Here is a step-by-step tutorial creating a map of the room and navigating around the room using the newly-created map in real time. I’m using here a Windows PC running ROS2 in a Docker container. You can use a Linux PC running Docker as well.

If you are using a Windows PC, make sure to set up your Windows PC folliwing these instructions here.

If you are using a Linux PC, please install Docker for your Linux distro.

The (simple) self-driving code is here.

Tutorial: Real-time mapping

Here are the steps.

• Launch Docker for Windows on your local Windows PC.
• Launch VcXsrv on your local Windows PC.
• Pull the Kaia.ai developer Docker image to your local PC.

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  docker pull kaiaai/kaiaai-ros-dev:humble
  

• Launch the Kaia.ai developer Docker image on your local PC.

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  docker run --name kaiaai-ros-dev-humble -it --rm -p 8888:8888/udp -e DISPLAY=host.docker.internal:0.0 -e LIBGL_ALWAYS_INDIRECT=0 kaiaai/kaiaai-ros-dev:humble
  

• Launch the telemetry.

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  ros2 launch kaiaai_bringup main.launch.py
  

• Open a new shell window and start a new bash session in the Docker container.

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  docker exec -it kaiaai-ros-dev-humble bash
  

• In the newly opened window, launch Google Cartographer to start creating a map of the room. The Cartographer launch script also opens the Rviz2 ROS2 viewer to view the map.

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  ros2 launch kaiaai_bringup cartographer.launch.py
  

• At this point you should be seeing Snoopy’s laser scan data
• Open yet another new shell window and start yet another new bash session in the Docker container.

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  docker exec -it kaiaai-ros-dev-humble bash
  

• Launch the keyboard teleoperation script in the newly created bash session.

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  ros2 run kaiaai_teleop teleop_keyboard
  

• Drive Snoopy around the room, creating the map as it goes.
• Press CTRL-C to stop Snoopy’s keyboard teleoperation script.
• Save the newly-created map.

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  ros2 run nav2_map_server map_saver_cli -f $HOME/my_map
  

• Press CTRL-C to stop Google Cartographer in the Cartographer’s shell window.
• Press CTRL-C stop Rviz2 in Rviz2 shell window.

Tutorial: Real-time navigation

Here are the steps.

• Launch Docker for Windows on your local Windows PC.
• Launch VcXsrv on your local Windows PC.
• Pull the Kaia.ai developer Docker image to your local PC.

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  docker pull kaiaai/kaiaai-ros-dev:humble
  

• Launch the Kaia.ai developer Docker image on your local PC.

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  docker run --name kaiaai-ros-dev-humble -it --rm -p 8888:8888/udp -e DISPLAY=host.docker.internal:0.0 -e LIBGL_ALWAYS_INDIRECT=0 kaiaai/kaiaai-ros-dev:humble
  

• Launch the telemetry.

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  ros2 launch kaiaai_bringup main.launch.py
  

• Open a new shell window and start a new bash session in the Docker container.

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  docker exec -it kaiaai-ros-dev-humble bash
  

• In the newly-opened shell, launch ROS2 Navigation and load the newly-created map. This launch script also opens a new Rviz2 window.

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  ros2 launch kaiaai_bringup navigation.launch.py map:=$HOME/my_map.yaml
  

• Manually specify Snoopy’s location on the map in Rviz2.
  • Click the 2D Pose Estimate button in the Rviz2 toolbar.
  • Click-and-hold on the map at Snoopy’s (approximate) current location.
  • Drag the mouse in the (approximate) direction Snoopy is currently facing.
  • Release the mouse button.
• Manually specify the location where you want Snoopy to navigate.
  • Click the Nav2 Goal button in the Rviz2 toolbar.
  • Click-and-hold on the map where you want Snoopy’s to move automatically.
  • Drag the mouse in the direction you want Snoopy to be facing once it arrives.
  • Release the mouse button.
• At this point Snoopy should navigate to the desired location automatically.
  • The Nav2 panel on the left in Rviz2 displays the status of navigation.
  • You can configure Snoopy’s autonomous navigation behavior - including its speed and how tight of a path it is willing to follow - in this file.
• Please keep in mind that Snoopy uses an inexpensive laser distance scanner to keep the overall project costs affordable.
  • An inexpensive laser scanner like this cannot sense obstacles above or below its scanning plane. For example, Snoopy cannot see obstacles that have a short height.
  • You can work around this limitation by manually placing objects - on the floor - that are tall enough for Snoopy to “see” and avoid. This works both for the real-world operation and simulations.