Installing Livox-SDK2

Create a workspace directory for the Livox components:

mkdir -p ~/livox_ws/src
cd ~/livox_ws/src

Clone and build the Livox-SDK2 repository:

git clone https://github.com/Livox-SDK/Livox-SDK2.git
cd Livox-SDK2
mkdir build
cd build
cmake .. && make -j8
sudo make install

During compilation, you might encounter CMake version requirements. If your system runs a older vertion (e.g., 3.10.2), upgrade to at least 3.14.1:

sudo rm -rf /usr/bin/cmake
sudo ln -s /path/to/cmake-3.14.1/bin/cmake /usr/bin/cmake
cmake --version

Installing livox_ros_driver2

Clone and compile the ROS driver:

cd ~/livox_ws/src
git clone https://github.com/Livox-SDK/livox_ros_driver2.git ws_livox/src/livox_ros_driver2
cd ws_livox
source /opt/ros/melodic/setup.sh  # Replace with your ROS distribution
./build.sh ROS1

Network Configuration

Modify the configuration files to match your network setup. Update livox_ros_driver2/config/MID360_config.json:

• Set host_net_info to match your static IP configuration
• Configure lidar_configs with IP address 192.168.1.1XX, where XX represents the last two digits of your MID360's broadcast code

Also udpate the following launch files with your specific broadcast code:

• livox_ros_driver2/launch_ROS1/rviz_MID360.launch
• livox_ros_driver2/launch_ROS1/msg_MID360.launch

Using the correct broadcast code prevents interference in multi-lidar environments and ensures accurate SLAM performance.

Testing the Installation

Add the workspace to your ROS environment:

echo 'source ~/livox_ws/src/ws_livox/devel/setup.bash' >> ~/.bashrc
source ~/.bashrc

Launch the driver with different point cloud formats:

# CustomMsg format
roslaunch livox_ros_driver2 msg_MID360.launch

# PointCloud2 format with RViz visualization
roslaunch livox_ros_driver2 rviz_MID360.launch

Your applications can subscribe to the /livox/lidar topic to receive point cloud data.

Open3D Installation for Visualization

Install Open3D for point cloud visualization:

pip install ipywidgets==7.6.3
pip install open3d

The test procedure involves two visualization passes:

1. Identify regions with reflectance values above 130 within 40×10 cm areas
2. Display all point clouds with color coding: red for high reflectance (≥130), blue for lower values