Sensor Fusion

Sensor Fusion v/s Sensor Integration

Sensor integration is the process of connecting and configuring multiple sensors in a system so that their data can be accessed and used.

Focus

  • Physical and software connection
  • Data acquisition and communication
  • Ensuring sensors are properly interfaced with the controller or computer

Examples

  • Connecting IMU and GPS via I2C and UART to a microcontroller
  • Writing ROS2 drivers or launching sensor nodes to access sensor data
  • Syncing timestamps from different sensors (e.g., camera and LiDAR)

Goal

Make raw data from sensors available for use.

Sensor Fusion

Sensor fusion is the mathematical process of combining data from multiple sensors to produce a more accurate, reliable, or comprehensive estimate of the environment or system state.

Focus

  • Algorithms and estimation
  • Accuracy, reliability, and robustness
  • Overcoming individual sensor limitations

Examples

  • Using Kalman Filter to fuse IMU and GPS for accurate position estimation
  • Combining camera and LiDAR data for better object detection
  • Merging ultrasonic and IR data for obstacle distance estimation

Goal

Improve perception or estimation by leveraging complementary strengths of multiple sensors.

Summary Table

Aspect Sensor Integration Sensor Fusion
What it does Connects and sets up sensors Combines data from sensors
Focus Hardware + basic software Algorithms and data processing
Level Low-level system setup High-level information processing
Tools/Techniques Interfaces (I2C, SPI, UART), Drivers, ROS2 Kalman Filter, Bayesian Estimation, DNNs
Output Raw or calibrated sensor data Refined or estimated data (e.g., position, map)

Example: Drone

  • Sensor Integration: Connecting IMU, GPS, and barometer to Pixhawk or Jetson Nano; ensuring each publishes data to a ROS2 topic.
  • Sensor Fusion: Using an Extended Kalman Filter (EKF) to combine IMU + GPS + barometer to estimate drone position with higher accuracy.

Understanding Sensor Fusion: What to Focus On

1. Sensor Hardware Level — What to Know

You are not designing the sensors, but using them effectively. So focus on:

Power Requirements

  • Each sensor needs a specific voltage and current.
  • Essential for battery-powered or embedded systems.
  • Why? To avoid brownouts, overheating, or damage.

Communication Protocols

  • Common protocols: I2C, SPI, UART, CAN, USB.
  • You need compatible ports on your microcontroller or computer.
  • Why? To correctly read and sync data.

Data Output Format

  • Some sensors give raw data (e.g., accelerometer in m/s²).
  • Others give processed values (e.g., GPS gives coordinates).
  • Why? To interpret and fuse data correctly.

2. Software Level — Sensor Integration

Once sensors are powered and connected, ensure their data is accessible in software:

  • Use or write drivers (ROS2 nodes, C/C++/Python interfaces).
  • Ensure data is publishing (e.g., /imu/data, /gps/fix topics).
  • Sync data sources using timestamps if needed.

3. Fusion Algorithms — Combining Sensor Data

Popular Algorithms

  • Kalman Filter (KF) — For linear systems.
  • Extended Kalman Filter (EKF) — For nonlinear systems.
  • Complementary Filter — Simple, good for fusing gyroscope and accelerometer.
  • Particle Filter — Probabilistic, used in localization.
  • Deep Learning Models — For perception tasks (e.g., image + LiDAR).

What Fusion Algorithms Do

  • Filter out noise from raw data.
  • Merge complementary data types (e.g., IMU + GPS).
  • Handle delays, dropout, and uncertainties.

Key Concepts to Understand

  • State vector: What are you estimating? (e.g., position, velocity, orientation)
  • Measurement model: How sensor data maps to the state.
  • Update rate: Fusion must handle fast and slow sensors gracefully.

4. Example: Fusing IMU + GPS

Step What You Focus On
Choose Sensors Check power, communication (e.g., IMU via I2C, GPS via UART)
Integrate Write or use drivers, confirm data on topics
Fuse Use EKF to combine GPS and IMU for accurate position
Use Output Feed fused result to controller or mapping system

🧑‍💻 Sensor Fusion Tutorials

First we will focus on individual topics. Then we will move forward to sensors and will see how these topics reflect on the sensors.