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Simulating Sensors: LiDAR, Cameras, and IMUs

📡 Sensor Types​

LiDAR (Light Detection and Ranging)​

<gazebo reference="lidar_link">
  <sensor name="lidar" type="ray">
    <pose>0 0 0 0 0 0</pose>
    <ray>
      <scan>
        <horizontal>
          <samples>360</samples>
          <resolution>1</resolution>
          <min_angle>-3.14159</min_angle>
          <max_angle>3.14159</max_angle>
        </horizontal>
      </scan>
      <range>
        <min>0.1</min>
        <max>30.0</max>
      </range>
    </ray>
    <plugin name="gazebo_ros_lidar" filename="libgazebo_ros_ray_sensor.so">
      <ros>
        <namespace>/robot</namespace>
        <remapping>~/out:=scan</remapping>
      </ros>
      <output_type>sensor_msgs/LaserScan</output_type>
    </plugin>
  </sensor>
</gazebo>

RGB-D Camera​

<gazebo reference="camera_link">
  <sensor name="depth_camera" type="depth">
    <camera>
      <horizontal_fov>1.047</horizontal_fov>
      <image>
        <width>640</width>
        <height>480</height>
      </image>
      <clip>
        <near>0.1</near>
        <far>10.0</far>
      </clip>
    </camera>
    <plugin name="depth_camera_controller" filename="libgazebo_ros_camera.so">
      <ros>
        <namespace>/camera</namespace>
      </ros>
      <camera_name>depth</camera_name>
      <frame_name>camera_link</frame_name>
    </plugin>
  </sensor>
</gazebo>

IMU (Inertial Measurement Unit)​

<gazebo reference="imu_link">
  <sensor name="imu_sensor" type="imu">
    <plugin name="imu_plugin" filename="libgazebo_ros_imu_sensor.so">
      <ros>
        <namespace>/imu</namespace>
        <remapping>~/out:=data</remapping>
      </ros>
      <initial_orientation_as_reference>false</initial_orientation_as_reference>
    </plugin>
  </sensor>
</gazebo>

📊 Processing Sensor Data​

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import LaserScan, Image, Imu
from cv_bridge import CvBridge

class SensorFusion(Node):
    def __init__(self):
        super().__init__('sensor_fusion')
        
        self.create_subscription(LaserScan, '/scan', self.lidar_callback, 10)
        self.create_subscription(Image, '/camera/depth/image_raw', self.depth_callback, 10)
        self.create_subscription(Imu, '/imu/data', self.imu_callback, 10)
        
        self.bridge = CvBridge()
    
    def lidar_callback(self, msg):
        # Process LiDAR data
        min_distance = min(msg.ranges)
        self.get_logger().info(f'Closest obstacle: {min_distance}m')
    
    def depth_callback(self, msg):
        # Process depth image
        depth_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
    
    def imu_callback(self, msg):
        # Process IMU data
        self.get_logger().info(f'Orientation: {msg.orientation}')

🎯 Key Takeaways​

  • LiDAR provides 360° obstacle detection
  • RGB-D cameras combine color and depth
  • IMUs measure orientation and acceleration
  • Sensor fusion improves perception accuracy

Next: Hands-On Lab →