ROS PCL visualizer fails on Turtlebot

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Hello,

I am trying to run PCL visualizer on the turtlebot's computer such that it will subscribe to the Kinect and display a point cloud, and request three floor points from the mouse. It will use this to create and visualize a point cloud that detects people. This is from a PCL tutorial. The code I have used to run this is below. When I run the code on a separate computer that is directly connected to a Kinect, the visualizer runs fine. However, when I run it on the turtlebot, the initial point cloud is displayed, but after I input the three points and run the second point cloud, it exits and displays the following error:

Illegal instruction (core dumped)

Any thoughts? Here is the code.

Thank you!

#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <ros/ros.h>
#include <pcl/console/parse.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>    
#include <pcl/io/openni_grabber.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/people/ground_based_people_detection_app.h>
#include <pcl/common/time.h>
#include <sensor_msgs/PointCloud2.h>
#include <pcl_conversions/pcl_conversions.h>

typedef pcl::PointXYZRGBA PointT;
typedef pcl::PointCloud<PointT> PointCloudT;

// PCL viewer //
pcl::visualization::PCLVisualizer viewer("PCL Viewer");

// Mutex: //
boost::mutex cloud_mutex;
bool new_cloud_available_flag;
enum { COLS = 640, ROWS = 480 };

int print_help()
{
  cout << "*******************************************************" << std::endl;
  cout << "Ground based people detection app options:" << std::endl;
  cout << "   --help    <show_this_help>" << std::endl;
  cout << "   --svm     <path_to_svm_file>" << std::endl;
  cout << "   --conf    <minimum_HOG_confidence (default = -1.5)>" << std::endl;
  cout << "   --min_h   <minimum_person_height (default = 1.3)>" << std::endl;
  cout << "   --max_h   <maximum_person_height (default = 2.3)>" << std::endl;
  cout << "   --sample  <sampling_factor (default = 1)>" << std::endl;
  cout << "*******************************************************" <<std::endl;
}

 PointCloudT::Ptr cloud(new PointCloudT);

void callback(const sensor_msgs::PointCloud2ConstPtr& msg){
   cloud_mutex.lock();
   sensor_msgs::PointCloud2 msg0 = *msg;
   PointCloudT cloud0;
   pcl::fromROSMsg(msg0, cloud0);
   *cloud = cloud0;
   new_cloud_available_flag = true;
   cloud_mutex.unlock();
 }

struct callback_args{
  // structure used to pass arguments to the callback function
  PointCloudT::Ptr clicked_points_3d;
  pcl::visualization::PCLVisualizer::Ptr viewerPtr;
};

void
pp_callback (const pcl::visualization::PointPickingEvent& event, void* args)
{
  struct callback_args* data = (struct callback_args *)args;
  if (event.getPointIndex () == -1)
    return;
  PointT current_point;
  event.getPoint(current_point.x, current_point.y, current_point.z);
  data->clicked_points_3d->points.push_back(current_point);
  // Draw clicked points in red:
  pcl::visualization::PointCloudColorHandlerCustom<PointT> red (data->clicked_points_3d, 255, 0, 0);
  data->viewerPtr->removePointCloud("clicked_points");
  data->viewerPtr->addPointCloud(data->clicked_points_3d, red, "clicked_points");
  data->viewerPtr->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 10, "clicked_points");
  std::cout << current_point.x << " " << current_point.y << " " << current_point.z << std::endl;
}

int main (int argc, char** argv)
{
  ros::init(argc, argv, "viewer_node");
  ros::NodeHandle n;
  new_cloud_available_flag = false;

  ros::Subscriber sub = n.subscribe<sensor_msgs::PointCloud2>("/camera/depth_registered/points", 1000, callback);

  if(pcl::console::find_switch (argc, argv, "--help") || pcl::console::find_switch (argc, argv, "-h"))
        return print_help();

  // Algorithm parameters:
  std::string svm_filename = "/usr/share/doc/libpcl-1.7-doc/doc/pcl-1.7/tutorials/sources/ground_based_rgbd_people_detection/data/trainedLinearSVMForPeopleDetectionWithHOG.yaml";
  float min_confidence = -1.5;
  float min_height = 1.3;
  float max_height = 2.3;
  float voxel_size = 0.06;
  float sampling_factor = 1;
  Eigen::Matrix3f rgb_intrinsics_matrix;
  rgb_intrinsics_matrix << 525, 0.0, 319.5, 0.0, 525, 239.5, 0.0, 0.0, 1.0; // Kinect RGB camera intrinsics ...