual_box_shared/neural_networks/five_classes_13_features_39_neurons.csv',
+ neural_net_number_of_features: 13,
+ neural_net_number_of_neurons_in_hidden_layer: 39,
+ neural_net_number_of_neurons_in_output_layer: 5,
# labeling parameters
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
# visualization and debug parameters
diff --git a/src/ground_segmentation_alg_node.cpp b/src/ground_segmentation_alg_node.cpp
index 1b86f967b696eeec78e2f0407bebb601090bbf88..a79fb569f59ab8515d094a77726d126f6f86c2cd 100644
--- a/src/ground_segmentation_alg_node.cpp
+++ b/src/ground_segmentation_alg_node.cpp
@@ -151,6 +151,36 @@ GroundSegmentationAlgNode::GroundSegmentationAlgNode(void) :
this->alg_.filtering_configuration_.neural_net_filename_with_global_path =
config_.neural_net_filename_with_global_path;
+ if (!this->private_node_handle_.getParam("neural_net_number_of_features",
+ this->config_.neural_net_number_of_features))
+ {
+ ROS_WARN(
+ "GroundSegmentationAlgNode::GroundSegmentationAlgNode: param 'neural_net_number_of_features' not found");
+ }
+ else
+ this->alg_.filtering_configuration_.neural_net_number_of_features =
+ config_.neural_net_number_of_features;
+
+ if (!this->private_node_handle_.getParam("neural_net_number_of_neurons_in_hidden_layer",
+ this->config_.neural_net_number_of_neurons_in_hidden_layer))
+ {
+ ROS_WARN(
+ "GroundSegmentationAlgNode::GroundSegmentationAlgNode: param 'neural_net_number_of_neurons_in_hidden_layer' not found");
+ }
+ else
+ this->alg_.filtering_configuration_.neural_net_number_of_neurons_in_hidden_layer =
+ config_.neural_net_number_of_neurons_in_hidden_layer;
+
+ if (!this->private_node_handle_.getParam("neural_net_number_of_neurons_in_output_layer",
+ this->config_.neural_net_number_of_neurons_in_output_layer))
+ {
+ ROS_WARN(
+ "GroundSegmentationAlgNode::GroundSegmentationAlgNode: param 'neural_net_number_of_neurons_in_output_layer' not found");
+ }
+ else
+ this->alg_.filtering_configuration_.neural_net_number_of_neurons_in_output_layer =
+ config_.neural_net_number_of_neurons_in_output_layer;
+
////////////////// labeling parameters
if (!this->private_node_handle_.getParam("max_pred_std_dev_for_labelling",
this->config_.max_pred_std_dev_for_labelling))