Simulating Sensors in Gazebo

Sensors are the robot's eyes and ears. Gazebo simulates realistic sensor data including cameras, LiDAR, IMUs, and GPS, enabling you to test perception algorithms before deploying to real hardware.

Why Simulate Sensors?

Sensor simulation offers critical advantages:

Cost Savings: Test expensive sensors (LiDAR, depth cameras) without buying them
Perfect Ground Truth: Know exact positions and measurements for algorithm validation
Rapid Iteration: Change sensor placement instantly without rewiring
Edge Case Testing: Simulate difficult conditions (fog, darkness, sensor failures)
Parallel Development: Software team works while hardware team builds robot

Key Sensor Types

1. Camera: Visual Perception

Cameras capture RGB images for object detection, lane following, and visual navigation.

<sensor name="camera" type="camera">
  <pose>0.2 0 0.3 0 0 0</pose>
  <camera>
    <horizontal_fov>1.047</horizontal_fov>  <!-- 60 degrees -->
    <image>
      <width>640</width>
      <height>480</height>
      <format>R8G8B8</format>
    </image>
    <clip>
      <near>0.1</near>
      <far>100</far>
    </clip>
  </camera>
  <update_rate>30</update_rate>
  <visualize>true</visualize>
  <plugin name="camera_controller" filename="libgazebo_ros_camera.so">
    <ros>
      <namespace>/robot</namespace>
      <argument>--ros-args --remap ~/image_raw:=camera/image_raw</argument>
    </ros>
    <frame_name>camera_link</frame_name>
  </plugin>
</sensor>

2. LiDAR: Distance Measurement

LiDAR (Light Detection and Ranging) measures distances to objects using laser beams.

<sensor name="lidar" type="ray">
  <pose>0 0 0.4 0 0 0</pose>
  <ray>
    <scan>
      <horizontal>
        <samples>360</samples>  <!-- 360-degree scan -->
        <resolution>1</resolution>
        <min_angle>-3.14159</min_angle>
        <max_angle>3.14159</max_angle>
      </horizontal>
    </scan>
    <range>
      <min>0.12</min>
      <max>10.0</max>
      <resolution>0.01</resolution>
    </range>
  </ray>
  <update_rate>10</update_rate>
  <plugin name="lidar_controller" filename="libgazebo_ros_ray_sensor.so">
    <ros>
      <namespace>/robot</namespace>
      <argument>--ros-args --remap ~/out:=scan</argument>
    </ros>
    <output_type>sensor_msgs/LaserScan</output_type>
    <frame_name>lidar_link</frame_name>
  </plugin>
</sensor>

3. IMU: Orientation and Motion

IMU (Inertial Measurement Unit) tracks rotation, acceleration, and angular velocity.

<sensor name="imu" type="imu">
  <pose>0 0 0.1 0 0 0</pose>
  <imu>
    <angular_velocity>
      <x>
        <noise type="gaussian">
          <mean>0.0</mean>
          <stddev>0.0002</stddev>
        </noise>
      </x>
      <!-- y and z similar -->
    </angular_velocity>
    <linear_acceleration>
      <x>
        <noise type="gaussian">
          <mean>0.0</mean>
          <stddev>0.017</stddev>
        </noise>
      </x>
      <!-- y and z similar -->
    </linear_acceleration>
  </imu>
  <update_rate>100</update_rate>
  <plugin name="imu_controller" filename="libgazebo_ros_imu_sensor.so">
    <ros>
      <namespace>/robot</namespace>
      <argument>--ros-args --remap ~/out:=imu/data</argument>
    </ros>
    <frame_name>imu_link</frame_name>
  </plugin>
</sensor>

4. Depth Camera: 3D Perception

Depth cameras (like RealSense or Kinect) provide RGB images plus depth information.

<sensor name="depth_camera" type="depth">
  <pose>0.2 0 0.3 0 0 0</pose>
  <camera>
    <horizontal_fov>1.047</horizontal_fov>
    <image>
      <width>640</width>
      <height>480</height>
      <format>R8G8B8</format>
    </image>
    <clip>
      <near>0.1</near>
      <far>10.0</far>
    </clip>
  </camera>
  <update_rate>30</update_rate>
  <plugin name="depth_camera_controller" filename="libgazebo_ros_camera.so">
    <ros>
      <namespace>/robot</namespace>
    </ros>
    <frame_name>depth_camera_link</frame_name>
    <min_depth>0.1</min_depth>
    <max_depth>10.0</max_depth>
  </plugin>
</sensor>

Sensor Noise Models

Real sensors have noise. Gazebo simulates this for realistic testing:

Noise Type	Description	When to Use
Gaussian	Random variation around mean	Camera pixels, IMU drift
Uniform	Equal probability in range	Distance sensors
Salt & Pepper	Random outliers	LiDAR false readings

Accessing Sensor Data in ROS 2

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

class SensorSubscriber(Node):
    def __init__(self):
        super().__init__('sensor_subscriber')

        # Subscribe to LiDAR
        self.lidar_sub = self.create_subscription(
            LaserScan,
            '/robot/scan',
            self.lidar_callback,
            10
        )

        # Subscribe to camera
        self.camera_sub = self.create_subscription(
            Image,
            '/robot/camera/image_raw',
            self.camera_callback,
            10
        )

        # Subscribe to IMU
        self.imu_sub = self.create_subscription(
            Imu,
            '/robot/imu/data',
            self.imu_callback,
            10
        )

    def lidar_callback(self, msg):
        # msg.ranges contains distance measurements
        min_distance = min(msg.ranges)
        self.get_logger().info(f'Closest obstacle: {min_distance:.2f}m')

    def camera_callback(self, msg):
        # msg.data contains image pixels
        self.get_logger().info(f'Camera frame: {msg.width}x{msg.height}')

    def imu_callback(self, msg):
        # msg.linear_acceleration contains acceleration data
        self.get_logger().info(f'Acceleration: {msg.linear_acceleration.x:.2f}')

def main():
    rclpy.init()
    node = SensorSubscriber()
    rclpy.spin(node)

Sensor Placement Best Practices

Camera Height: Mount at typical viewing height (0.3-0.5m for ground robots)
LiDAR Clearance: Place above obstacles to avoid blind spots
IMU Location: Mount near center of mass for accurate readings
Field of View: Ensure cameras cover intended detection zones
Update Rates: Match real sensor specs (30 Hz cameras, 10 Hz LiDAR)

Common Sensor Issues

Issue	Cause	Solution
No data published	Plugin not loaded	Check plugin filename and ROS namespace
All zero readings	Sensor inside collision mesh	Adjust sensor pose
Noisy data	Excessive noise parameters	Reduce stddev in noise model
Low frame rate	High resolution + complex scene	Reduce image size or scene complexity

Visualizing Sensor Data

# View camera feed
ros2 run rqt_image_view rqt_image_view /robot/camera/image_raw

# View LiDAR scan in RViz
rviz2
# Add Display → LaserScan → Topic: /robot/scan

# Plot IMU data
ros2 run rqt_plot rqt_plot /robot/imu/data/linear_acceleration/x

Try It Yourself

Ask the chatbot:

"How do I add noise to a camera sensor in Gazebo?"
"What's the difference between a camera and a depth camera?"
"How do I visualize LiDAR data in RViz?"

Why Simulate Sensors?​

Key Sensor Types​

1. Camera: Visual Perception​

2. LiDAR: Distance Measurement​

3. IMU: Orientation and Motion​

4. Depth Camera: 3D Perception​

Sensor Noise Models​

Accessing Sensor Data in ROS 2​

Sensor Placement Best Practices​

Common Sensor Issues​

Visualizing Sensor Data​

Try It Yourself​