We use the **[[Point Cloud Library PCL]][[http://pointclouds.org/documentation/]]**  that aims at detecting obstacles (with Kinect). 
====== Objectif ======

We catched the depth data and tranformed to point cloud. Then we filter that object which is higher than the ground. We projected this result to create a 2D image. By using this image, we can send back reference position of an obstacle.
  * You can also take a look to the tutorial:[[http://wiki.ros.org/pcl/Tutorials]]

Calibration Parameters for the calculation of 3D coordinates from the raw image measurements include: 
**The height of Kinect.** 
(In addition, we found that y-axis of the point cloud corresponds to the height, means the z-axis for a tf-tranform of Kinect Sensor)

===== package =====

The package: {{:pcl_detector.tar.gz|}}

1. Launch Kinect Sensor
  * //roslaunch openni_launch openni.launch//
2. Run 
  * //rosrun pcl_detector pcl_detector//

==== Simulation ====

You can simulate the result by using rviz:
  * //rosrun rviz rviz//

Choose the output topic "output" in type of "PointCloud2"