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Chapter 10: Sensor Fusion

Learning Objectives​

By the end of this chapter, you will:

  • Understand why robots use multiple sensors
  • Learn sensor fusion techniques
  • Understand the Kalman filter
  • Learn how to combine sensor data

Introduction​

Real robots rarely rely on a single sensor. Combining data from multiple sensors provides more robust and accurate perception.

Placeholder Content​

Topics to be covered:

  • Multi-sensor systems
  • Data fusion algorithms
  • Kalman filters
  • Uncertainty estimation

Did You Know?​

Interesting fact about sensor fusion.

Real-World Examples​

  1. Autonomous Vehicles - Combine cameras, LiDAR, and radar
  2. Robotic Arms - Use force sensors and position sensors
  3. Mobile Robots - Combine odometry, IMU, and LiDAR

Key Takeaways​

  • To be added

Try It!​

Sensor fusion simulation.

Exercises​

  1. Question
  2. Question
  3. Question

Further Reading​

  • Module 3: Control and Motion Planning
  • Chapter 6: Understanding Sensors

Next Module: Module 3: Control and Motion Planning