The simulation has been developed and primarily used on Ubuntu 20.04. Other Linux distributions were not tested. Additionally, ROS Installation must be installed.


The first step is of course to clone the repository. These are some ways to get it:

  • KITcar internal. Clone this repository in the same directory as kitcar-ros.

    git clone $KITCAR_REPO_PATH/kitcar-gazebo-simulation


The environment variable $KITCAR_REPO_PATH must contain the directory in which you’ve cloned kitcar-gazebo-simulation.

Make sure that $KITCAR_REPO_PATH is set to the directory where you’ve cloned kitcar-gazebo-simulation into:

cd $KITCAR_REPO_PATH/kitcar-gazebo-simulation

should put you into the root directory of kitcar-gazebo-simulation.

If it doesn’t work, create the variable with:

export KITCAR_REPO_PATH=<DIRECTORY WHERE kitcar-gazebo-simulation IS>


export KITCAR_REPO_PATH=<DIRECTORY WHERE kitcar-gazebo-simulation IS>

to your .bashrc ensures that the path is always set.


Ensure that your Python-Version is 3.8 or larger:

python3 -V

If not, upgrade your system to Python 3.8. (KITcar internal: Goto KITcar-Gazebo-Simulation Python )


ROS must also be installed on your machine. If it’s not yet installed, follow the installation guide.


To install required packages run the init script. The packages are installed for the current user. Change into kitcar-gazebo-simulation folder and run the script:

cd $KITCAR_REPO_PATH/kitcar-gazebo-simulation

(Ubuntu 18.04: Ignore any error thrown by pip when trying to install pygobject, it seems to be irrelevant.)

If you want to, you can also install packages that are used to run machine learning tasks or compile the documentation. But these packages are not needed to run all basic components of the simulation.


Then build kitcar-gazebo-repository by calling catkin_make in the simulation folder of this repository.

Data Version Control (DVC)

Datasets, machine learning models and similar are stored within a DVC container. DVC is designed to bring version control to the world of machine learning and large datasets. It integrates well with git. If you have set up your credentials as described in our WIKI , you can download all our datasets (very large, approx. 10 Gb) just execute:

dvc pull

This is only necessary if you want to train or test our neural networks.

If you are not part of KITcar, you cannot download our datasets. However, you can either setup your DVC server or simply use DVC locally. See for more details. Our pipelines used to train and test models or extract datasets from the simulation will still work.


This is only required by some scripts to record rosbags in the simulation.

  • KITcar internal. Clone kitcar-rosbag in the same directory as kitcar-ros.

    git clone $KITCAR_REPO_PATH/kitcar-rosbag

    Then follow the installation steps there.


Let’s test if everything works. Open up a new terminal.

You can now start the simulation with

roslaunch gazebo_simulation master.launch

Gazebo should now open with the car and a road.


  • Gazebo isn’t shown correctly

    This might be due to your graphics driver causing problems. Try to set


    For further information take a look here.