diff --git a/include/structs_definitions.h b/include/structs_definitions.h
index 05773cace21782c672405d860e3e678283e9ae30..5263d124b4b8300858ec0558726c206c14e0da5d 100644
--- a/include/structs_definitions.h
+++ b/include/structs_definitions.h
@@ -10,6 +10,10 @@
#include <pcl/point_types.h>
+const int TITS_EVALUATION = 0;
+const int TRAVEL_EVALUATION = 1;
+const int KATA_EVALUATION = 2;
+
const float OUT_OF_RANGE = 1000.0;
const float MAX_RANGE = 100.0;
@@ -94,6 +98,10 @@ const uint8_t R_CLASS_KITTI_OBSTACLE = 0;
const uint8_t G_CLASS_KITTI_OBSTACLE = 255;
const uint8_t B_CLASS_KITTI_OBSTACLE = 255;
+const uint8_t R_CLASS_NON_TRAVERSABLE = 255;
+const uint8_t G_CLASS_NON_TRAVERSABLE = 0;
+const uint8_t B_CLASS_NON_TRAVERSABLE = 255;
+
// Key obstacles like cars, pedestrians and so on are the rest of the classes so we will not explicitly write them here
//Custom labels not present in Kitti semantic
diff --git a/src/kf_based_terrain_analysis.cpp b/src/kf_based_terrain_analysis.cpp
index 1e3f454116f650309f37d17ca1eeaa94e45ea415..d8ab35d0a83ba8629a6f37b44cf251433de9b737 100644
--- a/src/kf_based_terrain_analysis.cpp
+++ b/src/kf_based_terrain_analysis.cpp
@@ -893,9 +893,9 @@ void CKf_Based_Terrain_Analysis::createDenseGroundCloud(
float min_std_dev = 1000.0;
float max_std_dev = -1.0;
- for (float i = -40.0; i < 40.0; i += 0.2)
+ for (float i = -80.0; i < 80.0; i += 0.4)
{
- for (float j = -40.0; j < 40.0; j += 0.2)
+ for (float j = -80.0; j < 80.0; j += 0.4)
{
pcl::PointXYZRGBNormal point;
point.x = i;
@@ -990,11 +990,11 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
for (pcl::PointCloud<pcl::PointXYZRGBNormal>::iterator i = elevation_cloud_ptr->begin();
i != elevation_cloud_ptr->end(); ++i)
{
- pcl::PointXYZRGBNormal point_in_sensor_frame = *i;
+ pcl::PointXYZRGBNormal elevation_cloud_point = *i;
// We pick the reference that made the best prediction for this point in the elevation cloud
int reference_index = (int)std::floor(
- point_in_sensor_frame.data_c[DATA_C_2_INDEX_OF_GROUND_REF_THAT_MADE_THE_LABEL]);
+ elevation_cloud_point.data_c[DATA_C_2_INDEX_OF_GROUND_REF_THAT_MADE_THE_LABEL]);
if (reference_index != INDEX_UNKNOWN)
{
@@ -1006,9 +1006,9 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
// ground classification
std::vector<int> features_indexes_in_pcl_cloud_ptr;
- std::vector<double> rgb_values;
- std::vector<double> hsv_values;
- std::vector<double> local_binary_patterns;
+ std::vector<double> rgb_values; // Currently not in use, but we left it here for future research in sensor fusion
+ std::vector<double> hsv_values; // Currently not in use, but we left it here for future research in sensor fusion
+ std::vector<double> local_binary_patterns; // Currently not in use, but we left it here for future research in sensor fusion
std::vector<double> squared_distances;
std::vector<double> incidence_angles;
@@ -1020,7 +1020,7 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
// 2) We point to the vector of indices related to the elevation_cloud point under evaluation
std::vector<std::vector<int>>::const_iterator index_iterator = correspondence_indexes.begin()
- + point_in_sensor_frame.data_c[DATA_C_3_ORIGINAL_INDEX];
+ + elevation_cloud_point.data_c[DATA_C_3_ORIGINAL_INDEX];
// and iterate over this vector
for (std::vector<int>::const_iterator point_iterator = index_iterator->begin();
@@ -1050,9 +1050,9 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
features_indexes_in_pcl_cloud_ptr.push_back(*point_iterator); // Index to modify the classification in a later step
//std::cout << "Index saved = " << *point_iterator << std::endl;
- rgb_values.push_back(point_to_evaluate.data_n[DATA_N_1_INTENSITY_VARIANCE]); // Feature!
- hsv_values.push_back(point_to_evaluate.data_n[DATA_N_2_Z_MEAN]); // Feature!
- local_binary_patterns.push_back(point_to_evaluate.data_n[DATA_N_3_Z_VARIANCE]); // Feature!
+ rgb_values.push_back(point_to_evaluate.data_n[DATA_N_1_INTENSITY_VARIANCE]); // Feature! // Currently not in use, but we left it here for future research in sensor fusion
+ hsv_values.push_back(point_to_evaluate.data_n[DATA_N_2_Z_MEAN]); // Feature! // Currently not in use, but we left it here for future research in sensor fusion
+ local_binary_patterns.push_back(point_to_evaluate.data_n[DATA_N_3_Z_VARIANCE]); // Feature! // Currently not in use, but we left it here for future research in sensor fusion
squared_distances.push_back(
(double)(point_to_evaluate.x * point_to_evaluate.x + point_to_evaluate.y * point_to_evaluate.y
@@ -1084,7 +1084,9 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
}
else
{
- if ((pcl_cloud_ptr->points[*point_iterator].z - point_in_sensor_frame.z)
+ //if ((pcl_cloud_ptr->points[*point_iterator].z - elevation_cloud_point.z)
+ // < filtering_configuration.robot_height)
+ if (euclideanDistance(reference_in_sensor_frame, pcl_cloud_ptr->points[*point_iterator])
< filtering_configuration.robot_height)
{
// TODO: when passing the point cloud through a ROS topic,
@@ -1128,7 +1130,7 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
&& (filtering_configuration.use_neural_network
|| filtering_configuration.extract_data_to_external_training_of_the_network))
{
- int total_num_of_points = correspondence_indexes[point_in_sensor_frame.data_c[DATA_C_3_ORIGINAL_INDEX]].size();
+ int total_num_of_points = correspondence_indexes[elevation_cloud_point.data_c[DATA_C_3_ORIGINAL_INDEX]].size();
//std::cout << "total_num_of_points = " << total_num_of_points << std::endl;
double ratio_ground_points = (double)num_of_ground_points / (double)total_num_of_points; // Feature!
@@ -1220,63 +1222,80 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
int predicted_class = nn_.predict(features);
- if (predicted_class == CLASS_OBSTACLE)
+ switch (nn_.number_of_neurons_in_output_layer_)
{
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
- (float)predicted_class + 0.0;
+ case (2):
+ if (predicted_class == CLASS_OBSTACLE)
+ {
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
+ (float)predicted_class + 0.0;
+
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_NON_TRAVERSABLE;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_NON_TRAVERSABLE;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_NON_TRAVERSABLE;
+ }
+ else
+ {
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
+ (float)predicted_class + (float)scores[n];
+
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_GROUND;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_GROUND;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_GROUND;
+ }
+ //std::cout << "Class + score = " << pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] << std::endl;
+ break;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_OBSTACLE;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_OBSTACLE;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_OBSTACLE;
- }
- else
- {
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
- (float)predicted_class + (float)scores[n];
+ case (5):
+ switch (predicted_class)
+ {
+ case ROAD:
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_ROAD;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_ROAD;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_ROAD;
+ //std::cout << "NN says point is road!" << std::endl;
+ break;
+
+ case SIDEWALK:
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_SIDEWALK;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_SIDEWALK;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_SIDEWALK;
+ //std::cout << "NN says point is sidewalk!" << std::endl;
+ break;
+
+ case TERRAIN:
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_TERRAIN;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_TERRAIN;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_TERRAIN;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
+ (float)CLASS_OBSTACLE + 0.0;
+ //std::cout << "NN says point is terrain!" << std::endl;
+ break;
+
+ case VEGETATION:
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_VEGETATION;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_VEGETATION;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_VEGETATION;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
+ (float)CLASS_OBSTACLE + 0.0;
+ //std::cout << "NN says point is vegetation!" << std::endl;
+ break;
+
+ case CLASS_OBSTACLE:
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_KITTI_OBSTACLE;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_KITTI_OBSTACLE;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_KITTI_OBSTACLE;
+ pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] =
+ (float)CLASS_OBSTACLE + 0.0;
+ //std::cout << "NN says point is Obstacle!" << std::endl;
+ break;
+ }
+ break;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_GROUND;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_GROUND;
- pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_GROUND;
+ default:
+ assert("ERROR in CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel: Unrecognized number of outputs in Shallow Neural Network" && false);
+ break;
}
- //std::cout << "Class + score = " << pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].data_c[DATA_C_1_ID_CLASS] << std::endl;
-
-// switch (predicted_class)
-// {
-// case ROAD:
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_ROAD;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_ROAD;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_ROAD;
-// //std::cout << "NN says point is road!" << std::endl;
-// break;
-//
-// case SIDEWALK:
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_SIDEWALK;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_SIDEWALK;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_SIDEWALK;
-// //std::cout << "NN says point is sidewalk!" << std::endl;
-// break;
-//
-// case TERRAIN:
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_TERRAIN;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_TERRAIN;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_TERRAIN;
-// //std::cout << "NN says point is terrain!" << std::endl;
-// break;
-//
-// case VEGETATION:
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_VEGETATION;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_VEGETATION;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_VEGETATION;
-// //std::cout << "NN says point is vegetation!" << std::endl;
-// break;
-//
-// case CLASS_OBSTACLE:
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].r = R_CLASS_KITTI_OBSTACLE;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].g = G_CLASS_KITTI_OBSTACLE;
-// pcl_cloud_ptr->points[features_indexes_in_pcl_cloud_ptr[n]].b = B_CLASS_KITTI_OBSTACLE;
-// //std::cout << "NN says point is Obstacle!" << std::endl;
-// break;
-// }
}
}
@@ -1369,79 +1388,80 @@ void CKf_Based_Terrain_Analysis::fastLabelPointcloudUsingGroundModel(
}
else // if we don't have enough information to try to predict the ground level at this point
{
- if (filtering_configuration.classify_not_labeled_points_as_obstacles)
- {
- std::vector<std::vector<int>>::const_iterator index_iterator = correspondence_indexes.begin()
- + point_in_sensor_frame.data_c[DATA_C_3_ORIGINAL_INDEX];
+ std::vector<std::vector<int>>::const_iterator index_iterator = correspondence_indexes.begin()
+ + elevation_cloud_point.data_c[DATA_C_3_ORIGINAL_INDEX];
- for (std::vector<int>::const_iterator point_iterator = index_iterator->begin();
- point_iterator != index_iterator->end(); ++point_iterator)
- {
- // We now copy the data that is common to every point in the vector (because are statistics extracted
- // from the points in the vector)
-
- // in data_n[0] we have the point intensity, so nothing to change
- // in data_n[1] we will store the intensity variance in the elevation cloud cell
- pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_1_INTENSITY_VARIANCE] =
- point_in_sensor_frame.data_n[DATA_N_1_INTENSITY_VARIANCE];
-
- // in data_n[2] we store the mean z value in the elevation cell
- pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_2_Z_MEAN] =
- point_in_sensor_frame.data_n[DATA_N_2_Z_MEAN];
-
- // in data_n[3] we store the z variance in the elevation cell
- pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_3_Z_VARIANCE] =
- point_in_sensor_frame.data_n[DATA_N_3_Z_VARIANCE];
-
- // TODO: we copy the mean intensity value in the channel reserved for the color, we do this because
- // we have detected a problem with pcl conversions: when passing the point cloud through a ROS topic,
- // channels c2 and c3 get corrupted, so we avoid to use them, we will investigate this problem in the
- // future (hopefully)
- // pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_2_MEAN_INTENSITY] =
- // point_in_sensor_frame.data_n[DATA_N_0_INTENSITY];
+ for (std::vector<int>::const_iterator point_iterator = index_iterator->begin();
+ point_iterator != index_iterator->end(); ++point_iterator)
+ {
+ // We now copy the data that is common to every point in the vector (because are statistics extracted
+ // from the points in the vector)
+
+ // in data_n[0] we have the point intensity, so nothing to change
+ // in data_n[1] we will store the intensity variance in the elevation cloud cell
+ pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_1_INTENSITY_VARIANCE] =
+ elevation_cloud_point.data_n[DATA_N_1_INTENSITY_VARIANCE];
+
+ // in data_n[2] we store the mean z value in the elevation cell
+ pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_2_Z_MEAN] = elevation_cloud_point.data_n[DATA_N_2_Z_MEAN];
+
+ // in data_n[3] we store the z variance in the elevation cell
+ pcl_cloud_ptr->points[*point_iterator].data_n[DATA_N_3_Z_VARIANCE] =
+ elevation_cloud_point.data_n[DATA_N_3_Z_VARIANCE];
+
+ // TODO: we copy the mean intensity value in the channel reserved for the color, we do this because
+ // we have detected a problem with pcl conversions: when passing the point cloud through a ROS topic,
+ // channels c2 and c3 get corrupted, so we avoid to use them, we will investigate this problem in the
+ // future (hopefully)
+ // pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_2_MEAN_INTENSITY] =
+ // elevation_cloud_point.data_n[DATA_N_0_INTENSITY];
// pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_0_RGB_CAST_INTO_FLOAT] =
-// point_in_sensor_frame.data_n[DATA_N_0_INTENSITY];
-
- if ((pcl_cloud_ptr->points[*point_iterator].z - point_in_sensor_frame.z)
+// elevation_cloud_point.data_n[DATA_N_0_INTENSITY];
+ if (filtering_configuration.classify_not_labeled_points_as_obstacles)
+ {
+ if ((pcl_cloud_ptr->points[*point_iterator].z - elevation_cloud_point.z)
< filtering_configuration.ground_threshold_in_not_analyzed_areas)
{
// TODO: when passing the point cloud through a ROS topic,
// channels c2 and c3 get corrupted, so we avoid to use them, we are temporarily the c0 channel
// which is the one storing RGB values packed as a float, so colour is disabled at the moment
- pcl_cloud_ptr->points[*point_iterator].r = 255;//R_CLASS_GROUND; // We use instead the r g b channels directly
- pcl_cloud_ptr->points[*point_iterator].g = 255;//G_CLASS_GROUND;
- pcl_cloud_ptr->points[*point_iterator].b = 0;//B_CLASS_GROUND;
+ pcl_cloud_ptr->points[*point_iterator].r = R_CLASS_GROUND; // 255 // We use instead the r g b channels directly
+ pcl_cloud_ptr->points[*point_iterator].g = G_CLASS_GROUND; // 0
+ pcl_cloud_ptr->points[*point_iterator].b = B_CLASS_GROUND; // 255
float score = 0.0; // we don't have too much confidence in these points, because they are not analyzed
pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_1_ID_CLASS] = (float)CLASS_GROUND + score;
}
- else if ((pcl_cloud_ptr->points[*point_iterator].z - point_in_sensor_frame.z)
+ else if ((pcl_cloud_ptr->points[*point_iterator].z - elevation_cloud_point.z)
< filtering_configuration.robot_height)
{
- // TODO: when passing the point cloud through a ROS topic,
- // channels c2 and c3 get corrupted, so we avoid to use them, we are temporarily the c0 channel
- // which is the one storing RGB values packed as a float, so colour is disabled at the moment
- pcl_cloud_ptr->points[*point_iterator].r = 255;//R_CLASS_OBSTACLE;
- pcl_cloud_ptr->points[*point_iterator].g = 255;//G_CLASS_OBSTACLE;
- pcl_cloud_ptr->points[*point_iterator].b = 0;//B_CLASS_OBSTACLE;
+ pcl_cloud_ptr->points[*point_iterator].r = R_CLASS_OBSTACLE; // 255
+ pcl_cloud_ptr->points[*point_iterator].g = G_CLASS_OBSTACLE; // 0
+ pcl_cloud_ptr->points[*point_iterator].b = B_CLASS_OBSTACLE; // 255
float score = 0.0;
pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_1_ID_CLASS] = (float)CLASS_OBSTACLE + score;
}
else
{
- // TODO: when passing the point cloud through a ROS topic,
- // channels c2 and c3 get corrupted, so we avoid to use them, we are temporarily the c0 channel
- // which is the one storing RGB values packed as a float, so colour is disabled at the moment
- pcl_cloud_ptr->points[*point_iterator].r = 255;//R_CLASS_OVERHANGING_OBSTACLE;
- pcl_cloud_ptr->points[*point_iterator].g = 255;//G_CLASS_OVERHANGING_OBSTACLE;
- pcl_cloud_ptr->points[*point_iterator].b = 0;//B_CLASS_OVERHANGING_OBSTACLE;
+ pcl_cloud_ptr->points[*point_iterator].r = R_CLASS_OVERHANGING_OBSTACLE; // 255
+ pcl_cloud_ptr->points[*point_iterator].g = G_CLASS_OVERHANGING_OBSTACLE; // 0
+ pcl_cloud_ptr->points[*point_iterator].b = B_CLASS_OVERHANGING_OBSTACLE; // 255
float score = 0.0;
pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_1_ID_CLASS] = (float)CLASS_OVERHANGING_OBSTACLE
+ score;
}
}
+ else
+ {
+ pcl_cloud_ptr->points[*point_iterator].r = R_CLASS_UNKNOWN;
+ pcl_cloud_ptr->points[*point_iterator].g = G_CLASS_UNKNOWN;
+ pcl_cloud_ptr->points[*point_iterator].b = B_CLASS_UNKNOWN;
+
+ float score = 0.0;
+ pcl_cloud_ptr->points[*point_iterator].data_c[DATA_C_1_ID_CLASS] = (float)CLASS_UNKNOWN + score;
+ }
}
}
}