Kalman Filter for MPU9250 IMU

This Arduino library provides a Kalman filter implementation for the MPU9250 IMU sensor, offering accurate orientation estimation (roll, pitch, and yaw) by fusing accelerometer and gyroscope data.

Features

• Kalman filter implementation for 3-axis orientation estimation
• Support for MPU9250 IMU sensor
• Configurable filter parameters for optimal performance
• Raw and filtered angle outputs
• Automatic calibration support
• Easy-to-use interface

Installation

1. Download this repository as a ZIP file
2. In the Arduino IDE, go to Sketch > Include Library > Add .ZIP Library
3. Select the downloaded ZIP file

Hardware Requirements

• Arduino-compatible board
• MPU9250 IMU sensor
• I2C connections (SDA and SCL pins)

Usage

Basic Usage

#include <Kalman_MPU9250.h>

// Create IMU object with default settings
Kalman_MPU9250 IMU();

// Or specify number of samples for calibration
Kalman_MPU9250 IMU(500);

// Or specify both samples and Kalman filter parameters
Kalman_MPU9250 IMU(500, 0.001, 0.003, 0.03, 0.001, 0.003, 0.03, 0.001, 0.003, 0.03);

void setup() {
  Serial.begin(115200);
  IMU.begin();
}

void loop() {
  IMU.update();
  
  // Get filtered angles
  float roll = IMU.getRoll();
  float pitch = IMU.getPitch();
  float yaw = IMU.getYaw();
  
  // Get raw angles
  float rawRoll = IMU.getRawRoll();
  float rawPitch = IMU.getRawPitch();
  float rawYaw = IMU.getRawYaw();
}

Available Methods

• begin(): Initialize the IMU and Kalman filter
• update(): Update the sensor readings and filter calculations
• getRoll(): Get filtered roll angle
• getPitch(): Get filtered pitch angle
• getYaw(): Get filtered yaw angle
• getRawRoll(): Get raw roll angle
• getRawPitch(): Get raw pitch angle
• getRawYaw(): Get raw yaw angle

Kalman Filter Parameters

The Kalman filter parameters can be customized for each axis:

• Q_angle: Process noise covariance for the angle
• Q_bias: Process noise covariance for the bias
• R_measure: Measurement noise covariance

Default values are provided, but you can adjust them based on your specific requirements and sensor characteristics.

Examples

Check the examples folder for complete usage examples.

License

This project is open source and available under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.