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#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <pcl/common/transforms.h>
#include <pcl/console/parse.h>
#include <pcl/console/print.h>
#include <pcl/io/pcd_io.h>
#include <iostream>
#include <flann/flann.h>
#include <flann/io/hdf5.h>
#include <pcl/features/vfh.h>
#include <pcl/features/normal_3d.h>
#include "ros/ros.h"
#include "sensor_msgs/PointCloud2.h"
#include <pcl/ModelCoefficients.h>
#include "vfh_recognition/Recognize.h"
#include <vfh_recognition/SupportClasses.h>
#include <tabletop_object_detector/TabletopSegmentation.h>
#include <tabletop_object_detector/TabletopObjectRecognition.h>
#include <boost/filesystem.hpp>
typedef std::pair<std::string, std::vector<float> > vfh_model;
std::vector<vfh_model> models;
flann::Matrix<int> k_indices;
flann::Matrix<float> k_distances;
flann::Matrix<float> *data;
//ros::Publisher recognized_pub;
sensor_msgs::PointCloud2 fromKinect;
ros::Publisher pub;
/** \brief Search for the closest k neighbors
* \param index the tree
* \param model the query model
* \param k the number of neighbors to search for
* \param indices the resultant neighbor indices
* \param distances the resultant neighbor distances
inline void
nearestKSearch (flann::Index<flann::ChiSquareDistance<float> > &index, const vfh_model &model,
int k, flann::Matrix<int> &indices, flann::Matrix<float> &distances)
// Query point
flann::Matrix<float> p = flann::Matrix<float>(new float[model.second.size ()], 1, model.second.size ());
memcpy (&p.ptr ()[0], &model.second[0], p.cols * p.rows * sizeof (float));
indices = flann::Matrix<int>(new int[k], 1, k);
distances = flann::Matrix<float>(new float[k], 1, k);
index.knnSearch (p, indices, distances, k, flann::SearchParams (512));
delete[] p.ptr ();
/** \brief Load the list of file model names from an ASCII file
* \param models the resultant list of model name
* \param filename the input file name
loadFileList (std::vector<vfh_model> &models, const std::string &filename)
std::ifstream fs; (filename.c_str ());
if (!fs.is_open () || ())
return (false);
std::string line;
while (!fs.eof ())
getline (fs, line);
if (line.empty ())
vfh_model m;
m.first = line;
models.push_back (m);
fs.close ();
return (true);
loadHist (const boost::filesystem::path &path, vfh_model &vfh)
int vfh_idx;
// Load the file as a PCD
sensor_msgs::PointCloud2 cloud;
int version;
Eigen::Vector4f origin;
Eigen::Quaternionf orientation;
pcl::PCDReader r;
int type; int idx;
r.readHeader (path.string (), cloud, origin, orientation, version, type, idx);
vfh_idx = pcl::getFieldIndex (cloud, "vfh");
if (vfh_idx == -1)
return (false);
if ((int)cloud.width * cloud.height != 1)
return (false);
catch (pcl::InvalidConversionException e)
return (false);
// Treat the VFH signature as a single Point Cloud
pcl::PointCloud <pcl::VFHSignature308> point;
pcl::io::loadPCDFile (path.string (), point);
vfh.second.resize (308);
std::vector <sensor_msgs::PointField> fields;
pcl::getFieldIndex (point, "vfh", fields);
for (size_t i = 0; i < fields[vfh_idx].count; ++i)
vfh.second[i] = point.points[0].histogram[i];
vfh.first = path.string ();
return (true);
loadFeatureModels (const boost::filesystem::path &base_dir, const std::string &extension,
std::vector<vfh_model> &models)
if (!boost::filesystem::exists (base_dir) && !boost::filesystem::is_directory (base_dir))
for (boost::filesystem::directory_iterator it (base_dir); it != boost::filesystem::directory_iterator (); ++it)
if (boost::filesystem::is_directory (it->status ()))
std::stringstream ss;
ss << it->path ();
pcl::console::print_highlight ("Loading %s (%lu models loaded so far).\n", ss.str ().c_str (), (unsigned long)models.size ());
loadFeatureModels (it->path (), extension, models);
if (boost::filesystem::is_regular_file (it->status ()) && boost::filesystem::extension (it->path ()) == extension)
vfh_model m;
if (loadHist (base_dir / it->path ().filename (), m))
models.push_back (m);
bool recognize_cb(tabletop_object_detector::TabletopObjectRecognition::Request &srv_request,
tabletop_object_detector::TabletopObjectRecognition::Response &srv_response)
//build kdtree index
flann::Index<flann::ChiSquareDistance<float> > index (*data, flann::LinearIndexParams ());
index.buildIndex ();
//clear any models in the response:
// Create the VFH estimation class, and pass the input dataset+normals to it
pcl::VFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::VFHSignature308> vfh;
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> clouds;
//Euclidean segmentation:
SegmentCloud(fromKinect, clouds);
//For storing results:
pcl::PointCloud<pcl::PointXYZ>::Ptr aligned_template (new pcl::PointCloud<pcl::PointXYZ>);
sensor_msgs::PointCloud2 recognized_msg;
Eigen::Matrix4f objectToView;
int numFound = 0;
std::cout << "Found " << clouds.size() << " clusters.\n";
//For each segment passed in:
for(unsigned int segment_it = 0; segment_it < clouds.size(); segment_it++){
vfh.setInputCloud (;
//Estimate normals:
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud (;
pcl::search::KdTree<pcl::PointXYZ>::Ptr treeNorm (new pcl::search::KdTree<pcl::PointXYZ> ());
ne.setSearchMethod (treeNorm);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
ne.setRadiusSearch (0.03);
ne.compute (*cloud_normals);
//VFH estimation
vfh.setInputNormals (cloud_normals);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
vfh.setSearchMethod (tree);
pcl::PointCloud<pcl::VFHSignature308>::Ptr vfhs (new pcl::PointCloud<pcl::VFHSignature308> ());
vfh.compute (*vfhs);
//Load histogram
vfh_model histogram;
for (size_t i = 0; i < 308; ++i)
histogram.second[i] = vfhs->points[0].histogram[i];
//Algorithm parameters
float thresh = 100; //similarity threshold
int k = 1; //number of neighbors
//KNN classification
flann::Index<flann::ChiSquareDistance<float> > index (*data, flann::LinearIndexParams ());
index.buildIndex ();
nearestKSearch (index, histogram, k, k_indices, k_distances);
//If model match is close enough, do finer pose estimation by RANSAC fitting.
if(k_distances[0][0] < thresh){
//Load nearest match
std::string cloud_name =[0][0]).first;
//Extract object label and view number from file name:
cloud_name.erase(cloud_name.end()-8, cloud_name.end()-4);
std::string recognitionLabel, viewNumber;
recognitionLabel.assign(cloud_name.begin()+5, cloud_name.end()-6);
viewNumber.assign(cloud_name.end()-5, cloud_name.end()-4);
//ROS_INFO the recognitionLabel and view number. This info will later be used to look the object up in a manipulation database.
ROS_INFO("%s", recognitionLabel.c_str());
ROS_INFO("%i", atoi(viewNumber.c_str()));
//Here, objectToView is the transformation of the detected object to its nearest viewpoint in the database.
//To get the object pose in the world frame: T(camera_to_world)*T(training_view_to_camera)*objectToView.
//The transformation to camera coordinates would then happen here by finding T(view_cam) in a lookup table and premultiplying
//by objectToView
alignTemplate(, cloud_name, aligned_template, objectToView);
//Convert rotational component of objectToView to Quaternion for messaging:
Eigen::Matrix3f rotation = objectToView.block<3,3>(0, 0);
Eigen::Quaternionf rotQ(rotation);
//Set num models in request:
srv_request.num_models = numFound;
//Build service response:
srv_response.models[numFound-1].model_list[0].model_id = atoi(viewNumber.c_str()); //This ID will eventually correspond to the object label.
srv_response.models[numFound-1].model_list[0].pose.header.seq = 1; //Don't know what this is, but it's set.
srv_response.models[numFound-1].model_list[0].pose.header.stamp = fromKinect.header.stamp;
srv_response.models[numFound-1].model_list[0].pose.header.frame_id = "/camera_depth_optical_frame";
srv_response.models[numFound-1].model_list[0].pose.pose.position.x = objectToView(0,3);
srv_response.models[numFound-1].model_list[0].pose.pose.position.y = objectToView(1,3);
srv_response.models[numFound-1].model_list[0].pose.pose.position.z = objectToView(2,3);
srv_response.models[numFound-1].model_list[0].pose.pose.orientation.x = rotQ.x();
srv_response.models[numFound-1].model_list[0].pose.pose.orientation.y = rotQ.y();
srv_response.models[numFound-1].model_list[0].pose.pose.orientation.z = rotQ.z();
srv_response.models[numFound-1].model_list[0].pose.pose.orientation.w = rotQ.w();
//confidence and cluster_model_indcices are not currently used.
}//end threshold if statement
}//end segment iterator
void kinect_cb(const sensor_msgs::PointCloud2 inCloud)
fromKinect = inCloud;
int main(int argc, char **argv)
ros::init(argc, argv, "recongition_node");
ros::NodeHandle n;
loadFeatureModels ("data", ".pcd", models);
pcl::console::print_highlight ("Loaded %d VFH models. Creating training data\n",
(int)models.size ());
// Convert data into FLANN format
data = new flann::Matrix<float> (new float[models.size () * models[0].second.size ()], models.size (), models[0].second.size ());
std::cout << "data size: [" << data->rows << " , " << data->cols << "]\n";
for (size_t i = 0; i < data->rows; ++i)
for (size_t j = 0; j < data->cols; ++j)
*(data->ptr()+(i*data->cols + j)) = models[i].second[j];
pcl::console::print_error ("Training data loaded.\n");
ros::Subscriber sub = n.subscribe("/camera/depth_registered/points", 1, kinect_cb);
ros::ServiceServer serv = n.advertiseService("/object_recognition", recognize_cb);
ROS_INFO("Recognition node ready.");
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