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Module 2: Sensors and Perception for Humanoid Robots

Focus: How humanoid robots sense and understand their environment

This module builds on Module 1 (ROS 2 fundamentals) to explore how humanoid robots perceive the world through multiple sensor modalities. You'll learn about cameras, depth sensors, IMUs, and how to combine them through sensor fusion for robust perception.

Learning Goals​

  • Understand camera systems and computer vision basics for humanoid robots
  • Learn LiDAR and depth sensing technologies
  • Master IMU (Inertial Measurement Unit) for balance and orientation
  • Apply sensor fusion techniques to combine multiple sensor inputs

Prerequisites​

  • Module 1 Complete: Understanding of ROS 2 nodes, topics, publishers, subscribers, and message types
  • Python 3.11+: Object-oriented programming, type hints, imports
  • Basic Linear Algebra: Vectors, matrices, transformations (for sensor fusion)

Module Structure​

Lesson 1: Camera Systems and Computer Vision​

Time: 30-45 minutes | Level: Beginner

Learn how humanoid robots use cameras to perceive their environment, including camera types (monocular, stereo, RGB-D), ROS 2 Image messages, and camera placement strategies for humanoid tasks.

Key Concepts: Camera types, sensor_msgs/Image, field of view, resolution

Lesson 2: Depth Sensing Technologies​

Time: 30-45 minutes | Level: Beginner-Intermediate

Explore how humanoid robots measure distances using LiDAR, structured light cameras, and time-of-flight sensors. Understand point clouds and depth data representation in ROS 2.

Key Concepts: LiDAR, depth cameras, sensor_msgs/PointCloud2, sensor_msgs/LaserScan

Lesson 3: IMU and Proprioception​

Time: 30-45 minutes | Level: Intermediate

Understand how humanoid robots maintain balance and know their body position using Inertial Measurement Units (IMUs). Learn about accelerometers, gyroscopes, magnetometers, and proprioception.

Key Concepts: IMU components, sensor_msgs/Imu, drift mitigation, balance control

Lesson 4: Sensor Fusion Techniques​

Time: 30-45 minutes | Level: Intermediate

Discover how humanoid robots combine data from multiple sensors (cameras, LiDAR, IMU) to create robust perception. Learn Kalman filtering, complementary filtering, and visual-inertial odometry concepts.

Key Concepts: Sensor fusion, Kalman filter, visual-inertial odometry, robot_localization

Capstone Project: Multi-Sensor Perception System Design​

Time: 60-90 minutes | Level: Intermediate

Apply your knowledge from all 4 lessons to design a multi-sensor perception system for a humanoid robot task. Select sensors, justify fusion strategies, and sketch ROS 2 node architecture.

Integration: Cameras + Depth Sensors + IMU + Sensor Fusion

Quiz: Module 2 Assessment​

Time: 15-20 minutes | Questions: 15-20

Test your understanding of sensor perception concepts across all lessons. Includes conceptual questions, code-reading exercises, and scenario-based problems.

Learning Approach​

Each lesson follows a consistent structure:

  • 💬 AI Colearning Prompts: Explore concepts with AI assistants (Claude, ChatGPT)
  • 🎓 Expert Insights: Learn best practices and common pitfalls
  • 🤝 Practice Exercises: Apply concepts through design challenges
  • Real-World Examples: Case studies from Boston Dynamics, Tesla Optimus, Agility Robotics

Hands-On Practice​

While this module focuses on conceptual understanding, you can enhance learning with:

  • RViz Visualization: Visualize sensor data (camera images, point clouds, IMU axes)
  • Gazebo Simulation: Generate synthetic sensor data without hardware
  • ROS 2 Tools: Use ros2 topic echo to inspect sensor messages

Success Criteria​

After completing this module, you should be able to:

  • ✅ Differentiate between monocular, stereo, and RGB-D cameras
  • ✅ Explain LiDAR, structured light, and time-of-flight depth sensing
  • ✅ Describe IMU components and their role in balance control
  • ✅ Design a sensor fusion strategy for a humanoid robot task
  • ✅ Score 80%+ on the module quiz

Next Module​

After mastering sensor perception, you'll be ready for:

Module 3: Motion Planning and Control (coming soon) Learn how humanoid robots plan collision-free paths and execute stable locomotion using sensor feedback.

Estimated Total Time: 3-4 hours (including capstone and quiz) Target Audience: CS students with Python + ROS 2 Module 1 knowledge