LiDAR, which stands for Light Detection and Ranging, is a remote sensing method used to measure distances to a target and create detailed 3D maps of the surrounding environment. It operates by emitting short pulses of light and measuring the time it takes for the reflected light to return to the receiver. The data collected from the reflected light is recorded as millions of points, known as a "point cloud," which represents the 3D positions of objects in the environment.
2D LiDAR: Uses thesensor_msgs/LaserScanmessage type.3D LiDAR: Uses thesensor_msgs/PointCloud2message type.
To integrate a LiDAR sensor in your robot's URDF description using Xacro, you include a separate Xacro file dedicated to the LiDAR setup. In your main robot Xacro file, you would include the LiDAR configuration as follows:
<xacro:include filename="lidar.xacro" />Below is an example of how to define the LiDAR configuration in the lidar.xacro file:
<?xml version="1.0"?>
<robot xmlns:xacro="http://www.ros.org/wiki/xacro">
<joint name="laser_joint" type="fixed">
<parent link="chassis" />
<child link="laser_frame" />
<origin xyz="0.1 0 0.175" rpy="0 0 0" />
</joint>
<link name="laser_frame">
<visual>
<geometry>
<cylinder radius="0.05" length="0.04" />
</geometry>
<material name="red" />
</visual>
<collision>
<geometry>
<cylinder radius="0.05" length="0.04" />
</geometry>
</collision>
<xacro:inertial_cylinder mass="0.1" length="0.04" radius="0.05">
<origin xyz="0 0 0" rpy="0 0 0" />
</xacro:inertial_cylinder>
</link>
<gazebo reference="laser_frame">
<material>Gazebo/Red</material>
<sensor name="laser" type="ray">
<pose>0 0 0 0 0 0</pose>
<visualize>true</visualize>
<update_rate>10</update_rate>
<ray>
<scan>
<horizontal>
<samples>360</samples>
<min_angle>-3.14</min_angle>
<max_angle>3.14</max_angle>
</horizontal>
</scan>
<range>
<min>0.3</min>
<max>12</max>
</range>
</ray>
<plugin name="laser_controller" filename="libgazebo_ros_ray_sensor.so">
<ros>
<argument>~/out:=scan</argument>
</ros>
<output_type>sensor_msgs/LaserScan</output_type>
<frame_name>laser_frame</frame_name>
</plugin>
</sensor>
</gazebo>
</robot>You can create a launch file to bring up the LiDAR node. Below is an example launch file rplidar.launch.py for an RPLIDAR sensor:
import os
from launch import LaunchDescription
from launch_ros.actions import Node
def generate_launch_description():
return LaunchDescription([
Node(
package='rplidar_ros',
executable='rplidar_composition',
output='screen',
parameters=[{
'serial_port': '/dev/serial/by-path/platform-fd500000.pcie-pci-0000:01:00.0-usb-0:1.3:1.0-port0',
'frame_id': 'laser_frame',
'angle_compensate': True,
'scan_mode': 'Standard'
}]
)
])To launch the simulation with the LiDAR sensor, use the following command:
$ ros2 launch articubot_one launch_sim.launch.py world:=./src/articubot_one/worlds/obstacles.worldTo use a real LiDAR sensor, you need to install the relevant ROS 2 package and run the LiDAR node with appropriate parameters:
- Install the RPLIDAR package:
$ sudo apt install ros-foxy-rplidar-ros. - Run the RPLIDAR node:
$ ros2 run rplidar_ros rplidar_composition --ros-args -p serial_port:=/dev/ttyUSB0 -p frame_id:=laser_frame -p angle_compensate:=true -p scan_mode:=Standard.
To visualize the LiDAR data in RViz, run the following command: $ rviz2.
- Check serial ports:
$ ls /dev/serial/by-. - Stop the LiDAR motor:
$ ros2 service call /stop_motor std_srvs/srv/Empty {}. - Force stop the LiDAR node:
$ killall rplidar_composition.
8 bits : 0 - 255, 3 channels (R, G, B) | Camera → [ Driver Node ] → [ sensor_msgs/Image ] / [ sensor_msgs/CompressedImage ]→ [ Algorithms & stuff ].
The [image_transport library/nodes] does all the conversions between Image and CompressedImage. The unprocessed image is at [/image_raw] and for compressed images [/image_raw/compressed].
[ sensor_msgs/Image ] ; [ sensor_msgs/CameraInfo ]
In the main robot xacro file : <xacro:include filename="camera.xacro">
In camera.xacro file:
<joint name="camera_joint" type="fixed">
<parent link="chassis"/>
<child link="camera_link"/>
<origin xyz="0.305 0 0.08" rpy="0 0 0"/>
</joint>
<link name="camera_link">
<visual>
<geometry>
<box size="0.010 0.03 0.03">
</geometry>
<material name="red">
</visual>
</link>
<joint name="camera_optical_joint" type="fixed">
<parent link="camera_link"/>
<child link="camera_link_optical"/>
<origin xyz="0 0 0" rpy="${-pi/2} 0 ${-pi/2}"/>
</joint>
<link name="camera_link_optical"></link>
<gazebo reference="camera_link">
<material>Gazebo/Red</material>
<sensor name="camera" type="camera">
<pose>0 0 0 0 0 0</pose>
<visualize>true<visualize>
<update_rate>10</update_rate>
<camera>
<horizontal_fov>1.089</horizontal_fov>
<image>
<format>R8G8B8</format>
<width>640</width>
<height>480</height>
</image>
<clip>
<near>0.05</near>
<far>8.0</far>
</clip>
</camera>
<plugin>
<ros>
<argument>~/out:=scan</argument>
</ros>
<output_type>sensor_msgs/LaserScan</output_type>
<frame_name>camera_link_optical</frame_name>
</plugin>
</sensor>
</gazebo>To test : $ colcon build --symlink-install → $ source install/setup.bash → $ ros2 launch articubot_one launch_sim.launch.py world:=./src/articubot_one/worlds/obstacles.world.
Plugins for image compression: $ sudo apt install ros-foxy-image-transport-plugins → $ sudo apt install ros-foxy-rqt-image-view → $ ros2 run rqt_image_view rqt_image_view.
Next : $ ros2 run image_transport list_transports.
To get image of one type and republish it to another type : $ ros2 run image_transport republish compressed raw --ros-args -r in/compressed:=/camera/image_raw/compressed -r out:=/camera/image_raw/uncompressed.
$ sudo apt install libraspberrypi-bin v4l-utils ros-foxy-v4l2-camera ros-foxy-image-transport-plugins ros-foxy-rqt-image-view → $ sudo usermod -aG video username.
To check camera : $ vcgencmd get_camera → $ raspistill -k (to stream data from camera).
If video for linux subsystem can see it : $ v4l2-ctl --list-devices.
To run video for linux driver : $ ros2 run v4l2_camera v4l2_camera_node --ros-args -p image_size:="[640, 480]" -p camera_frame_id:=camera_link_optical → $ ros2 run rqt_image_view.
import os
from launch import LaunchDescription
from launch_ros.actions import Node
def generate_launch_description():
return LaunchDescription([
Node(
package='v4l2_camera',
executable='v4l2_camera_node',
output='screen',
parameters=[{
'image_size': [640,480],
'camera_frame_id': 'camera_link_optical'
}]
)
])To run with the launch file : $ pluma camera.launch.py → $ ros2 launch camera.launch.py.
Depth image → 3D projection → Point cloud. Data is stored as 32 bit float. ROS 2 also provides the depth_image_proc package which is in image_pipelline repository/metapackage/stack.
To simulate a depth camera in gazebo: In the main robot xacro file : <xacro:include filename="depth_camera.xacro">
In depth_camera.xacro file:
<joint name="camera_joint" type="fixed">
<parent link="chassis"/>
<child link="camera_link"/>
<origin xyz="0.305 0 0.08" rpy="0 0 0"/>
</joint>
<link name="camera_link">
<visual>
<geometry>
<box size="0.010 0.03 0.03">
</geometry>
<material name="red">
</visual>
</link>
<joint name="camera_optical_joint" type="fixed">
<parent link="camera_link"/>
<child link="camera_link_optical"/>
<origin xyz="0 0 0" rpy="${-pi/2} 0 ${-pi/2}"/>
</joint>
<link name="camera_link_optical"></link>
<gazebo reference="camera_link">
<material>Gazebo/Red</material>
<sensor name="camera" type="depth">
<pose>0 0 0 0 0 0</pose>
<visualize>true<visualize>
<update_rate>10</update_rate>
<camera>
<horizontal_fov>1.089</horizontal_fov>
<image>
<format>B8G8R8</format>
<width>640</width>
<height>480</height>
</image>
<clip>
<near>0.05</near>
<far>8.0</far>
</clip>
</camera>
<plugin name="camera_controller" filename="lib_gazebo_ros">
<frame_name>camera_link_optical</frame_name>
<min_depth>0.1</min_depth>
<max_depth>0.1</max_depth>
</plugin>
</sensor>
</gazebo>to run : $ colcon build --symlink-install → $ source install/setup.bash.
OAK = OpenCV AI Kit → $ lsusb | github.com/luxonis/depthai-ros, (Depth Cameras in ROS).