Comprehensive Guide: Running SLAM with RPLidar A2/A3 on ROS2

This guide walks you through the installation and execution of SLAM using the RPLidar A2/A3 on ROS2, leveraging the rf2o_laser_odometry and turtlebot4 packages for odometry and visualization.

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Step 1: Install ROS2

If you haven't installed ROS2 yet, follow the official instructions for your distribution. This guide assumes you're using ROS2 Humble or any other supported version.

1. Follow the official ROS2 installation instructions from here.

2. Ensure your workspace is set up:

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws
colcon build --symlink-install
source install/setup.bash

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Step 2: Install RPLidar Dependencies

2.1 Install RPLidar ROS2 Package

You'll need to install the sllidar_ros2 package, which contains the drivers to interface with the RPLidar A2/A3.

1. Clone the sllidar_ros2 repository:

cd ~/ros2_ws/src
git clone https://github.com/Slamtec/sllidar_ros2.git

2. Build the workspace:

cd ~/ros2_ws
colcon build --symlink-install
source install/setup.bash

3. Ensure the RPLidar is connected to your machine via USB. You may also need to set the correct permissions for the USB port. Run:

sudo chmod 666 /dev/ttyUSB0

2.2 Test the RPLidar Node

To ensure the RPLidar node is functioning correctly:

1. Start the RPLidar node:

ros2 launch sllidar_ros2 view_sllidar_a3_launch.py

2. Confirm that scan data is being published:

ros2 topic echo /scan

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Step 3: Install Odometry Package (rf2o_laser_odometry)

Odometry is essential for accurate SLAM. Install the rf2o_laser_odometry package to calculate odometry using the LIDAR scan data.

1. Install and build the package:

cd ~/ros2_ws/src
git clone https://github.com/MAPIRlab/rf2o_laser_odometry.git

Now build the package using colcon:

cd ~/ros2_ws
colcon build --packages-select rf2o_laser_odometry

2. Start the odometry node:

ros2 launch rf2o_laser_odometry rf2o_laser_odometry.launch.py laser_scan_topic:=scan

This will process the LIDAR data from the /scan topic and compute odometry.

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Step 4: Set Up Static Transformations

You need to publish static transformations between base_link, laser, and odom frames to ensure proper frame alignment for SLAM.

1. Publish the transform between the base_link and laser:

ros2 run tf2_ros static_transform_publisher 0 0 0 0 0 0 base_link laser

2. Publish the transform between the base_link and odom:

ros2 run tf2_ros static_transform_publisher 0 0 0 0 0 0 base_link odom

These transforms are necessary to ensure the SLAM algorithm can accurately interpret the robot's position.

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Step 5: Install the TurtleBot4 Package for SLAM

The TurtleBot4 package provides a SLAM implementation that can be used with any robot setup. It includes SLAM algorithms and visualization tools.

5.1 Install the TurtleBot4 Package

Follow the installation instructions from the official Turtlebot4 Documentation.

sudo apt install ros-<ros2-distro>-turtlebot4*

5.2 Launch the SLAM Algorithm

First, you need to find the path to the slam.yaml file in your turtlebot4_navigation package. Run the following command to find the installation path of the turtlebot4_navigation package

ros2 pkg prefix turtlebot4_navigation

Navigate to the config directory within this package to locate the slam.yaml file.

Once you have the path to slam.yaml, you can lauch the the slam algorithm using the following command, replacing the /path/to/slam.yaml with the actual path to your SLAM configuration file:

ros2 launch turtlebot4_navigation slam.launch.py params:=/full/path/to/slam.yaml

This starts the SLAM process and allows you to map the environment using LIDAR data.

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Step 6: Visualize the Robot in RViz

To visualize the robot and SLAM output in RViz:

1. Launch the robot visualization:

ros2 launch turtlebot4_viz view_robot.launch.py

2. This will open an RViz window where you can see the LIDAR scans, robot position, and SLAM map being generated.

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Step 7: Troubleshooting

7.1 Check for Scans

Make sure that the LIDAR is publishing scan data:

ros2 topic echo /scan

If the scan data is not being published, double-check the RPLidar connection and permissions.

7.2 Verify Odometry

If you encounter issues with odometry, verify that the rf2o_laser_odometry node is running correctly and that the topic /odom is being published.

ros2 topic echo /odom

7.3 Transform Issues

If you get errors about missing transforms, ensure you have correctly published the static transforms for base_link, laser, and odom.

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Conclusion

By following these steps, you should have a complete SLAM system running with the RPLidar A2/A3, utilizing odometry and the Turtlebot4 package for navigation and visualization. This setup will allow you to map your environment in real-time and monitor the SLAM process visually.

If you encounter any issues, refer to the troubleshooting steps or ROS2 logs for debugging, or raise the Pull Request.

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Contributors

• Syed Muhammad Hussain
• Sadiqah Mushtaq