Using Kinect input instead of a .pcd file and viewing results in RVIZ? [closed]
I am trying to apply some filters and euclidean clustering to input from a Kinect sensor. Currently I am using a piece of code that calls in the "table_scene_lms400.pcd" but I would like to know how I can use input from a Kinect sensor rather than this .pcd file? I would also like to view the results in RVIZ? I know this involves subscribers and publishers and despite numerous attempts using tutorials, etc. I have not been able to accomplish this so was wondering if anybody has any code to do the above or could edit my own code/give me some tips to help me? I have included my code below. (I have tried following most online tutorials and guides but I am still struggling so any help would be greatly appreciated)
#include <ros/ros.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <sensor_msgs/PointCloud2.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
ros::Publisher pub;
int
main (int argc, char** argv)
{
ros::init(argc, argv, "perception_node");
ros::NodeHandle nh;
// Read in the cloud data
pcl::PCDReader reader;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
reader.read ("table_scene_lms400.pcd", *cloud);
std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*
// Create the filtering object: downsample the dataset using a leaf size of 1cm
pcl::VoxelGrid<pcl::PointXYZ> vg;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
vg.setInputCloud (cloud);
vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg.filter (*cloud_filtered);
std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; //*
// Create the segmentation object for the planar model and set all the parameters
pcl::SACSegmentation<pcl::PointXYZ> seg;
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PCDWriter writer;
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (200);
seg.setDistanceThreshold (0.02);
int i=0, nr_points = (int) cloud_filtered->points.size ();
while (cloud_filtered->points.size () > 0.3 * nr_points)
{
// Segment the largest planar component from the remaining cloud
seg.setInputCloud (cloud_filtered);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
break;
}
// Extract the planar inliers from the input cloud
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers);
extract.setNegative (false);
// Get the points associated with the planar surface
extract.filter (*cloud_plane);
std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_f);
*cloud_filtered = *cloud_f ...