基于Kalman滤波算法的IMU代码

以下是基于Kalman滤波算法的IMU代码示例，使用Arduino编写：

#include <Kalman.h>

// Define the matrices and vectors for the Kalman filter
Kalman kalmanX;      // Create the Kalman objects for X, Y and Z
Kalman kalmanY;
Kalman kalmanZ;
float accX, accY, accZ;
float gyroX, gyroY, gyroZ;
float roll, pitch, yaw;

void setup() {
  // Initialize the Kalman filters
  kalmanX.setAngle(roll);   // Set the starting angle for X
  kalmanY.setAngle(pitch);  // Set the starting angle for Y
  kalmanZ.setAngle(yaw);    // Set the starting angle for Z
}

void loop() {
  // Read the accelerometer and gyroscope data
  accX = analogRead(A0);
  accY = analogRead(A1);
  accZ = analogRead(A2);
  gyroX = analogRead(A3);
  gyroY = analogRead(A4);
  gyroZ = analogRead(A5);

  // Calculate the angle from the accelerometer data
  roll = atan2(accY, accZ) * 180 / PI;
  pitch = atan2(-accX, sqrt(accY * accY + accZ * accZ)) * 180 / PI;

  // Calculate the angle from the gyroscope data
  float dt = 0.01;  // Time interval between readings
  kalmanX.setQangle(0.001);  // Set the process noise covariance
  kalmanY.setQangle(0.001);
  kalmanZ.setQangle(0.001);
  kalmanX.setRmeasure(0.03);  // Set the measurement noise covariance
  kalmanY.setRmeasure(0.03);
  kalmanZ.setRmeasure(0.03);
  roll += gyroX * dt;
  pitch += gyroY * dt;
  yaw += gyroZ * dt;

  // Update the Kalman filters with the accelerometer and gyroscope data
  kalmanX.setAngle(roll);
  kalmanY.setAngle(pitch);
  kalmanZ.setAngle(yaw);
  roll = kalmanX.getAngle();
  pitch = kalmanY.getAngle();
  yaw = kalmanZ.getAngle();

  // Print the roll, pitch and yaw angles
  Serial.print("Roll: ");
  Serial.print(roll);
  Serial.print(", Pitch: ");
  Serial.print(pitch);
  Serial.print(", Yaw: ");
  Serial.println(yaw);

  // Delay for a short time before the next reading
  delay(10);
}

这段代码使用Kalman滤波算法对加速度计和陀螺仪数据进行滤波，以获取更加精确的姿态角数据。在代码中，我们使用了Kalman库提供的Kalman对象来实现滤波。首先，我们初始化Kalman对象，并设置初始角度。然后，我们使用加速度计数据计算出姿态角roll和pitch，并使用陀螺仪数据更新角度值。最后，我们将更新后的角度值传递给Kalman对象进行滤波，以获得更加准确的姿态角数据。