Integrate MPU6050 with Kalman Filter for Motion Tracking on STM32

Integrating MPU6050 Data with Kalman Filter on STM32

The MPU6050 sensor combines a 3-axis accelerometer and a 3-axis gyroscope, making it a popular choice for motion tracking applications. This tutorial will guide you through integrating MPU6050 data using a Kalman filter on an STM32 microcontroller for precise motion tracking. Follow the steps below to set up your hardware and software.

Prerequisites

• Basic understanding of microcontrollers and embedded systems
• Familiarity with C/C++ programming
• STM32 development environment set up (e.g., STM32CubeIDE)
• MPU6050 sensor module
• Wiring tools (breadboard, jumper wires)

Parts/Tools

• STM32 Microcontroller (e.g., STM32F4 series)
• MPU6050 Sensor Module
• Connecting Wires
• STM32CubeIDE or any preferred IDE
• Library for I2C communication

Steps

1. Wiring the MPU6050 to STM32
   • Connect MPU6050 VCC to STM32 3.3V.
   • Connect MPU6050 GND to STM32 Ground.
   • Connect MPU6050 SDA to STM32 I2C SDA Pin (e.g., PA10).
   • Connect MPU6050 SCL to STM32 I2C SCL Pin (e.g., PA9).
2. Setting Up STM32CubeIDE
   • Create a new STM32 project.
   • Enable I2C peripheral in the configuration settings.
   • Include necessary libraries for I2C communication.
3. Installing the MPU6050 Library

   Use an existing library or create your own for interfacing with the MPU6050. Below is a simple example of how to read raw data:

   
   #include "MPU6050.h"
   
   MPU6050 mpu;
   void setup() {
       Wire.begin(); // Initialize I2C
       mpu.initialize(); // Initialize MPU6050
   }
   
   void loop() {
       int16_t ax, ay, az, gx, gy, gz;
       mpu.getMotion6(&ax, &ay, &az, &gx, &gy, &gz);
       // Use ax, ay, az, gx, gy, gz for further processing
   }
           

4. Implementing the Kalman Filter

   The Kalman filter helps in estimating the state of a dynamic system. Below is a basic implementation:

   
   class Kalman {
   public:
       Kalman() : q(0.001), r(0.1), x(0), p(1) {}
   
       float getAngle(float newAngle, float newRate, float dt) {
           // Predict
           p += q;
           x += newRate * dt;
   
           // Update
           float k = p / (p + r);
           x += k * (newAngle - x);
           p *= (1 - k);
           return x;
       }
   
   private:
       float q; // Process noise covariance
       float r; // Measurement noise covariance
       float x; // Estimated value
       float p; // Estimation error covariance
   };
           

5. Combining Data from MPU6050 with Kalman Filter

   In your loop, combine the accelerometer and gyroscope data using the Kalman filter:

   
   Kalman kalmanX, kalmanY;
   void loop() {
       int16_t ax, ay, az, gx, gy, gz;
       mpu.getMotion6(&ax, &ay, &az, &gx, &gy, &gz);
   
       float dt = 0.01; // Assume 100Hz sampling rate
       float angleX = atan2(ay, az) * 180 / M_PI;
       float angleY = atan2(ax, az) * 180 / M_PI;
   
       float filteredX = kalmanX.getAngle(angleX, gx, dt);
       float filteredY = kalmanY.getAngle(angleY, gy, dt);
   }
           

Troubleshooting

• Sensor Not Responding: Check the wiring connections and ensure the I2C address is correct.
• Inaccurate Readings: Re-calibrate the sensor and ensure the Kalman filter parameters are tuned properly.
• Compilation Errors: Ensure all libraries are correctly included and that your STM32CubeIDE project settings are configured properly.

Conclusion

Integrating the MPU6050 with a Kalman filter on an STM32 microcontroller provides a robust solution for precise motion tracking. By following the steps outlined in this tutorial, you can gather accurate motion data for various applications. Experiment with different filtering parameters to optimize performance for your specific use case.