LIO-SAM Gazebo ROS2

This repository contains the implementation of LIO-SAM (Lidar Inertial Odometry via Smoothing and Mapping) integrated with ROS 2 (Robot Operating System 2) for robust and accurate SLAM (Simultaneous Localization and Mapping) applications.

This repository contains the implementation of LIO-SAM (Lidar Inertial Odometry via Smoothing and Mapping) integrated with ROS 2 (Robot Operating System 2) for robust and accurate SLAM (Simultaneous Localization and Mapping) applications. LIO-SAM leverages both Lidar and IMU (Inertial Measurement Unit) data to achieve high accuracy in mapping and localization in real-time.

Features

  • Tightly coupled integration: Combines Lidar and IMU data for improved accuracy in challenging environments.
  • Real-time performance: Designed for efficient computation, making it suitable for embedded systems.
  • Factor graph optimization: Utilizes advanced optimization techniques to refine localization estimates.
  • ROS 2 support: Facilitates easy communication and control in robotics applications.

Requirements

To run this project, you need to have the following installed:

  • ROS 2 (Foxy, Galactic, or later)
  • Gazebo (for simulation)
  • Colcon (build tool for ROS 2 packages)
  • A compatible Lidar and IMU setup

Installation

  1. Clone this repository to your local machine:

    git clone https://github.com/TixiaoShan/LIO-SAM.git
cd LIO-SAM

  2. Checkout the ROS 2 branch:

    git checkout ros2

  3. Build the package using Colcon:

    colcon build

  4. Source the setup file:

    source install/setup.bash

Running LIO-SAM with ROS 2

  1. Launch LIO-SAM nodes in the first terminal:

    ros2 launch lio_sam run.launch.py

  2. Launch the Gazebo simulation in a second terminal:

    ros2 launch robot_gazebo robot_sim.launch.py

Usage

ROS 2 Topics

  • /lidar_points: Raw point cloud data from the Lidar sensor.
  • /imu/data: IMU data providing orientation and acceleration.
  • /tf: Transformation frames for the robot’s pose.
  • /map: The generated map from the LIO-SAM algorithm.
  • /odometry: Estimated odometry data from LIO-SAM.

You can visualize the results in Rviz or any other compatible visualization tool by running:

ros2 run rviz2 rviz2

## Contributing

Feel free to submit issues, create pull requests, or fork this repository to improve and extend the project.