Index / Work / N° 10
Project N°10 of 24
CategoryRobotics · Control
Year2025

IMU Sensor Fusion & State Estimation

Engineered a state estimation algorithm in MATLAB to compute the precise roll, pitch, and yaw orientation of a body by fusing raw 6-DOF IMU data.

Key Engineering Contributions

  1. 01
    Sensor Fusion: Processed raw angular velocity (gyroscope) and linear acceleration (accelerometer) data to independently estimate orientation angles.
  2. 02
    Kalman Filtering: Implemented a Kalman Filter algorithm to fuse the independent sensor readings, effectively mitigating gyroscope drift and accelerometer noise to produce a highly accurate, stable orientation estimate.
  3. 03
    Functional Calibration: Executed functional calibration procedures to compute and plot calibrated 3D coordinate axes directly from raw kinematic movement data.

Visual Documentation

Plot of raw gyroscope, accelerometer, Kalman filter, and factory Kalman filter r
Figure 1
01.png
Plot of raw gyroscope, accelerometer, Kalman filter, and factory Kalman filter roll angles.
Plot of raw gyroscope, accelerometer, Kalman filter, and factory Kalman filter roll angles.
Plot of raw gyroscope, raw accelerometer, Kalman filter, and factory Kalman filt
Figure 2
02.png
Plot of raw gyroscope, raw accelerometer, Kalman filter, and factory Kalman filter pitch angles.
Plot of raw gyroscope, raw accelerometer, Kalman filter, and factory Kalman filter pitch angles.
Plot of raw gyroscope and factory Kalman filter yaw angles.
Figure 3
03.png
Plot of raw gyroscope and factory Kalman filter yaw angles.
Plot of raw gyroscope and factory Kalman filter yaw angles.