Sensor Fusion Expert

Overview

Sensor fusion is the key technology for improving drone state estimation accuracy. In this project, you will learn how to develop multi-sensor data fusion systems.

What You’ll Learn

• Kalman Filter (KF) principles
• Extended Kalman Filter (EKF)
• Unscented Kalman Filter (UKF)
• Multi-sensor fusion

Materials Needed

Item	Quantity	Notes
Fully Assembled Drone	1	With MPU6050, BMP280, VL53L0X sensors
Computer	1	With VS Code + ESP-IDF environment
USB Cable	1	For programming

Step 1: Understand Sensor Fusion

The goal of sensor fusion is to combine information from multiple sensors to obtain more accurate state estimation.

Common sensor fusion algorithms:

• Kalman Filter (KF)
• Extended Kalman Filter (EKF)
• Unscented Kalman Filter (UKF)
• Particle Filter (PF)

Step 2: Algorithm Selection

Select sensor fusion algorithm suitable for drones, such as EKF (for nonlinear systems).

Design state vector and observation vector, establish system model.

Step 3: Code Implementation

Create a new estimator file in components/core/crazyflie/modules/src/estimator:

estimator_ekf.c

typedef struct {
    float x[12];      // State vector
    float P[12][12];  // Covariance matrix
    float Q[12][12];  // Process noise covariance
    float R[6][6];    // Measurement noise covariance
} EKF_State;

void ekf_predict(EKF_State *ekf, float dt) {
    // Prediction step
    // x = f(x, u)
    // P = F * P * F' + Q
}

void ekf_update(EKF_State *ekf, float *measurement) {
    // Update step
    // K = P * H' * (H * P * H' + R)^-1
    // x = x + K * (z - h(x))
    // P = (I - K * H) * P
}

void ekf_fuse_sensors(EKF_State *ekf,
                       MPU6050_Data *imu,
                       BMP280_Data *baro,
                       VL53L0X_Data *tof) {
    // Fuse IMU data
    ekf_predict(ekf, 0.01);

    // Fuse barometer data
    float z_measurement[1] = {baro->altitude};
    ekf_update(ekf, z_measurement);

    // Fuse TOF data
    float tof_measurement[1] = {tof->distance};
    ekf_update(ekf, tof_measurement);
}

Step 4: Compile, Flash and Test

1. Compile and flash the code
2. Flight test, compare state estimation accuracy between new estimator and original estimator

Troubleshooting

Estimated value diverges

• Check noise covariance matrices Q and R
• Confirm sensor data quality

Response delay

• Optimize algorithm calculation efficiency
• Reduce state vector dimension

Achievement

Congratulations! You have mastered the core technology of sensor fusion, which is the key to drone state estimation!

Next Steps

In the next project, you will learn how to use camera optical flow data for indoor positioning.