pcl supervoxel clustering: unable to visualise voxels - c++

Question

I am trying to incorporate supervoxel clustering into my 3D pointcloud application. However, the point clouds can't be visualised on RVIZ. When i tried debugging using gdb, i realised that my map object still has no data inside even after the line
std::multimap<uint32_t, uint32_t> supervoxel_adjacency; super.getSupervoxelAdjacency(supervoxel_adjacency);
The tutorial i am referencing is this https://pointclouds.org/documentation/tutorials/supervoxel_clustering.html
What is the problem? It should fill the map struct with data for me to iterate over but it isn't.
void PointCloudProcessor::voxel_clustering(type_pclcloudptr& input, ros::Publisher& Publisher)
{
float voxel_resolution = 0.008f;
float seed_resolution = 0.1f;
float color_importance = 0.2f;
float spatial_importance = 0.4f;
float normal_importance = 1.0f;
pcl::SupervoxelClustering<pcl::PointXYZ> super(voxel_resolution, seed_resolution);
super.setUseSingleCameraTransform(false);
super.setInputCloud(input);
super.setColorImportance(color_importance);
super.setSpatialImportance(spatial_importance);
super.setNormalImportance(normal_importance);
std::map <uint32_t, pcl::Supervoxel<pcl::PointXYZ>::Ptr > supervoxel_clusters;
if ( !( input->points.empty() ) )
{
super.extract(supervoxel_clusters);
type_pclcloudptr voxel_centroid_cloud = super.getVoxelCentroidCloud();
pcl::PointCloud<pcl::PointXYZL>::Ptr labeled_voxel_cloud = super.getLabeledVoxelCloud();
std::multimap<uint32_t, uint32_t> supervoxel_adjacency;
super.getSupervoxelAdjacency(supervoxel_adjacency);
// To make a graph of the supervoxel adjacency, we need to iterate through the supervoxel adjacency multimap
std::multimap<uint32_t, uint32_t>::iterator label_itr = supervoxel_adjacency.begin();
int idx = 0;
//PointCloudProcessor::mMarkerArray.markers.resize(supervoxel_adjacency.size());
for (; label_itr != supervoxel_adjacency.end(); )
{
//First get the label
uint32_t supervoxel_label = label_itr->first;
Eigen::Vector4f centroid;
Eigen::Vector4f min;
Eigen::Vector4f max;
//Now get the supervoxel corresponding to the label
pcl::Supervoxel<pcl::PointXYZ>::Ptr supervoxel = supervoxel_clusters.at(supervoxel_label);
//Now we need to iterate through the adjacent supervoxels and make a point cloud of them
type_pclcloudptr adjacent_supervoxel_centers;
std::multimap<uint32_t, uint32_t>::iterator adjacent_itr = supervoxel_adjacency.equal_range(supervoxel_label).first;
for (; adjacent_itr != supervoxel_adjacency.equal_range(supervoxel_label).second; ++adjacent_itr)
{
pcl::Supervoxel<pcl::PointXYZ>::Ptr neighbor_supervoxel = supervoxel_clusters.at(adjacent_itr->second);
pcl::PointXYZ neighbour_centroid_xyz;
pcl::PointXYZRGBA neighbour_centroid_xyzrgba = neighbor_supervoxel->centroid_;
pcl::copyPoint(neighbour_centroid_xyzrgba, neighbour_centroid_xyz);
adjacent_supervoxel_centers->push_back(neighbour_centroid_xyz);
}
// pcl::compute3DCentroid(*adjacent_supervoxel_centers,centroid);
// pcl::getMinMax3D(*adjacent_supervoxel_centers, min, max);
// PointCloudProcessor::mark_cluster(centroid,min, max, idx);
adjacent_supervoxel_centers->width = adjacent_supervoxel_centers->size ();
adjacent_supervoxel_centers->height = 1;
adjacent_supervoxel_centers->is_dense = true;
adjacent_supervoxel_centers->header.frame_id = "rearAxle";
Publisher.publish(adjacent_supervoxel_centers);
//Move iterator forward to next label
label_itr = supervoxel_adjacency.upper_bound(supervoxel_label);
idx++;
}
}
}