How to Fuse MPU6050 Data with Kalman Filter for STM32 Motion Tracking

Introduction

The MPU6050 is a versatile sensor that combines a 3-axis accelerometer and a 3-axis gyroscope, making it ideal for motion tracking applications. To enhance the accuracy of motion tracking, we can fuse the data from the MPU6050 using a Kalman filter. This tutorial will guide you through the process of setting up the MPU6050, collecting data, and applying a Kalman filter for precise motion tracking using an STM32 microcontroller.

Prerequisites

• Basic understanding of embedded systems and C programming
• STM32 microcontroller development board (e.g., STM32F4, STM32F1)
• MPU6050 sensor module
• Wires for connections
• STM32CubeIDE or any compatible IDE for STM32 development
• Basic knowledge of I2C communication

Parts/Tools

• 1 x STM32 development board
• 1 x MPU6050 sensor module
• 1 x Breadboard (optional)
• Jumper wires

Steps

1. Wire the MPU6050 to the STM32
   1. Connect the VCC pin of the MPU6050 to the 3.3V pin on the STM32.
   2. Connect the GND pin of the MPU6050 to the GND pin on the STM32.
   3. Connect the SDA pin of the MPU6050 to the SDA pin on the STM32 (typically pin B7).
   4. Connect the SCL pin of the MPU6050 to the SCL pin on the STM32 (typically pin B6).
2. Set up the STM32 project
   1. Open STM32CubeIDE and create a new project.
   2. Select your STM32 board and configure I2C in the “Pinout & Configuration” tab.
   3. Enable the I2C peripheral and set the parameters (usually standard mode 100 kHz).
   4. Generate the project code.
3. Install the necessary libraries
   1. Download the MPU6050 library from a reliable source or create your own I2C communication functions.
   2. Add the library files to your STM32 project.
4. Initialize the MPU6050

   
   #include "mpu6050.h"
   
   void MPU6050_Init() {
       // Initialize I2C
       HAL_I2C_Mem_Write(&hi2c1, MPU6050_ADDRESS, PWR_MGMT_1, 1, &data, 1, HAL_MAX_DELAY);
       // Wake up the sensor
       data = 0; 
       HAL_I2C_Mem_Write(&hi2c1, MPU6050_ADDRESS, PWR_MGMT_1, 1, &data, 1, HAL_MAX_DELAY);
   }
           

5. Read sensor data

   
   void MPU6050_Read_Accel_Gyro(int16_t *ax, int16_t *ay, int16_t *az, int16_t *gx, int16_t *gy, int16_t *gz) {
       uint8_t buffer[14];
       HAL_I2C_Mem_Read(&hi2c1, MPU6050_ADDRESS, ACCEL_XOUT_H, 1, buffer, 14, HAL_MAX_DELAY);
       *ax = (buffer[0] << 8) | buffer[1];
       *ay = (buffer[2] << 8) | buffer[3];
       *az = (buffer[4] << 8) | buffer[5];
       *gx = (buffer[8] << 8) | buffer[9];
       *gy = (buffer[10] << 8) | buffer[11];
       *gz = (buffer[12] << 8) | buffer[13];
   }
           

6. Implement the Kalman Filter
   1. Define the Kalman filter variables:

   
   typedef struct {
       float q; // Process noise covariance
       float r; // Measurement noise covariance
       float x; // Value
       float p; // Estimation error covariance
       float k; // Kalman gain
   } KalmanFilter;
   
   KalmanFilter kalman;
               

   2. Initialize the Kalman filter parameters:

   
   void Kalman_Init(KalmanFilter *kf) {
       kf->q = 0.01; // Process noise covariance
       kf->r = 0.1;  // Measurement noise covariance
       kf->x = 0;    // Initial value
       kf->p = 1;    // Initial estimation error covariance
   }
               

   3. Update the Kalman filter with new measurements:

   
   float Kalman_Update(KalmanFilter *kf, float measurement) {
       // Prediction update
       kf->p += kf->q;
   
       // Measurement update
       kf->k = kf->p / (kf->p + kf->r);
       kf->x += kf->k * (measurement - kf->x);
       kf->p *= (1 - kf->k);
       return kf->x;
   }
               

7. Combine data and filter

   
   void Process_Data() {
       int16_t ax, ay, az, gx, gy, gz;
       MPU6050_Read_Accel_Gyro(&ax, &ay, &az, &gx, &gy, &gz);
       
       float filtered_angle = Kalman_Update(&kalman, atan2(ay, az) * 180 / M_PI);
       // Use filtered_angle for further processing
   }
           

Troubleshooting

• Sensor not responding: Ensure proper wiring and check the I2C address.
• Inconsistent readings: Verify the Kalman filter parameters and update rates.
• Compile errors: Check library inclusions and ensure all dependencies are correctly linked.

Conclusion

Fusing MPU6050 accelerometer and gyroscope data using a Kalman filter allows for enhanced motion tracking accuracy in STM32 applications. By following this tutorial, you should now be able to set up the MPU6050, read sensor data, and implement a Kalman filter to improve your application’s performance. Experiment with different parameters of the Kalman filter to achieve optimal results for your specific use case.