Depth camera integration in ROS - Mini Project: Build & Apply

Depth camera integration

📖 Scenario: You are building a simple ROS node to read depth data from a camera sensor and process it.This is common in robotics to understand distances and obstacles.

🎯 Goal: Create a ROS node that subscribes to a depth camera topic, processes the depth data, and publishes a filtered result.

📋 What You'll Learn

Create a ROS subscriber node for the depth camera topic

Add a configuration variable for a depth threshold

Process the incoming depth data to filter out values beyond the threshold

Publish the filtered depth data on a new topic

💡 Why This Matters

🌍 Real World

Robots use depth cameras to detect obstacles and navigate safely. Filtering depth data helps focus on relevant distances.

💼 Career

Understanding ROS topics, subscribers, and publishers is essential for robotics software engineers working with sensors.

1

Create ROS subscriber node for depth data

Write a ROS node in Python that subscribes to the /camera/depth/image_raw topic using rospy.Subscriber and stores the incoming messages in a callback function named depth_callback. Initialize the node with the name depth_listener.

ROS

# Your code here to initialize ROS node and subscriber

Hint

Use rospy.init_node to start the node and rospy.Subscriber to listen to the depth topic.

2

Add depth threshold configuration

Add a variable called depth_threshold and set it to 2.0 to represent the maximum depth in meters to consider.

ROS

import rospy
from sensor_msgs.msg import Image

rospy.init_node('depth_listener')

depth_threshold = 2.0

# Your code here to add depth_threshold variable

def depth_callback(data):
    pass

rospy.Subscriber('/camera/depth/image_raw', Image, depth_callback)
rospy.spin()

Hint

Just create a variable depth_threshold and assign 2.0.

3

Filter depth data in callback

Inside the depth_callback function, add code to process the incoming data and filter out depth values greater than depth_threshold. Use a placeholder comment # Filter depth data here to indicate where the filtering logic goes.

ROS

import rospy
from sensor_msgs.msg import Image

rospy.init_node('depth_listener')

depth_threshold = 2.0

def depth_callback(data):
    # Filter depth data here
    pass

# Your code here to process depth data

rospy.Subscriber('/camera/depth/image_raw', Image, depth_callback)
rospy.spin()

Hint

Inside the callback, add a comment # Filter depth data here to mark where filtering will happen.

4

Publish filtered depth data

Create a ROS publisher named filtered_pub that publishes to the /camera/depth/filtered topic with message type Image. Inside depth_callback, add code to publish the filtered depth data (use the original data for this example).

ROS

import rospy
from sensor_msgs.msg import Image

rospy.init_node('depth_listener')

depth_threshold = 2.0

filtered_pub = rospy.Publisher('/camera/depth/filtered', Image, queue_size=10)

def depth_callback(data):
    # Filter depth data here
    # For simplicity, just print a message (real filtering requires image processing)
    if data:
        rospy.loginfo('Received depth data, filtering values above threshold')
        # Publish filtered data
        pass

# Your code here to publish filtered data

rospy.Subscriber('/camera/depth/image_raw', Image, depth_callback)
rospy.spin()

Hint

Create a publisher before the callback and use it inside the callback to publish filtered data.

Practice

1. What is the main purpose of integrating a depth camera in a ROS robot system?

easy

A. To send commands to other robots

B. To measure distances and perceive 3D space around the robot

C. To control the robot's motors directly

D. To store large amounts of data on the robot

Solution

1. Step 1: Understand depth camera function

   A depth camera captures distance information and 3D structure of the environment.

2. Step 2: Identify its role in ROS

   In ROS, depth cameras help robots sense obstacles and navigate safely by providing spatial data.

3. Final Answer:

   To measure distances and perceive 3D space around the robot -> Option B

4. Quick Check:

   Depth camera = 3D sensing [OK]

Hint: Depth cameras help robots see distance and shapes [OK]

Common Mistakes:

• Confusing depth camera with motor control
• Thinking it stores data instead of sensing environment
• Assuming it sends commands to other robots

2. Which ROS launch file command correctly starts a depth camera node named depth_camera_node?

easy

A.

B.

C.

D.

Solution

1. Step 1: Recall ROS launch syntax for nodes

   ROS launch files use the <node> tag with attributes pkg, type, and name to start nodes.

2. Step 2: Match correct syntax

   uses , which is correct syntax.

3. Final Answer:

   <launch><node pkg="camera_pkg" type="depth_camera_node" name="depth_camera_node"/></launch> -> Option A

4. Quick Check:

   Use <node> tag with pkg and type [OK]

Hint: Use <node> tag with pkg and type attributes [OK]

Common Mistakes:

• Using incorrect tags like <start> or <run>
• Missing the name attribute
• Confusing node and type attributes

3. Given this ROS Python snippet subscribing to a depth image topic, what will be printed when a message arrives?

import rospy
from sensor_msgs.msg import Image

def callback(data):
    print(f"Received depth image with width: {data.width}")

rospy.init_node('depth_listener')
rospy.Subscriber('/camera/depth/image_raw', Image, callback)
rospy.spin()

medium

A. No output because callback is never called

B. Received depth image with width: data.width

C. Error: 'Image' object has no attribute 'width'

D. Received depth image with width: 640

Solution

1. Step 1: Understand the callback function

   The callback prints the width attribute of the received Image message.

2. Step 2: Recognize typical Image message fields

   sensor_msgs/Image messages include a width field with the image width, e.g., 640 pixels.

3. Final Answer:

   Received depth image with width: 640 -> Option D

4. Quick Check:

   Image.width prints actual width value [OK]

Hint: Image messages have width attribute with pixel count [OK]

Common Mistakes:

• Thinking width is a string 'data.width'
• Assuming Image message lacks width attribute
• Believing callback never triggers

4. You wrote this ROS launch snippet to start a depth camera node but get an error:

<launch>
  <node pkg="camera_pkg" type="depth_camera" name="depth_camera"/>
</launch>

What is the likely cause of the error?

medium

A. Missing closing tag for <launch>

B. The package 'camera_pkg' is not installed

C. The node type 'depth_camera' does not match the executable name

D. The node name cannot be the same as the package name

Solution

1. Step 1: Check node type and executable name

   The 'type' attribute must exactly match the executable file name in the package.

2. Step 2: Identify mismatch causing error

   If the executable is named 'depth_camera_node' but 'depth_camera' is used, ROS cannot find it.

3. Final Answer:

   The node type 'depth_camera' does not match the executable name -> Option C

4. Quick Check:

   Node type must match executable name [OK]

Hint: Node type must exactly match executable filename [OK]

Common Mistakes:

• Assuming missing closing tag causes error
• Ignoring package installation issues
• Thinking node name conflicts cause this error

5. You want to visualize depth data from a ROS topic /camera/depth/image_raw using OpenCV in Python. Which code snippet correctly converts the ROS Image message to an OpenCV image for display?

hard

A. from cv_bridge import CvBridge
bridge = CvBridge()
cv_image = bridge.imgmsg_to_cv2(data, desired_encoding='passthrough')

B. import cv2
cv_image = cv2.imread('/camera/depth/image_raw')

C. cv_image = data.data.decode('utf-8')

D. cv_image = numpy.array(data)

Solution

1. Step 1: Use cv_bridge to convert ROS Image

   cv_bridge provides imgmsg_to_cv2 to convert ROS Image messages to OpenCV format.

2. Step 2: Apply correct conversion call

   from cv_bridge import CvBridge bridge = CvBridge() cv_image = bridge.imgmsg_to_cv2(data, desired_encoding='passthrough') uses bridge.imgmsg_to_cv2 with 'passthrough' encoding, which preserves depth data correctly.

3. Final Answer:

   from cv_bridge import CvBridge bridge = CvBridge() cv_image = bridge.imgmsg_to_cv2(data, desired_encoding='passthrough') -> Option A

4. Quick Check:

   Use cv_bridge.imgmsg_to_cv2 for ROS to OpenCV [OK]

Hint: Use cv_bridge to convert ROS Image to OpenCV image [OK]

Common Mistakes:

• Trying to read ROS topic as file with cv2.imread
• Decoding raw data as UTF-8 string
• Directly converting data to numpy array without bridge