a gesture control project for Parrot's ARDrone developed by first-year students at Supélec, MALIS Lab.
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Gesture control for drone with onboard camera

a gesture control project for Parrot's ARDrone developed by first-year students at Supélec, MALIS Lab.

Overall Architecture



  • The project includes the source code of ardrone_autonomy which should be able to compile with catkin_make.
  • The project is built upon ros-hydro and compile with catkin_make. More explanation and tutorial can be found here. Choose catkin and hydro when asked.
  • The project makes use of gngt whose implementation is based on robia/vq2-1.33 and adapted in ros/catkin_ws/src/robia/AI.h and .cpp. Details of the algorithms and how to compile from source can be found here. This implementation is developped by M.Frezza-Buet Herve at Supélec Metz.
  • The project depends on dye color filter, whose source is avaiable here. A port to OpenCV is avaiable at robia/tracker. tracker is a standalone application that can be run with standard opencv compilation flags, a makefile is avaiable in robia/tracker as well.


The source-code developped for this project is available in robia/ros. Other subdirectories are libraries projects that we have used in our project.

You can clone the whole repository to use it.

git clone https://github.com/liusiqi43/robia.git

Once cloned and properly configured, you need to run


to compile and

roslaunch robia ros.launch

to run.

This assumes that you are connected to the ParrotAR drone via wifi successfully. Multiples nodes would be launched:

	<node name="ardrone_driver" pkg="ardrone_autonomy" type="ardrone_driver" output="screen"/>
	<node name="keyboard_cmd" pkg="robia" type="keyboard_cmd" output="screen"/>
	<node name="AI" pkg="robia" type="AI" output="screen"/>
	<node name="Control" pkg="robia" type="Control" output="screen"/>
	<node name="image_view" pkg="image_view" type="image_view">
		<remap from="image" to="/robia/gngt_output"/>
  1. ardrone_driver is based on open-source project avaiable here. It's the bridge between drone and other components in the program.
  2. keyboard_cmd is developped for control over keyboard
  3. AI includes image processing (color filter, denoising using erosion and dilatation) as well as vectorial quantification (gngt). The output of this node is a gesture representation.
  4. Control reacts to different gesture and send commands back to drone.
  5. image_view is a real-time visualiser of the drone's vision and the result of calculation done by AI node.


A video demo of our first attemps is available here.


Related documents are available in doc/, including slides and report both in French.