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Sensors & Fusion

Robots rely on multiple sensors to perceive their environment. Each sensor has strengths and limitations.
Sensor fusion combines data from different sensors to create a more accurate and reliable understanding of the world.

Common Robotics Sensors​

1. Cameras​

  • RGB cameras for visual perception
  • Depth cameras for 3D perception
  • Applications: object detection, SLAM, gesture recognition

2. LiDAR​

  • Measures distances using laser pulses
  • Generates 3D point clouds of the environment
  • Applications: mapping, navigation, obstacle detection

3. IMU (Inertial Measurement Unit)​

  • Measures acceleration and angular velocity
  • Helps estimate robot pose and motion
  • Applications: stabilization, navigation, odometry

4. Force/Torque Sensors​

  • Measure interaction forces at joints or end-effectors
  • Applications: manipulation, assembly, safe human-robot interaction

Why Sensor Fusion is Important​

Single sensors are prone to errors, noise, or occlusions. Sensor fusion improves:

  • Accuracy of robot localization
  • Robustness of perception in dynamic environments
  • Reliability for autonomous navigation and manipulation

Fusion Techniques​

Some common methods used in robotics:

  1. Kalman Filter – combines noisy sensor data for state estimation
  2. Extended/Unscented Kalman Filters – handle nonlinear dynamics
  3. Particle Filters – probabilistic localization in uncertain environments
  4. Complementary Filters – combine fast/slow sensor signals

Learning Outcomes for Chapter 2​

By the end of this chapter, you should be able to:

  • Identify core sensors used in robotics
  • Understand the strengths and limitations of each sensor
  • Implement basic sensor fusion for robot perception
  • Apply sensor data to navigation, mapping, and manipulation tasks

Next Chapter → Simulating Robot Movements & Environment Interactions