Object detection pipeline in ROS - Mini Project: Build & Apply

Object detection pipeline in ROS

📖 Scenario: You are building a simple object detection pipeline using ROS (Robot Operating System). The pipeline will subscribe to a camera image topic, process the image to detect objects, and publish the detection results.This project simulates a real robot scenario where a camera captures images, and the system identifies objects in the scene.

🎯 Goal: Create a ROS node that subscribes to the /camera/image_raw topic, processes the images to detect objects, and publishes the detected object names on the /detected_objects topic.

📋 What You'll Learn

Create a subscriber to /camera/image_raw topic

Create a publisher to /detected_objects topic

Implement a simple object detection logic (mock detection)

Publish detected object names as strings

💡 Why This Matters

🌍 Real World

Robots use object detection pipelines to understand their environment and interact with objects, such as picking items or avoiding obstacles.

💼 Career

ROS skills and object detection are essential for robotics engineers, automation developers, and researchers working on intelligent robotic systems.

1

Setup ROS node and subscriber

Create a ROS node named object_detector and subscribe to the /camera/image_raw topic using rospy.Subscriber. Use sensor_msgs.msg.Image as the message type and image_callback as the callback function.

ROS

# Initialize ROS node and subscriber
import rospy
from sensor_msgs.msg import Image

# Your code here

Hint

Use rospy.init_node to create the node and rospy.Subscriber to subscribe to the image topic.

2

Create publisher for detected objects

Add a publisher named object_pub that publishes std_msgs.msg.String messages on the /detected_objects topic. Initialize it before the subscriber.

ROS

import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String

# Your code here

def image_callback(msg):
    pass

rospy.init_node('object_detector')
rospy.Subscriber('/camera/image_raw', Image, image_callback)
rospy.spin()

Hint

Use rospy.Publisher with topic /detected_objects and message type String.

3

Implement mock object detection logic

Inside the image_callback function, create a variable detected_object and set it to the string 'bottle' to simulate detection. Then create a std_msgs.msg.String message with this data and publish it using object_pub.publish().

ROS

import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String

object_pub = rospy.Publisher('/detected_objects', String, queue_size=10)

def image_callback(msg):
    # Your code here

rospy.init_node('object_detector')
rospy.Subscriber('/camera/image_raw', Image, image_callback)
rospy.spin()

Hint

Create a String message with the detected object and publish it.

4

Add ROS node shutdown handling

Add a try-except block around rospy.spin() to catch rospy.ROSInterruptException and allow clean shutdown of the object_detector node.

ROS

import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String

object_pub = rospy.Publisher('/detected_objects', String, queue_size=10)

def image_callback(msg):
    detected_object = 'bottle'
    msg_out = String(data=detected_object)
    object_pub.publish(msg_out)

# Your code here

Hint

Wrap rospy.spin() in a try-except block to handle shutdown.

Practice

1. What is the main purpose of an object detection pipeline in ROS?

easy

A. To help robots find and recognize objects around them

B. To control the robot's movement speed

C. To manage battery usage efficiently

D. To create 3D maps of the environment

Solution

1. Step 1: Understand the role of object detection in robotics

   Object detection helps robots identify and locate objects in their surroundings.

2. Step 2: Differentiate from other robot functions

   Controlling speed, battery, or mapping are different tasks not directly related to object detection.

3. Final Answer:

   To help robots find and recognize objects around them -> Option A

4. Quick Check:

   Object detection = finding objects [OK]

Hint: Object detection means finding objects, not moving or mapping [OK]

Common Mistakes:

• Confusing object detection with robot movement control
• Thinking object detection manages battery
• Mixing object detection with mapping tasks

2. Which ROS command is used to start an object detection pipeline defined in a launch file?

easy

A. rosrun object_detection detect_node

B. rosstart object_detection pipeline

C. roslaunch object_detection pipeline.launch

D. rosinit object_detection start

Solution

1. Step 1: Identify the command to start multiple ROS nodes together

   roslaunch is used to start nodes defined in a launch file.

2. Step 2: Check the options for correct syntax

   roslaunch object_detection pipeline.launch matches the correct syntax to launch the pipeline.

3. Final Answer:

   roslaunch object_detection pipeline.launch -> Option C

4. Quick Check:

   Use roslaunch to start launch files [OK]

Hint: Use roslaunch for launch files, rosrun for single nodes [OK]

Common Mistakes:

• Using rosrun instead of roslaunch for launch files
• Using non-existent commands like rosstart or rosinit
• Missing the .launch extension

3. Given this ROS Python snippet for object detection node:

import rospy
from sensor_msgs.msg import Image

def callback(data):
    rospy.loginfo('Image received')

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

What will be printed when an image message arrives on /camera/image_raw?

medium

A. Image processed successfully

B. No output, node does not subscribe

C. Error: callback not defined

D. Image received

Solution

1. Step 1: Understand the subscriber and callback

   The node subscribes to '/camera/image_raw' and calls callback on message arrival.

2. Step 2: Check what callback does

   Callback prints 'Image received' using rospy.loginfo when called.

3. Final Answer:

   Image received -> Option D

4. Quick Check:

   Callback prints 'Image received' on message [OK]

Hint: Subscriber calls callback printing 'Image received' [OK]

Common Mistakes:

• Thinking no output occurs without explicit print
• Confusing rospy.loginfo with error
• Assuming callback is undefined

4. You wrote this launch file snippet to start your object detection node:

<launch>
  <node pkg="object_detection" type="detector.py" name="detector" />
</launch>

But when you run roslaunch, you get an error saying the node type is not found. What is the likely problem?

medium

A. The node file 'detector.py' is not marked executable

B. The package name 'object_detection' is incorrect

C. The launch file syntax is invalid

D. The node name 'detector' is already in use

Solution

1. Step 1: Check common causes for 'type not found' error

   This error often happens if the node script is not executable.

2. Step 2: Verify other options

   Package name or syntax errors cause different errors; node name conflict causes runtime warnings.

3. Final Answer:

   The node file 'detector.py' is not marked executable -> Option A

4. Quick Check:

   Make node scripts executable to avoid 'type not found' [OK]

Hint: Check file permissions if node type not found error appears [OK]

Common Mistakes:

• Assuming package name is wrong without checking
• Thinking launch syntax is always the cause
• Ignoring file permission issues

5. You want to improve your ROS object detection pipeline by adding a node that filters detected objects by size before publishing results. Which approach best fits this requirement?

hard

A. Publish filtered results directly from the detection node without a new node

B. Add a new node subscribing to detection results, filter objects, then publish filtered results

C. Change the launch file to exclude the detection node and only run the filter node

D. Modify the camera driver node to filter objects before detection

Solution

1. Step 1: Understand modular ROS pipeline design

   Adding a separate node to filter results keeps pipeline modular and clear.

2. Step 2: Evaluate other options

   Modifying camera driver or excluding detection breaks pipeline; filtering inside detection node reduces modularity.

3. Final Answer:

   Add a new node subscribing to detection results, filter objects, then publish filtered results -> Option B

4. Quick Check:

   Use separate nodes for filtering to keep pipeline modular [OK]

Hint: Add filter as separate node subscribing to detection output [OK]

Common Mistakes:

• Modifying unrelated nodes like camera driver
• Removing detection node from launch file
• Mixing filtering inside detection node reducing clarity