kriswiner
commented
4 years ago The Madgwick and Mahony filters are not Kalman filters per se, and these parameters are not being used in the usual Madgwick filter anyway, so it doesn;t matter if they are zero.
The accle and gyro should be calibrated in the setup.
On Wed, Feb 12, 2020 at 6:54 PM romatou18 notifications@github.com wrote:
Hi Kris,
I have remarks and questions about the code that initialises the constants of the Kalman filter used to fusion the accelero and the gyro readings :
// 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 account 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
Romain
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romatou18
Hi Kris,
I have remarks and questions about the code that initialises the constants of the Kalman filter used to fusion the accelero and the gyro readings :
` // 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);
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 account 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 accelerometerShouldn't the bias be calculated during i.e. the selfTest or during accelero and gyro calibration ? Regards
Romain