shubhampaul / real_time_planet_tracking_system Goto Github PK

C:\Users\Jonathan\Documents\Arduino\libraries\Real_Time_Planet_Tracking_System-master\MPU9250BasicAHRS.ino\MPU9250BasicAHRS.ino.ino: In function 'void loop()':

MPU9250BasicAHRS.ino:399: error: 'MahonyQuaternionUpdate' was not declared in this scope

MahonyQuaternionUpdate(ax, ay, az, gxPI/180.0f, gyPI/180.0f, gz*PI/180.0f, my, mx, mz);

                                                                                      ^

exit status 1
'MahonyQuaternionUpdate' was not declared in this scope

Hi Paul,

First of all thanks for you great article on hackster.io and the work done on the mpu9250 lib.

I have remarks and questions about this code:
in https://github.com/shubhampaul/Real_Time_Planet_Tracking_System/blob/master/MPU_fux_BNO_mBias/MPU_fux_BNO_mBias.ino line 244

`// global constants for 9 DoF fusion and AHRS (Attitude and Heading Reference System)
float GyroMeasError = PI * (40.0f / 180.0f); // gyroscope measurement error in rads/s (start at 40 deg/s)

float GyroMeasDrift = PI * (0.0f / 180.0f); // gyroscope measurement drift in rad/s/s (start at 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.

float beta = sqrt(3.0f / 4.0f) * GyroMeasError; // compute beta

float 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`

float GyroMeasDrift = PI * (0.0f / 180.0f); means that this constant is 0 => 0/180 = 0 * Pi = 0. It means that the Gyro drift is considered to be 0 ?
This also means that float zeta = sqrt(3.0f / 4.0f) * GyroMeasDrift; the zeta parameter is 0 ?

I am not an IMU or Kalman filter specialist but I am confused with that as I understand that a good kalman filter parameter evaluation is quite crucial to getting reliable results.

Could you shed a light on why these 2 parameters would tend towards zero and thus not taking into accoun the Gyro drift over time if I am correct?

Also I have another question about the Accelero and Gyro bias vectors:

float gyroBias[3] = {0, 0, 0}, accelBias[3] = {0, 0, 0}; // Bias corrections for gyro and accelerometer

Shouldn't the bias be calculated during i.e. the selfTest or during accelero and gyro calibration ?
Regards

Regards

Romain

9-DOF 16-bit
motion sensor
60 ug LSB
MPU9250 I AM FF I should be 71
MPU9250
I AM
FF
I Should Be
71
Could not connect to MPU9250: 0xFF