Lab. 03 - 2D SLAM and AMCL

1. What are SLAM and AMCL?

SLAM, or Simultaneous Localization And Mapping, is the problem of continuously building and updating a map of an unknown environment while at the same time estimating the pose of an agent (e.g. a robot) in it.

AMCL, or Adaptive Monte Carlo Localization, is a probabilistic localization system that uses a particle filter to track the agent’s pose on a known map.

2. Goal of the class

The goal of this class is to run SLAM and AMCL on a simulated turtlebot3 robot. You will build a map of the robot’s environment and then navigate autonomously in this known environment.

By modifying the parameters of these systems, you will be able to analyze how they affect the algorithms.

3. Environment setup

Before you start, prepare the environment: Environment setup instructions. The image is based on osrf/ros:jazzy-desktop-full (ROS 2 Jazzy Jalisco) with the Gazebo Harmonic simulator.

By default the container is named ARM_03.

NOTE! When run, the scripts remove a container with this name before creating a new one.

Using the container

After every computer restart, remember to run:

xhost +local:root

A new terminal can be attached to the container with:

docker exec -it ARM_03 bash

ROS_DOMAIN_ID

When several computers work in the same local network (eduroam may require this too), you may need to set the ROS_DOMAIN_ID environment variable to different values. Choose any integer from 0 to 100. Set this value in the ~/.bashrc file:

export ROS_DOMAIN_ID=10  # example value

or directly in the terminal (it then applies only to that terminal):

export ROS_DOMAIN_ID=10  # example value

It is suggested to use the last two digits of the computer’s IP address as the ROS_DOMAIN_ID value, to avoid conflicts with other users in the network. To check your IP address, you can use:

ip addr show

The IP address is shown under the network interface, e.g. wlan0 or eth0.

It may be convenient to add this export to bashrc:

echo 'export ROS_DOMAIN_ID=<chosen_value>' >> ~/.bashrc 

Gazebo: GZ_IP and GZ_PARTITION

Gazebo communicates independently of ROS 2 (through the gz-transport library), so ROS_DOMAIN_ID does not separate simulations. By default, Gazebo connects simulations started by the same user on computers with the same name (hostname), and a container started with --network=host takes the computer’s name and runs as root. If computers in the network have the same names, simulations of different people can interfere with each other.

That is why the image sets GZ_IP=127.0.0.1 in the ~/.bashrc file, so Gazebo communicates only within one computer. In containers created from an older version of the image (the echo $GZ_IP command prints nothing), add it yourself:

echo 'export GZ_IP=127.0.0.1' >> ~/.bashrc

If several simulations run on one computer (e.g. in several containers), you can also separate them by setting a different GZ_PARTITION value in each container (Gazebo nodes only see each other within the same partition):

echo 'export GZ_PARTITION=<chosen_name>' >> ~/.bashrc  # e.g. arm_10

The GZ_PARTITION value must be the same in all terminals where you use Gazebo (starting the simulation, gz commands), so it is better to set it in ~/.bashrc than in a single terminal. Changes in ~/.bashrc apply to newly opened terminals, so restart the simulation after making them.

4. Running SLAM

We will work with the Turtlebot3 robot, so we need to set the model we will use:

export TURTLEBOT3_MODEL=burger

The available models are burger, waffle and waffle_pi.

Next, start the world simulation:

ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

In a new terminal, start the cartographer node, which is the SLAM module:

export TURTLEBOT3_MODEL=burger
ros2 launch turtlebot3_cartographer cartographer.launch.py use_sim_time:=True

As you can see in rviz2, the system has started building the map. Since the robot is not moving, it sees its surroundings from a single point all the time, so it lacks the measurements needed to build a complete map. You will have to move the robot around the “world”.

In a separate terminal, start the keyboard teleoperation node:

export TURTLEBOT3_MODEL=burger
ros2 run turtlebot3_teleop teleop_keyboard

Then, using the w, a, s, d and x keys, drive the robot so that a map of the whole “world” gets built.

The map can be saved with:

cd /arm_ws
mkdir maps
ros2 run nav2_map_server map_saver_cli -f /arm_ws/maps/turtlebot3_world_map

Once the map is saved, you can close the simulation.

5. Running AMCL

As before, start by launching the simulation:

export TURTLEBOT3_MODEL=burger
ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

Then you can start the navigation2 node. You need to tell it to use the simulation time and give it the location of the previously saved map file. In a new terminal:

export TURTLEBOT3_MODEL=burger
ros2 launch turtlebot3_navigation2 navigation2.launch.py use_sim_time:=True map:=/arm_ws/maps/turtlebot3_world_map.yaml

After startup, the robot is not localized on the map. You need to give an initial estimate of its pose. 1. Click 2D Pose Estimate in the rviz2 window. 2. Click the place on the map where you judge the robot to be and drag towards its front. 3. Repeat steps 1 and 2 until the result is satisfactory.

Autonomous navigation

6. Independent task

As independent work, you need to:

1. Carry out the procedures from sections 4 and 5 for a different simulation world - “turtlebot3_house”

2. In the turtlebot3_navigation2 package, the param folder contains the burger.yaml file. Please describe how changing the parameters below affects the AMCL navigation module:

   1. beam_skip_distance with do_beamskip set to True
   2. laser_max_range and laser_min_range
   3. max_beams
   4. max_particles
   5. resample_interval
   6. update_min_a and update_min_d

Upload to eKursy: - the saved map (converted to png or as a screenshot) - the answer to point 2 as text on eKursy or as a .txt file.

_{Author: Kamil Młodzikowski
Based on: Turtlebot3 Simulation docs.}