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5.4.1 SLAM Mapping

Feature Introduction​

SLAM stands for Simultaneous Localization and Mapping.
This section uses ROS2's SLAM-Toolbox as the mapping algorithm. We control a robot car in Gazebo to build a map and observe the mapping results via Rviz2.
SLAM-Toolbox runs on the RDK, while Gazebo and Rviz2 run on a PC connected to the same network segment as the RDK.

Supported Platforms​

PlatformRuntime Environment
RDK X3, RDK X3 ModuleUbuntu 20.04 (Foxy), Ubuntu 22.04 (Humble)
RDK X5, RDK X5 ModuleUbuntu 22.04 (Humble)
RDK S100, RDK S100PUbuntu 22.04 (Humble)
RDK UltraUbuntu 20.04 (Foxy)

Prerequisites​

RDK Platform​

  1. The RDK has been flashed with either Ubuntu 20.04 or Ubuntu 22.04 system image.

  2. TogetheROS.Bot has been successfully installed on the RDK.

  3. After successful installation of tros.b, install SLAM-Toolbox:

sudo apt-get install ros-foxy-slam-toolbox
info

If the installation fails with an error like:

  The following packages have unmet dependencies:
   ros-foxy-slam-toolbox : Depends: ros-foxy-nav2-map-server but it is not going to be installed
  E: Unable to correct problems, you have held broken packages.

Please run the following commands before attempting installation again:

apt update
sudo apt install libwebp6=0.6.1-2ubuntu0.20.04.3
Note

If the sudo apt update command fails or reports errors, please refer to the FAQ section Common Issues, specifically Q10: How to resolve failures or errors when running apt update?

  1. On a PC within the same network segment as the RDK, ensure that Ubuntu 20.04/Ubuntu 22.04, ROS2 Desktop, the Gazebo simulation environment, and the visualization tool Rviz2 are already installed.
  • Ubuntu 20.04 and ROS2 Foxy Desktop
  • After successfully installing ROS2 on the PC, install Gazebo and Turtlebot3-related packages as follows:
sudo apt-get install ros-foxy-gazebo-*
sudo apt install ros-foxy-turtlebot3
sudo apt install ros-foxy-turtlebot3-bringup
sudo apt install ros-foxy-turtlebot3-simulations
sudo apt install ros-foxy-teleop-twist-keyboard

Usage Instructions​

RDK Platform​

This section describes how to run the SLAM algorithm on the RDK and observe the mapping results on the PC.

Start the simulation environment on the PC:

source /opt/ros/foxy/setup.bash
export TURTLEBOT3_MODEL=burger
ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py
info

If startup fails with the error [ERROR] [gzclient-2]: process has died, please run source /usr/share/gazebo/setup.sh before launching again.

The simulation environment appears as shown below:

Open another terminal on the PC and launch Rviz2 to visualize the mapping process:

source /opt/ros/foxy/setup.bash
ros2 launch turtlebot3_bringup rviz2.launch.py

After opening Rviz2, add the "Map" visualization display to show the generated map. Follow the steps illustrated below:

Run SLAM-Toolbox on the RDK:

# Configure tros.b environment
source /opt/tros/setup.bash
# Launch SLAM launch file
ros2 launch slam_toolbox online_sync_launch.py

Open another terminal on the PC and launch the teleoperation tool to control the robot using your keyboard. Refer to the instructions printed in the terminal for control details—these will not be repeated here:

source /opt/ros/foxy/setup.bash
ros2 run teleop_twist_keyboard teleop_twist_keyboard

Control the robot's movement. As the robot's LiDAR detects more environmental information, the SLAM algorithm simultaneously constructs a map of the environment, which can be observed in Rviz2.

Result Analysis​

The terminal output when running on the RDK board is as follows:

[INFO] [launch]: All log files can be found below /root/.ros/log/2022-06-10-06-40-34-204213-ubuntu-5390
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [sync_slam_toolbox_node-1]: process started with pid [5392]
[sync_slam_toolbox_node-1] [INFO] [1654843239.403931058] [slam_toolbox]: Node using stack size 40000000
[sync_slam_toolbox_node-1] [INFO] [1654843240.092340814] [slam_toolbox]: Using solver plugin solver_plugins::CeresSolver
[sync_slam_toolbox_node-1] [INFO] [1654843240.096554433] [slam_toolbox]: CeresSolver: Using SCHUR_JACOBI preconditioner.
[sync_slam_toolbox_node-1] Info: clipped range threshold to be within minimum and maximum range!
[sync_slam_toolbox_node-1] [WARN] [1654843589.431524393] [slam_toolbox]: maximum laser range setting (20.0 m) exceeds the capabilities of the used Lidar (3.5 m)
[sync_slam_toolbox_node-1] Registering sensor: [Custom Described Lidar]
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