MPU-6050 IMU Data Fusion with Complementary Filter | Arduino & Processing

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Sensor Fusion

Sensor Fusion

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Playing Section
  • 1

    Demonstrates MPU 6050 IMU with accelerometer and gyroscope wired to Arduino.

  • 2

    Shows raw sensor outputs: jittery accelerometer and drifting gyroscope data.

  • 3

    Highlights complementary filter for smooth, accurate orientation tracking.

Basic understanding of IMU sensors, specifically the operational differences between an accelerometer (measures gravity and linear acceleration, prone to noise) and a gyroscope (measures angular velocity, prone to integration drift).
Fundamentals of Arduino programming, including reading sensor registers over I2C communication and working with basic data structures.
Basic trigonometry and coordinate geometry concepts, particularly understanding Euler angles (pitch, roll, and yaw) and using trigonometric functions like arctangent to calculate tilt.
Elementary familiarity with the Processing development environment and Serial communication protocols to stream data between hardware and software.
Implementation of advanced state estimation and data fusion algorithms, such as the Kalman Filter or Extended Kalman Filter (EKF), for superior noise rejection.
Transitioning from Euler angles to Quaternions for 3D orientation representation to prevent mathematical singularities like gimbal lock.
Integrating a magnetometer (upgrading to a 9-DoF system) to calculate an absolute heading/yaw and implementing Madgwick or Mahony filters.
Applying filtered orientation data to closed-loop control systems, such as PID-controlled self-balancing robots, active camera gimbals, or drone flight controllers.
174.2K views1.4Klikes2:07@geekmomprojectsOriginal Release: 2013-04-02

A complementary filter combines accelerometer and gyroscope data to provide more accurate and stable orientation measurements than either sensor alone; accelerometers are sensitive to disturbances but stable over time, while gyroscopes drift systematically but respond quickly to rotation, making their combination particularly effective for real-time motion tracking applications.