2014-11-25 128 views
1

以下是SparkFun和Adafruit的代碼片段,它使用MPU-9150將3軸加速度,陀螺儀和指南針輸出到顯示器。我試圖修改代碼以便打印到串口,因爲我沒有顯示器可供使用。但是,無論芯片的位置或移動如何,我都會得到相同的輸出。MPU-9150 XYZ值輸出不正確

輸出:

ax = 0.00 ay = 0.00 az = 0.00 mg 
gx = 0.00 gy = 0.00 gz = 0.00 deg/s 
mx = 0 my = 0 mz = 0 mG 
q0 = 1.00 qx = 0.00 qy = 0.00 qz = 0.00 
Yaw, Pitch, Roll: 0.00, 0.00, 0.00 
rate = 778.82 Hz 
x y z 0 
0 
0 
mg 
0 
0 
0 
o/s 
0 
0 
0 
mG 
0 
0 
0 
ypr 

代碼:

#include <Wire.h> 
#include "I2Cdev.h" 
#include "MPU6050_9Axis_MotionApps41.h" 
//#include <Adafruit_GFX.h> 
//#include <Adafruit_PCD8544.h> 

// Using NOKIA 5110 monochrome 84 x 48 pixel display 
// pin 9 - Serial clock out (SCLK) 
// pin 8 - Serial data out (DIN) 
// pin 7 - Data/Command select (D/C) 
// pin 5 - LCD chip select (CS) 
// pin 6 - LCD reset (RST) 
//Adafruit_PCD8544 display = Adafruit_PCD8544(9, 8, 7, 5, 6); 

// Declare device MPU6050 class 
MPU6050 mpu; 

// global constants for 9 DoF fusion and AHRS (Attitude and Heading Reference System) 
#define GyroMeasError PI * (40.0f/180.0f)  // gyroscope measurement error in rads/s (shown as 3 deg/s) 
#define GyroMeasDrift PI * (0.0f/180.0f)  // gyroscope measurement drift in rad/s/s (shown as 0.0 deg/s/s) 
// There is a tradeoff in the beta parameter between accuracy and response speed. 
// In the original Madgwick study, beta of 0.041 (corresponding to GyroMeasError of 2.7 degrees/s) was found to give optimal accuracy. 
// However, with this value, the LSM9SD0 response time is about 10 seconds to a stable initial quaternion. 
// Subsequent changes also require a longish lag time to a stable output, not fast enough for a quadcopter or robot car! 
// By increasing beta (GyroMeasError) by about a factor of fifteen, the response time constant is reduced to ~2 sec 
// I haven't noticed any reduction in solution accuracy. This is essentially the I coefficient in a PID control sense; 
// the bigger the feedback coefficient, the faster the solution converges, usually at the expense of accuracy. 
// In any case, this is the free parameter in the Madgwick filtering and fusion scheme. 
#define beta sqrt(3.0f/4.0f) * GyroMeasError // compute beta 
#define zeta sqrt(3.0f/4.0f) * GyroMeasDrift // compute zeta, the other free parameter in the Madgwick scheme usually set to a small or zero value 
#define Kp 2.0f * 5.0f // these are the free parameters in the Mahony filter and fusion scheme, Kp for proportional feedback, Ki for integral 
#define Ki 0.0f 

int16_t a1, a2, a3, g1, g2, g3, m1, m2, m3;  // raw data arrays reading 
uint16_t count = 0; // used to control display output rate 
uint16_t delt_t = 0; // used to control display output rate 
uint16_t mcount = 0; // used to control display output rate 
uint8_t MagRate;  // read rate for magnetometer data 

float pitch, yaw, roll; 
float deltat = 0.0f;  // integration interval for both filter schemes 
uint16_t lastUpdate = 0; // used to calculate integration interval 
uint16_t now = 0;  // used to calculate integration interval 

float ax, ay, az, gx, gy, gz, mx, my, mz; // variables to hold latest sensor data values 
float q[4] = {1.0f, 0.0f, 0.0f, 0.0f}; // vector to hold quaternion 
float eInt[3] = {0.0f, 0.0f, 0.0f};  // vector to hold integral error for Mahony method 


void setup() 
{ 
    Serial.begin(38400); // Start serial at 38400 bps 

    //display.begin(); // Initialize the display 
    //display.setContrast(58); // Set the contrast 
    //display.setRotation(0); // 0 or 2) width = width, 1 or 3) width = height, swapped etc. 

// Start device display with ID of sensor 
    /* 
    display.clearDisplay(); 
    display.setTextSize(2); 
    display.setCursor(0,0); display.print("MPU9150"); 
    display.setTextSize(1); 
    display.setCursor(0, 20); display.print("9 DOF sensor"); 
    display.setCursor(0, 30); display.print("data fusion"); 
    display.setCursor(20, 40); display.print("AHRS"); 
    display.display(); 
    delay(2000); 

// Set up for data display 
    display.setTextSize(1); // Set text size to normal, 2 is twice normal etc. 
    display.setTextColor(BLACK); // Set pixel color; 1 on the monochrome screen 
    display.clearDisplay(); // clears the screen and buffer 
    display.display(); 
    */  

    // initialize MPU6050 device 
    Serial.println(F("Initializing I2C devices...")); 
    mpu.initialize(); 

    // verify connection 
    Serial.println(F("Testing device connections...")); 
    Serial.println(mpu.testConnection() ? F("MPU9150 connection successful") : F("MPU9150 connection failed")); 

// Set up the accelerometer, gyro, and magnetometer for data output 

    mpu.setRate(7); // set gyro rate to 8 kHz/(1 * rate) shows 1 kHz, accelerometer ODR is fixed at 1 KHz 

    MagRate = 10; // set magnetometer read rate in Hz; 10 to 100 (max) Hz are reasonable values 

// Digital low pass filter configuration. 
// It also determines the internal sampling rate used by the device as shown in the table below. 
// The accelerometer output rate is fixed at 1kHz. This means that for a Sample 
// Rate greater than 1kHz, the same accelerometer sample may be output to the 
// FIFO, DMP, and sensor registers more than once. 
/* 
*   | ACCELEROMETER |   GYROSCOPE 
* DLPF_CFG | Bandwidth | Delay | Bandwidth | Delay | Sample Rate 
* ---------+-----------+--------+-----------+--------+------------- 
* 0  | 260Hz  | 0ms | 256Hz  | 0.98ms | 8kHz 
* 1  | 184Hz  | 2.0ms | 188Hz  | 1.9ms | 1kHz 
* 2  | 94Hz  | 3.0ms | 98Hz  | 2.8ms | 1kHz 
* 3  | 44Hz  | 4.9ms | 42Hz  | 4.8ms | 1kHz 
* 4  | 21Hz  | 8.5ms | 20Hz  | 8.3ms | 1kHz 
* 5  | 10Hz  | 13.8ms | 10Hz  | 13.4ms | 1kHz 
* 6  | 5Hz  | 19.0ms | 5Hz  | 18.6ms | 1kHz 
*/ 
    mpu.setDLPFMode(4); // set bandwidth of both gyro and accelerometer to ~20 Hz 

// Full-scale range of the gyro sensors: 
// 0 = +/- 250 degrees/sec, 1 = +/- 500 degrees/sec, 2 = +/- 1000 degrees/sec, 3 = +/- 2000 degrees/sec 
    mpu.setFullScaleGyroRange(0); // set gyro range to 250 degrees/sec 

// Full-scale accelerometer range. 
// The full-scale range of the accelerometer: 0 = +/- 2g, 1 = +/- 4g, 2 = +/- 8g, 3 = +/- 16g 
    mpu.setFullScaleAccelRange(0); // set accelerometer to 2 g range 

    mpu.setIntDataReadyEnabled(true); // enable data ready interrupt 
} 

void loop() 
{ 
     if(mpu.getIntDataReadyStatus() == 1) { // wait for data ready status register to update all data registers 
      mcount++; 
      // read the raw sensor data 
      mpu.getAcceleration (&a1, &a2, &a3 ); 
      ax = a1*2.0f/32768.0f; // 2 g full range for accelerometer 
      ay = a2*2.0f/32768.0f; 
      az = a3*2.0f/32768.0f; 

      mpu.getRotation (&g1, &g2, &g3 ); 
      gx = g1*250.0f/32768.0f; // 250 deg/s full range for gyroscope 
      gy = g2*250.0f/32768.0f; 
      gz = g3*250.0f/32768.0f; 
// The gyros and accelerometers can in principle be calibrated in addition to any factory calibration but they are generally 
// pretty accurate. You can check the accelerometer by making sure the reading is +1 g in the positive direction for each axis. 
// The gyro should read zero for each axis when the sensor is at rest. Small or zero adjustment should be needed for these sensors. 
// The magnetometer is a different thing. Most magnetometers will be sensitive to circuit currents, computers, and 
// other both man-made and natural sources of magnetic field. The rough way to calibrate the magnetometer is to record 
// the maximum and minimum readings (generally achieved at the North magnetic direction). The average of the sum divided by two 
// should provide a pretty good calibration offset. Don't forget that for the MPU9150, the magnetometer x- and y-axes are switched 
// compared to the gyro and accelerometer! 
      if (mcount > 1000/MagRate) { // this is a poor man's way of setting the magnetometer read rate (see below) 
      mpu.getMag (&m1, &m2, &m3); 
      mx = m1*10.0f*1229.0f/4096.0f + 18.0f; // milliGauss (1229 microTesla per 2^12 bits, 10 mG per microTesla) 
      my = m2*10.0f*1229.0f/4096.0f + 70.0f; // apply calibration offsets in mG that correspond to your environment and magnetometer 
      mz = m3*10.0f*1229.0f/4096.0f + 270.0f; 
      mcount = 0; 
      }   
     } 

    now = micros(); 
    deltat = ((now - lastUpdate)/1000000.0f); // set integration time by time elapsed since last filter update 
    lastUpdate = now; 
    // Sensors x (y)-axis of the accelerometer is aligned with the y (x)-axis of the magnetometer; 
    // the magnetometer z-axis (+ down) is opposite to z-axis (+ up) of accelerometer and gyro! 
    // We have to make some allowance for this orientationmismatch in feeding the output to the quaternion filter. 
    // For the MPU-9150, we have chosen a magnetic rotation that keeps the sensor forward along the x-axis just like 
    // in the LSM9DS0 sensor. This rotation can be modified to allow any convenient orientation convention. 
    // This is ok by aircraft orientation standards! 
    // Pass gyro rate as rad/s 
    MadgwickQuaternionUpdate(ax, ay, az, gx*PI/180.0f, gy*PI/180.0f, gz*PI/180.0f, my, mx, mz); 
// MahonyQuaternionUpdate(ax, ay, az, gx*PI/180.0f, gy*PI/180.0f, gz*PI/180.0f, my, mx, mz); 

    // Serial print and/or display at 0.5 s rate independent of data rates 
    delt_t = millis() - count; 
    if (delt_t > 500) { // update LCD once per half-second independent of read rate 

    Serial.print("ax = "); Serial.print((int)1000*ax); 
    Serial.print(" ay = "); Serial.print((int)1000*ay); 
    Serial.print(" az = "); Serial.print((int)1000*az); Serial.println(" mg"); 
    Serial.print("gx = "); Serial.print(gx, 2); 
    Serial.print(" gy = "); Serial.print(gy, 2); 
    Serial.print(" gz = "); Serial.print(gz, 2); Serial.println(" deg/s"); 
    Serial.print("mx = "); Serial.print((int)mx); 
    Serial.print(" my = "); Serial.print((int)my); 
    Serial.print(" mz = "); Serial.print((int)mz); Serial.println(" mG"); 

    Serial.print("q0 = "); Serial.print(q[0]); 
    Serial.print(" qx = "); Serial.print(q[1]); 
    Serial.print(" qy = "); Serial.print(q[2]); 
    Serial.print(" qz = "); Serial.println(q[3]); 


    // Define output variables from updated quaternion---these are Tait-Bryan angles, commonly used in aircraft orientation. 
    // In this coordinate system, the positive z-axis is down toward Earth. 
    // Yaw is the angle between Sensor x-axis and Earth magnetic North (or true North if corrected for local declination, looking down on the sensor positive yaw is counterclockwise. 
    // Pitch is angle between sensor x-axis and Earth ground plane, toward the Earth is positive, up toward the sky is negative. 
    // Roll is angle between sensor y-axis and Earth ground plane, y-axis up is positive roll. 
    // These arise from the definition of the homogeneous rotation matrix constructed from quaternions. 
    // Tait-Bryan angles as well as Euler angles are non-commutative; that is, the get the correct orientation the rotations must be 
    // applied in the correct order which for this configuration is yaw, pitch, and then roll. 
    // For more see http://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles which has additional links. 
    yaw = atan2(2.0f * (q[1] * q[2] + q[0] * q[3]), q[0] * q[0] + q[1] * q[1] - q[2] * q[2] - q[3] * q[3]); 
    pitch = -asin(2.0f * (q[1] * q[3] - q[0] * q[2])); 
    roll = atan2(2.0f * (q[0] * q[1] + q[2] * q[3]), q[0] * q[0] - q[1] * q[1] - q[2] * q[2] + q[3] * q[3]); 
    pitch *= 180.0f/PI; 
    yaw *= 180.0f/PI - 13.8; // Declination at Danville, California is 13 degrees 48 minutes and 47 seconds on 2014-04-04 
    roll *= 180.0f/PI; 

    Serial.print("Yaw, Pitch, Roll: "); 
    Serial.print(yaw, 2); 
    Serial.print(", "); 
    Serial.print(pitch, 2); 
    Serial.print(", "); 
    Serial.println(roll, 2); 

    Serial.print("rate = "); Serial.print((float)1.0f/deltat, 2); Serial.println(" Hz"); 

    /* 
    display.clearDisplay(); 


    display.setCursor(0, 0); display.print(" x y z "); 

    display.setCursor(0, 8); display.print((int)(1000*ax)); 
    display.setCursor(24, 8); display.print((int)(1000*ay)); 
    display.setCursor(48, 8); display.print((int)(1000*az)); 
    display.setCursor(72, 8); display.print("mg"); 

    display.setCursor(0, 16); display.print((int)(gx)); 
    display.setCursor(24, 16); display.print((int)(gy)); 
    display.setCursor(48, 16); display.print((int)(gz)); 
    display.setCursor(66, 16); display.print("o/s");  

    display.setCursor(0, 24); display.print((int)(mx)); 
    display.setCursor(24, 24); display.print((int)(my)); 
    display.setCursor(48, 24); display.print((int)(mz)); 
    display.setCursor(72, 24); display.print("mG");  

    display.setCursor(0, 32); display.print((int)(yaw)); 
    display.setCursor(24, 32); display.print((int)(pitch)); 
    display.setCursor(48, 32); display.print((int)(roll)); 
    display.setCursor(66, 32); display.print("ypr"); 

    // With these settings the filter is updating at a ~145 Hz rate using the Madgwick scheme and 
    // >200 Hz using the Mahony scheme even though the display refreshes at only 2 Hz. 
    // The filter update rate is determined mostly by the mathematical steps in the respective algorithms, 
    // the processor speed (8 MHz for the 3.3V Pro Mini), and the magnetometer ODR: 
    // an ODR of 10 Hz for the magnetometer produce the above rates, maximum magnetometer ODR of 100 Hz produces 
    // filter update rates of 36 - 145 and ~38 Hz for the Madgwick and Mahony schemes, respectively. 
    // This is presumably because the magnetometer read takes longer than the gyro or accelerometer reads. 
    // This filter update rate should be fast enough to maintain accurate platform orientation for 
    // stabilization control of a fast-moving robot or quadcopter. Compare to the update rate of 200 Hz 
    // produced by the on-board Digital Motion Processor of Invensense's MPU6050 6 DoF and MPU9150 9DoF sensors. 
    // The 3.3 V 8 MHz Pro Mini is doing pretty well! 
    display.setCursor(0, 40); display.print("rt: "); display.print((1/deltat)); display.print(" Hz"); 

    display.display(); 
    count = millis(); 
    */ 
    Serial.print(" x y z "); 

    Serial.println((int)(1000*ax)); 
    Serial.println((int)(1000*ay)); 
    Serial.println((int)(1000*az)); 
    Serial.println("mg"); 
    Serial.println((int)(gx)); 
    Serial.println((int)(gy)); 
    Serial.println((int)(gz)); 
    Serial.println("o/s");  

    Serial.println((int)(mx)); 
    Serial.println((int)(my)); 
    Serial.println((int)(mz)); 
    Serial.println("mG");  

    Serial.println((int)(yaw)); 
    Serial.println((int)(pitch)); 
    Serial.println((int)(roll)); 
    Serial.println("ypr"); 
    } 
} 


// Implementation of Sebastian Madgwick's "...efficient orientation filter for... inertial/magnetic sensor arrays" 
// (see http://www.x-io.co.uk/category/open-source/ for examples and more details) 
// which fuses acceleration, rotation rate, and magnetic moments to produce a quaternion-based estimate of absolute 
// device orientation -- which can be converted to yaw, pitch, and roll. Useful for stabilizing quadcopters, etc. 
// The performance of the orientation filter is at least as good as conventional Kalman-based filtering algorithms 
// but is much less computationally intensive---it can be performed on a 3.3 V Pro Mini operating at 8 MHz! 
     void MadgwickQuaternionUpdate(float ax, float ay, float az, float gx, float gy, float gz, float mx, float my, float mz) 
     { 
      float q1 = q[0], q2 = q[1], q3 = q[2], q4 = q[3]; // short name local variable for readability 
      float norm; 
      float hx, hy, _2bx, _2bz; 
      float s1, s2, s3, s4; 
      float qDot1, qDot2, qDot3, qDot4; 

      // Auxiliary variables to avoid repeated arithmetic 
      float _2q1mx; 
      float _2q1my; 
      float _2q1mz; 
      float _2q2mx; 
      float _4bx; 
      float _4bz; 
      float _2q1 = 2.0f * q1; 
      float _2q2 = 2.0f * q2; 
      float _2q3 = 2.0f * q3; 
      float _2q4 = 2.0f * q4; 
      float _2q1q3 = 2.0f * q1 * q3; 
      float _2q3q4 = 2.0f * q3 * q4; 
      float q1q1 = q1 * q1; 
      float q1q2 = q1 * q2; 
      float q1q3 = q1 * q3; 
      float q1q4 = q1 * q4; 
      float q2q2 = q2 * q2; 
      float q2q3 = q2 * q3; 
      float q2q4 = q2 * q4; 
      float q3q3 = q3 * q3; 
      float q3q4 = q3 * q4; 
      float q4q4 = q4 * q4; 

      // Normalise accelerometer measurement 
      norm = sqrt(ax * ax + ay * ay + az * az); 
      if (norm == 0.0f) return; // handle NaN 
      norm = 1.0f/norm; 
      ax *= norm; 
      ay *= norm; 
      az *= norm; 

      // Normalise magnetometer measurement 
      norm = sqrt(mx * mx + my * my + mz * mz); 
      if (norm == 0.0f) return; // handle NaN 
      norm = 1.0f/norm; 
      mx *= norm; 
      my *= norm; 
      mz *= norm; 

      // Reference direction of Earth's magnetic field 
      _2q1mx = 2.0f * q1 * mx; 
      _2q1my = 2.0f * q1 * my; 
      _2q1mz = 2.0f * q1 * mz; 
      _2q2mx = 2.0f * q2 * mx; 
      hx = mx * q1q1 - _2q1my * q4 + _2q1mz * q3 + mx * q2q2 + _2q2 * my * q3 + _2q2 * mz * q4 - mx * q3q3 - mx * q4q4; 
      hy = _2q1mx * q4 + my * q1q1 - _2q1mz * q2 + _2q2mx * q3 - my * q2q2 + my * q3q3 + _2q3 * mz * q4 - my * q4q4; 
      _2bx = sqrt(hx * hx + hy * hy); 
      _2bz = -_2q1mx * q3 + _2q1my * q2 + mz * q1q1 + _2q2mx * q4 - mz * q2q2 + _2q3 * my * q4 - mz * q3q3 + mz * q4q4; 
      _4bx = 2.0f * _2bx; 
      _4bz = 2.0f * _2bz; 

      // Gradient decent algorithm corrective step 
      s1 = -_2q3 * (2.0f * q2q4 - _2q1q3 - ax) + _2q2 * (2.0f * q1q2 + _2q3q4 - ay) - _2bz * q3 * (_2bx * (0.5f - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + (-_2bx * q4 + _2bz * q2) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + _2bx * q3 * (_2bx * (q1q3 + q2q4) + _2bz * (0.5f - q2q2 - q3q3) - mz); 
      s2 = _2q4 * (2.0f * q2q4 - _2q1q3 - ax) + _2q1 * (2.0f * q1q2 + _2q3q4 - ay) - 4.0f * q2 * (1.0f - 2.0f * q2q2 - 2.0f * q3q3 - az) + _2bz * q4 * (_2bx * (0.5f - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + (_2bx * q3 + _2bz * q1) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + (_2bx * q4 - _4bz * q2) * (_2bx * (q1q3 + q2q4) + _2bz * (0.5f - q2q2 - q3q3) - mz); 
      s3 = -_2q1 * (2.0f * q2q4 - _2q1q3 - ax) + _2q4 * (2.0f * q1q2 + _2q3q4 - ay) - 4.0f * q3 * (1.0f - 2.0f * q2q2 - 2.0f * q3q3 - az) + (-_4bx * q3 - _2bz * q1) * (_2bx * (0.5f - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + (_2bx * q2 + _2bz * q4) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + (_2bx * q1 - _4bz * q3) * (_2bx * (q1q3 + q2q4) + _2bz * (0.5f - q2q2 - q3q3) - mz); 
      s4 = _2q2 * (2.0f * q2q4 - _2q1q3 - ax) + _2q3 * (2.0f * q1q2 + _2q3q4 - ay) + (-_4bx * q4 + _2bz * q2) * (_2bx * (0.5f - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + (-_2bx * q1 + _2bz * q3) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + _2bx * q2 * (_2bx * (q1q3 + q2q4) + _2bz * (0.5f - q2q2 - q3q3) - mz); 
      norm = sqrt(s1 * s1 + s2 * s2 + s3 * s3 + s4 * s4); // normalise step magnitude 
      norm = 1.0f/norm; 
      s1 *= norm; 
      s2 *= norm; 
      s3 *= norm; 
      s4 *= norm; 

      // Compute rate of change of quaternion 
      qDot1 = 0.5f * (-q2 * gx - q3 * gy - q4 * gz) - beta * s1; 
      qDot2 = 0.5f * (q1 * gx + q3 * gz - q4 * gy) - beta * s2; 
      qDot3 = 0.5f * (q1 * gy - q2 * gz + q4 * gx) - beta * s3; 
      qDot4 = 0.5f * (q1 * gz + q2 * gy - q3 * gx) - beta * s4; 

      // Integrate to yield quaternion 
      q1 += qDot1 * deltat; 
      q2 += qDot2 * deltat; 
      q3 += qDot3 * deltat; 
      q4 += qDot4 * deltat; 
      norm = sqrt(q1 * q1 + q2 * q2 + q3 * q3 + q4 * q4); // normalise quaternion 
      norm = 1.0f/norm; 
      q[0] = q1 * norm; 
      q[1] = q2 * norm; 
      q[2] = q3 * norm; 
      q[3] = q4 * norm; 

     } 



// Similar to Madgwick scheme but uses proportional and integral filtering on the error between estimated reference vectors and 
// measured ones. 
      void MahonyQuaternionUpdate(float ax, float ay, float az, float gx, float gy, float gz, float mx, float my, float mz) 
     { 
      float q1 = q[0], q2 = q[1], q3 = q[2], q4 = q[3]; // short name local variable for readability 
      float norm; 
      float hx, hy, bx, bz; 
      float vx, vy, vz, wx, wy, wz; 
      float ex, ey, ez; 
      float pa, pb, pc; 

      // Auxiliary variables to avoid repeated arithmetic 
      float q1q1 = q1 * q1; 
      float q1q2 = q1 * q2; 
      float q1q3 = q1 * q3; 
      float q1q4 = q1 * q4; 
      float q2q2 = q2 * q2; 
      float q2q3 = q2 * q3; 
      float q2q4 = q2 * q4; 
      float q3q3 = q3 * q3; 
      float q3q4 = q3 * q4; 
      float q4q4 = q4 * q4; 

      // Normalise accelerometer measurement 
      norm = sqrt(ax * ax + ay * ay + az * az); 
      if (norm == 0.0f) return; // handle NaN 
      norm = 1.0f/norm;  // use reciprocal for division 
      ax *= norm; 
      ay *= norm; 
      az *= norm; 

      // Normalise magnetometer measurement 
      norm = sqrt(mx * mx + my * my + mz * mz); 
      if (norm == 0.0f) return; // handle NaN 
      norm = 1.0f/norm;  // use reciprocal for division 
      mx *= norm; 
      my *= norm; 
      mz *= norm; 

      // Reference direction of Earth's magnetic field 
      hx = 2.0f * mx * (0.5f - q3q3 - q4q4) + 2.0f * my * (q2q3 - q1q4) + 2.0f * mz * (q2q4 + q1q3); 
      hy = 2.0f * mx * (q2q3 + q1q4) + 2.0f * my * (0.5f - q2q2 - q4q4) + 2.0f * mz * (q3q4 - q1q2); 
      bx = sqrt((hx * hx) + (hy * hy)); 
      bz = 2.0f * mx * (q2q4 - q1q3) + 2.0f * my * (q3q4 + q1q2) + 2.0f * mz * (0.5f - q2q2 - q3q3); 

      // Estimated direction of gravity and magnetic field 
      vx = 2.0f * (q2q4 - q1q3); 
      vy = 2.0f * (q1q2 + q3q4); 
      vz = q1q1 - q2q2 - q3q3 + q4q4; 
      wx = 2.0f * bx * (0.5f - q3q3 - q4q4) + 2.0f * bz * (q2q4 - q1q3); 
      wy = 2.0f * bx * (q2q3 - q1q4) + 2.0f * bz * (q1q2 + q3q4); 
      wz = 2.0f * bx * (q1q3 + q2q4) + 2.0f * bz * (0.5f - q2q2 - q3q3); 

      // Error is cross product between estimated direction and measured direction of gravity 
      ex = (ay * vz - az * vy) + (my * wz - mz * wy); 
      ey = (az * vx - ax * vz) + (mz * wx - mx * wz); 
      ez = (ax * vy - ay * vx) + (mx * wy - my * wx); 
      if (Ki > 0.0f) 
      { 
       eInt[0] += ex;  // accumulate integral error 
       eInt[1] += ey; 
       eInt[2] += ez; 
      } 
      else 
      { 
       eInt[0] = 0.0f;  // prevent integral wind up 
       eInt[1] = 0.0f; 
       eInt[2] = 0.0f; 
      } 

      // Apply feedback terms 
      gx = gx + Kp * ex + Ki * eInt[0]; 
      gy = gy + Kp * ey + Ki * eInt[1]; 
      gz = gz + Kp * ez + Ki * eInt[2]; 

      // Integrate rate of change of quaternion 
      pa = q2; 
      pb = q3; 
      pc = q4; 
      q1 = q1 + (-q2 * gx - q3 * gy - q4 * gz) * (0.5f * deltat); 
      q2 = pa + (q1 * gx + pb * gz - pc * gy) * (0.5f * deltat); 
      q3 = pb + (q1 * gy - pa * gz + pc * gx) * (0.5f * deltat); 
      q4 = pc + (q1 * gz + pa * gy - pb * gx) * (0.5f * deltat); 

      // Normalise quaternion 
      norm = sqrt(q1 * q1 + q2 * q2 + q3 * q3 + q4 * q4); 
      norm = 1.0f/norm; 
      q[0] = q1 * norm; 
      q[1] = q2 * norm; 
      q[2] = q3 * norm; 
      q[3] = q4 * norm; 

     } 
+0

您是否通過FIFO訪問MPU? – 2014-12-01 06:57:46

+0

@ LM.Croisez什麼是FIFO?我所知道的是我使用I2C來連接和傳輸數據。 – SuperAdmin 2014-12-01 16:21:19

+0

所有現代加速度計都提供一個FIFO(先進先出類型的緩衝器,它允許加速度計將採樣放入其中,並在必須讀取時通過中斷警告外部處理器) 讀取操作導致加速度計再次填充,等等。 這是一個非常有用的功能(必須),因爲當FIFO由加速度計填充時,處理器可以做很多其他的事情,例如,計算以前的FIFO數據,或者其他什麼 – 2014-12-02 08:41:57

回答