Attitude Magician

Overview

IMU (Inertial Measurement Unit) is the core sensor for drone attitude estimation. In this project, you will learn how to read IMU data and calculate drone attitude using complementary filtering algorithm.

What You’ll Learn

• IMU sensor principles
• Complementary filtering algorithm
• Quaternion attitude representation
• Attitude estimation

Materials Needed

Step 1: Open the Project

Extract imu_fusion.zip and open with VS Code.

Step 2: View Complementary Filtering Code

Open sensfusion6.c and find the sensfusion6UpdateQ function:

void sensfusion6UpdateQ(float gx, float gy, float gz, float ax, float ay, float az, float dt) {
  // 1. Gyroscope integration updates attitude (fast but drifts)
  // 2. Accelerometer calibrates attitude (accurate but affected by vibration)
  // 3. Complementary filtering: fuses both data
  beta = 0.02; // Filter coefficient, between 0-1
}

Step 3: Adjust Filter Coefficient

Test 1: beta=0.1 (Trust accelerometer more)

• Phenomenon: Fast response, but attitude data fluctuates when shaking

Test 2: beta=0.01 (Trust gyroscope more)

• Phenomenon: Stable, but drifts over time

Step 4: Find Optimal Value

Adjust beta until attitude data is stable when the drone is shaking, and there is no obvious drift during long flights.

Step 5: Challenge

Rotate the drone quickly and observe if attitude data can follow accurately.

Troubleshooting

Attitude data drift

• Decrease beta value
• Check gyroscope calibration

Attitude data fluctuation

• Increase beta value
• Add low-pass filter

Achievement

Congratulations! You have mastered the core technology of attitude estimation and understood the complementary filtering algorithm!

Next Steps

In the next project, you will learn how to read and analyze sensor data.

Continue to Project 05: Data Magician →