ardupilot/libraries/AP_NavEKF2/AP_NavEKF2_OptFlowFusion.cpp

721 lines
34 KiB
C++

#include <AP_HAL/AP_HAL.h>
#include "AP_NavEKF2.h"
#include "AP_NavEKF2_core.h"
extern const AP_HAL::HAL& hal;
/********************************************************
* RESET FUNCTIONS *
********************************************************/
/********************************************************
* FUSE MEASURED_DATA *
********************************************************/
// select fusion of optical flow measurements
void NavEKF2_core::SelectFlowFusion()
{
// Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
// If so, don't fuse measurements on this time step to reduce frame over-runs
// Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
if (magFusePerformed && dtIMUavg < 0.005f && !optFlowFusionDelayed) {
optFlowFusionDelayed = true;
return;
} else {
optFlowFusionDelayed = false;
}
// Perform Data Checks
// Check if the optical flow data is still valid
flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000);
// check is the terrain offset estimate is still valid - if we are using range finder as the main height reference, the ground is assumed to be at 0
gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000) || (activeHgtSource == HGT_SOURCE_RNG);
// Perform tilt check
bool tiltOK = (prevTnb.c.z > frontend->DCM33FlowMin);
// Constrain measurements to zero if takeoff is not detected and the height above ground
// is insufficient to achieve acceptable focus. This allows the vehicle to be picked up
// and carried to test optical flow operation
if (!takeOffDetected && ((terrainState - stateStruct.position.z) < 0.5f)) {
ofDataDelayed.flowRadXYcomp.zero();
ofDataDelayed.flowRadXY.zero();
flowDataValid = true;
}
// if have valid flow or range measurements, fuse data into a 1-state EKF to estimate terrain height
if (((flowDataToFuse && (frontend->_flowUse == FLOW_USE_TERRAIN)) || rangeDataToFuse) && tiltOK) {
// Estimate the terrain offset (runs a one state EKF)
EstimateTerrainOffset();
}
// Fuse optical flow data into the main filter
if (flowDataToFuse && tiltOK) {
if (frontend->_flowUse == FLOW_USE_NAV) {
// Set the flow noise used by the fusion processes
R_LOS = sq(MAX(frontend->_flowNoise, 0.05f));
// Fuse the optical flow X and Y axis data into the main filter sequentially
FuseOptFlow();
}
// reset flag to indicate that no new flow data is available for fusion
flowDataToFuse = false;
}
}
/*
Estimation of terrain offset using a single state EKF
The filter can fuse motion compensated optical flow rates and range finder measurements
Equations generated using https://github.com/PX4/ecl/tree/master/EKF/matlab/scripts/Terrain%20Estimator
*/
void NavEKF2_core::EstimateTerrainOffset()
{
// horizontal velocity squared
ftype velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y);
// don't fuse flow data if LOS rate is misaligned, without GPS, or insufficient velocity, as it is poorly observable
// don't fuse flow data if it exceeds validity limits
// don't update terrain offset if ground is being used as the zero height datum in the main filter
bool cantFuseFlowData = ((frontend->_flowUse != FLOW_USE_TERRAIN)
|| gpsNotAvailable
|| PV_AidingMode == AID_RELATIVE
|| velHorizSq < 25.0f
|| (MAX(ofDataDelayed.flowRadXY[0],ofDataDelayed.flowRadXY[1]) > frontend->_maxFlowRate));
if ((!rangeDataToFuse && cantFuseFlowData) || (activeHgtSource == HGT_SOURCE_RNG)) {
// skip update
inhibitGndState = true;
} else {
inhibitGndState = false;
// record the time we last updated the terrain offset state
gndHgtValidTime_ms = imuSampleTime_ms;
// propagate ground position state noise each time this is called using the difference in position since the last observations and an RMS gradient assumption
// limit distance to prevent initialisation after bad gps causing bad numerical conditioning
ftype distanceTravelledSq = sq(stateStruct.position[0] - prevPosN) + sq(stateStruct.position[1] - prevPosE);
distanceTravelledSq = MIN(distanceTravelledSq, 100.0f);
prevPosN = stateStruct.position[0];
prevPosE = stateStruct.position[1];
// in addition to a terrain gradient error model, we also have the growth in uncertainty due to the copters vertical velocity
ftype timeLapsed = MIN(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f);
ftype Pincrement = (distanceTravelledSq * sq(frontend->_terrGradMax)) + sq(timeLapsed)*P[5][5];
Popt += Pincrement;
timeAtLastAuxEKF_ms = imuSampleTime_ms;
// fuse range finder data
if (rangeDataToFuse) {
// predict range
ftype predRngMeas = MAX((terrainState - stateStruct.position[2]),rngOnGnd) / prevTnb.c.z;
// Copy required states to local variable names
ftype q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
ftype q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
ftype q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
ftype q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
// Set range finder measurement noise variance. TODO make this a function of range and tilt to allow for sensor, alignment and AHRS errors
ftype R_RNG = frontend->_rngNoise;
// calculate Kalman gain
ftype SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3);
ftype K_RNG = Popt/(SK_RNG*(R_RNG + Popt/sq(SK_RNG)));
// Calculate the innovation variance for data logging
varInnovRng = (R_RNG + Popt/sq(SK_RNG));
// constrain terrain height to be below the vehicle
terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
// Calculate the measurement innovation
innovRng = predRngMeas - rangeDataDelayed.rng;
// calculate the innovation consistency test ratio
auxRngTestRatio = sq(innovRng) / (sq(MAX(0.01f * (ftype)frontend->_rngInnovGate, 1.0f)) * varInnovRng);
// Check the innovation test ratio and don't fuse if too large
if (auxRngTestRatio < 1.0f) {
// correct the state
terrainState -= K_RNG * innovRng;
// constrain the state
terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
// correct the covariance
Popt = Popt - sq(Popt)/(SK_RNG*(R_RNG + Popt/sq(SK_RNG))*(sq(q0) - sq(q1) - sq(q2) + sq(q3)));
// prevent the state variance from becoming negative
Popt = MAX(Popt,0.0f);
}
}
if (!cantFuseFlowData) {
Vector3F relVelSensor; // velocity of sensor relative to ground in sensor axes
Vector2F losPred; // predicted optical flow angular rate measurement
ftype q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
ftype q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
ftype q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
ftype q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
ftype K_OPT;
ftype H_OPT;
Vector2F auxFlowObsInnovVar;
// predict range to centre of image
ftype flowRngPred = MAX((terrainState - stateStruct.position.z),rngOnGnd) / prevTnb.c.z;
// constrain terrain height to be below the vehicle
terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
// calculate relative velocity in sensor frame
relVelSensor = prevTnb*stateStruct.velocity;
// divide velocity by range, subtract body rates and apply scale factor to
// get predicted sensed angular optical rates relative to X and Y sensor axes
losPred.x = relVelSensor.y / flowRngPred;
losPred.y = - relVelSensor.x / flowRngPred;
// calculate innovations
auxFlowObsInnov = losPred - ofDataDelayed.flowRadXYcomp;
// calculate observation jacobians
ftype t2 = q0*q0;
ftype t3 = q1*q1;
ftype t4 = q2*q2;
ftype t5 = q3*q3;
ftype t6 = stateStruct.position.z - terrainState;
ftype t7 = 1.0f / (t6*t6);
ftype t8 = q0*q3*2.0f;
ftype t9 = t2-t3-t4+t5;
// prevent the state variances from becoming badly conditioned
Popt = MAX(Popt,1E-6f);
// calculate observation noise variance from parameter
ftype flow_noise_variance = sq(MAX(frontend->_flowNoise, 0.05f));
// Fuse Y axis data
// Calculate observation partial derivative
H_OPT = t7*t9*(-stateStruct.velocity.z*(q0*q2*2.0-q1*q3*2.0)+stateStruct.velocity.x*(t2+t3-t4-t5)+stateStruct.velocity.y*(t8+q1*q2*2.0));
// calculate innovation variance
auxFlowObsInnovVar.y = H_OPT * Popt * H_OPT + flow_noise_variance;
// calculate Kalman gain
K_OPT = Popt * H_OPT / auxFlowObsInnovVar.y;
// calculate the innovation consistency test ratio
auxFlowTestRatio.y = sq(auxFlowObsInnov.y) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.y);
// don't fuse if optical flow data is outside valid range
if (auxFlowTestRatio.y < 1.0f) {
// correct the state
terrainState -= K_OPT * auxFlowObsInnov.y;
// constrain the state
terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
// update intermediate variables used when fusing the X axis
t6 = stateStruct.position.z - terrainState;
t7 = 1.0f / (t6*t6);
// correct the covariance
Popt = Popt - K_OPT * H_OPT * Popt;
// prevent the state variances from becoming badly conditioned
Popt = MAX(Popt,1E-6f);
}
// fuse X axis data
H_OPT = -t7*t9*(stateStruct.velocity.z*(q0*q1*2.0+q2*q3*2.0)+stateStruct.velocity.y*(t2-t3+t4-t5)-stateStruct.velocity.x*(t8-q1*q2*2.0));
// calculate innovation variances
auxFlowObsInnovVar.x = H_OPT * Popt * H_OPT + flow_noise_variance;
// calculate Kalman gain
K_OPT = Popt * H_OPT / auxFlowObsInnovVar.x;
// calculate the innovation consistency test ratio
auxFlowTestRatio.x = sq(auxFlowObsInnov.x) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.x);
// don't fuse if optical flow data is outside valid range
if (auxFlowTestRatio.x < 1.0f) {
// correct the state
terrainState -= K_OPT * auxFlowObsInnov.x;
// constrain the state
terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
// correct the covariance
Popt = Popt - K_OPT * H_OPT * Popt;
// prevent the state variances from becoming badly conditioned
Popt = MAX(Popt,1E-6f);
}
}
}
}
/*
* Fuse angular motion compensated optical flow rates using explicit algebraic equations generated with Matlab symbolic toolbox.
* The script file used to generate these and other equations in this filter can be found here:
* https://github.com/priseborough/InertialNav/blob/master/derivations/RotationVectorAttitudeParameterisation/GenerateNavFilterEquations.m
* Requires a valid terrain height estimate.
*/
void NavEKF2_core::FuseOptFlow()
{
Vector24 H_LOS;
Vector3F relVelSensor;
Vector14 SH_LOS;
Vector2 losPred;
// Copy required states to local variable names
ftype q0 = stateStruct.quat[0];
ftype q1 = stateStruct.quat[1];
ftype q2 = stateStruct.quat[2];
ftype q3 = stateStruct.quat[3];
ftype vn = stateStruct.velocity.x;
ftype ve = stateStruct.velocity.y;
ftype vd = stateStruct.velocity.z;
ftype pd = stateStruct.position.z;
// constrain height above ground to be above range measured on ground
ftype heightAboveGndEst = MAX((terrainState - pd), rngOnGnd);
ftype ptd = pd + heightAboveGndEst;
// Calculate common expressions for observation jacobians
SH_LOS[0] = sq(q0) - sq(q1) - sq(q2) + sq(q3);
SH_LOS[1] = vn*(sq(q0) + sq(q1) - sq(q2) - sq(q3)) - vd*(2*q0*q2 - 2*q1*q3) + ve*(2*q0*q3 + 2*q1*q2);
SH_LOS[2] = ve*(sq(q0) - sq(q1) + sq(q2) - sq(q3)) + vd*(2*q0*q1 + 2*q2*q3) - vn*(2*q0*q3 - 2*q1*q2);
SH_LOS[3] = 1/(pd - ptd);
SH_LOS[4] = vd*SH_LOS[0] - ve*(2*q0*q1 - 2*q2*q3) + vn*(2*q0*q2 + 2*q1*q3);
SH_LOS[5] = 2.0f*q0*q2 - 2.0f*q1*q3;
SH_LOS[6] = 2.0f*q0*q1 + 2.0f*q2*q3;
SH_LOS[7] = q0*q0;
SH_LOS[8] = q1*q1;
SH_LOS[9] = q2*q2;
SH_LOS[10] = q3*q3;
SH_LOS[11] = q0*q3*2.0f;
SH_LOS[12] = pd-ptd;
SH_LOS[13] = 1.0f/(SH_LOS[12]*SH_LOS[12]);
// Fuse X and Y axis measurements sequentially assuming observation errors are uncorrelated
for (uint8_t obsIndex=0; obsIndex<=1; obsIndex++) { // fuse X axis data first
// calculate range from ground plane to centre of sensor fov assuming flat earth
ftype range = constrain_ftype((heightAboveGndEst/prevTnb.c.z),rngOnGnd,1000.0f);
// correct range for flow sensor offset body frame position offset
// the corrected value is the predicted range from the sensor focal point to the
// centre of the image on the ground assuming flat terrain
Vector3F posOffsetBody = ofDataDelayed.body_offset - accelPosOffset;
if (!posOffsetBody.is_zero()) {
Vector3F posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
range -= posOffsetEarth.z / prevTnb.c.z;
}
// calculate relative velocity in sensor frame including the relative motion due to rotation
relVelSensor = prevTnb*stateStruct.velocity + ofDataDelayed.bodyRadXYZ % posOffsetBody;
// divide velocity by range to get predicted angular LOS rates relative to X and Y axes
losPred[0] = relVelSensor.y/range;
losPred[1] = -relVelSensor.x/range;
// calculate observation jacobians and Kalman gains
memset(&H_LOS[0], 0, sizeof(H_LOS));
if (obsIndex == 0) {
H_LOS[0] = SH_LOS[3]*SH_LOS[2]*SH_LOS[6]-SH_LOS[3]*SH_LOS[0]*SH_LOS[4];
H_LOS[1] = SH_LOS[3]*SH_LOS[2]*SH_LOS[5];
H_LOS[2] = SH_LOS[3]*SH_LOS[0]*SH_LOS[1];
H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]-q1*q2*2.0f);
H_LOS[4] = -SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]-SH_LOS[8]+SH_LOS[9]-SH_LOS[10]);
H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[6];
H_LOS[8] = SH_LOS[2]*SH_LOS[0]*SH_LOS[13];
ftype t2 = SH_LOS[3];
ftype t3 = SH_LOS[0];
ftype t4 = SH_LOS[2];
ftype t5 = SH_LOS[6];
ftype t100 = t2 * t3 * t5;
ftype t6 = SH_LOS[4];
ftype t7 = t2*t3*t6;
ftype t9 = t2*t4*t5;
ftype t8 = t7-t9;
ftype t10 = q0*q3*2.0f;
ftype t21 = q1*q2*2.0f;
ftype t11 = t10-t21;
ftype t101 = t2 * t3 * t11;
ftype t12 = pd-ptd;
ftype t13 = 1.0f/(t12*t12);
ftype t104 = t3 * t4 * t13;
ftype t14 = SH_LOS[5];
ftype t102 = t2 * t4 * t14;
ftype t15 = SH_LOS[1];
ftype t103 = t2 * t3 * t15;
ftype t16 = q0*q0;
ftype t17 = q1*q1;
ftype t18 = q2*q2;
ftype t19 = q3*q3;
ftype t20 = t16-t17+t18-t19;
ftype t105 = t2 * t3 * t20;
ftype t22 = P[1][1]*t102;
ftype t23 = P[3][0]*t101;
ftype t24 = P[8][0]*t104;
ftype t25 = P[1][0]*t102;
ftype t26 = P[2][0]*t103;
ftype t63 = P[0][0]*t8;
ftype t64 = P[5][0]*t100;
ftype t65 = P[4][0]*t105;
ftype t27 = t23+t24+t25+t26-t63-t64-t65;
ftype t28 = P[3][3]*t101;
ftype t29 = P[8][3]*t104;
ftype t30 = P[1][3]*t102;
ftype t31 = P[2][3]*t103;
ftype t67 = P[0][3]*t8;
ftype t68 = P[5][3]*t100;
ftype t69 = P[4][3]*t105;
ftype t32 = t28+t29+t30+t31-t67-t68-t69;
ftype t33 = t101*t32;
ftype t34 = P[3][8]*t101;
ftype t35 = P[8][8]*t104;
ftype t36 = P[1][8]*t102;
ftype t37 = P[2][8]*t103;
ftype t70 = P[0][8]*t8;
ftype t71 = P[5][8]*t100;
ftype t72 = P[4][8]*t105;
ftype t38 = t34+t35+t36+t37-t70-t71-t72;
ftype t39 = t104*t38;
ftype t40 = P[3][1]*t101;
ftype t41 = P[8][1]*t104;
ftype t42 = P[2][1]*t103;
ftype t73 = P[0][1]*t8;
ftype t74 = P[5][1]*t100;
ftype t75 = P[4][1]*t105;
ftype t43 = t22+t40+t41+t42-t73-t74-t75;
ftype t44 = t102*t43;
ftype t45 = P[3][2]*t101;
ftype t46 = P[8][2]*t104;
ftype t47 = P[1][2]*t102;
ftype t48 = P[2][2]*t103;
ftype t76 = P[0][2]*t8;
ftype t77 = P[5][2]*t100;
ftype t78 = P[4][2]*t105;
ftype t49 = t45+t46+t47+t48-t76-t77-t78;
ftype t50 = t103*t49;
ftype t51 = P[3][5]*t101;
ftype t52 = P[8][5]*t104;
ftype t53 = P[1][5]*t102;
ftype t54 = P[2][5]*t103;
ftype t79 = P[0][5]*t8;
ftype t80 = P[5][5]*t100;
ftype t81 = P[4][5]*t105;
ftype t55 = t51+t52+t53+t54-t79-t80-t81;
ftype t56 = P[3][4]*t101;
ftype t57 = P[8][4]*t104;
ftype t58 = P[1][4]*t102;
ftype t59 = P[2][4]*t103;
ftype t83 = P[0][4]*t8;
ftype t84 = P[5][4]*t100;
ftype t85 = P[4][4]*t105;
ftype t60 = t56+t57+t58+t59-t83-t84-t85;
ftype t66 = t8*t27;
ftype t82 = t100*t55;
ftype t86 = t105*t60;
ftype t61 = R_LOS+t33+t39+t44+t50-t66-t82-t86;
ftype t62 = 1.0f/t61;
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
if (t61 > R_LOS) {
t62 = 1.0f/t61;
faultStatus.bad_yflow = false;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
faultStatus.bad_yflow = true;
return;
}
varInnovOptFlow[0] = t61;
// calculate innovation for X axis observation
innovOptFlow[0] = losPred[0] - ofDataDelayed.flowRadXYcomp.x;
// calculate Kalman gains for X-axis observation
Kfusion[0] = t62*(-P[0][0]*t8-P[0][5]*t100+P[0][3]*t101+P[0][1]*t102+P[0][2]*t103+P[0][8]*t104-P[0][4]*t105);
Kfusion[1] = t62*(t22-P[1][0]*t8-P[1][5]*t100+P[1][3]*t101+P[1][2]*t103+P[1][8]*t104-P[1][4]*t105);
Kfusion[2] = t62*(t48-P[2][0]*t8-P[2][5]*t100+P[2][3]*t101+P[2][1]*t102+P[2][8]*t104-P[2][4]*t105);
Kfusion[3] = t62*(t28-P[3][0]*t8-P[3][5]*t100+P[3][1]*t102+P[3][2]*t103+P[3][8]*t104-P[3][4]*t105);
Kfusion[4] = t62*(-t85-P[4][0]*t8-P[4][5]*t100+P[4][3]*t101+P[4][1]*t102+P[4][2]*t103+P[4][8]*t104);
Kfusion[5] = t62*(-t80-P[5][0]*t8+P[5][3]*t101+P[5][1]*t102+P[5][2]*t103+P[5][8]*t104-P[5][4]*t105);
Kfusion[6] = t62*(-P[6][0]*t8-P[6][5]*t100+P[6][3]*t101+P[6][1]*t102+P[6][2]*t103+P[6][8]*t104-P[6][4]*t105);
Kfusion[7] = t62*(-P[7][0]*t8-P[7][5]*t100+P[7][3]*t101+P[7][1]*t102+P[7][2]*t103+P[7][8]*t104-P[7][4]*t105);
Kfusion[8] = t62*(t35-P[8][0]*t8-P[8][5]*t100+P[8][3]*t101+P[8][1]*t102+P[8][2]*t103-P[8][4]*t105);
Kfusion[9] = t62*(-P[9][0]*t8-P[9][5]*t100+P[9][3]*t101+P[9][1]*t102+P[9][2]*t103+P[9][8]*t104-P[9][4]*t105);
Kfusion[10] = t62*(-P[10][0]*t8-P[10][5]*t100+P[10][3]*t101+P[10][1]*t102+P[10][2]*t103+P[10][8]*t104-P[10][4]*t105);
Kfusion[11] = t62*(-P[11][0]*t8-P[11][5]*t100+P[11][3]*t101+P[11][1]*t102+P[11][2]*t103+P[11][8]*t104-P[11][4]*t105);
Kfusion[12] = t62*(-P[12][0]*t8-P[12][5]*t100+P[12][3]*t101+P[12][1]*t102+P[12][2]*t103+P[12][8]*t104-P[12][4]*t105);
Kfusion[13] = t62*(-P[13][0]*t8-P[13][5]*t100+P[13][3]*t101+P[13][1]*t102+P[13][2]*t103+P[13][8]*t104-P[13][4]*t105);
Kfusion[14] = t62*(-P[14][0]*t8-P[14][5]*t100+P[14][3]*t101+P[14][1]*t102+P[14][2]*t103+P[14][8]*t104-P[14][4]*t105);
Kfusion[15] = t62*(-P[15][0]*t8-P[15][5]*t100+P[15][3]*t101+P[15][1]*t102+P[15][2]*t103+P[15][8]*t104-P[15][4]*t105);
if (!inhibitWindStates) {
Kfusion[22] = t62*(-P[22][0]*t8-P[22][5]*t100+P[22][3]*t101+P[22][1]*t102+P[22][2]*t103+P[22][8]*t104-P[22][4]*t105);
Kfusion[23] = t62*(-P[23][0]*t8-P[23][5]*t100+P[23][3]*t101+P[23][1]*t102+P[23][2]*t103+P[23][8]*t104-P[23][4]*t105);
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
if (!inhibitMagStates) {
Kfusion[16] = t62*(-P[16][0]*t8-P[16][5]*t100+P[16][3]*t101+P[16][1]*t102+P[16][2]*t103+P[16][8]*t104-P[16][4]*t105);
Kfusion[17] = t62*(-P[17][0]*t8-P[17][5]*t100+P[17][3]*t101+P[17][1]*t102+P[17][2]*t103+P[17][8]*t104-P[17][4]*t105);
Kfusion[18] = t62*(-P[18][0]*t8-P[18][5]*t100+P[18][3]*t101+P[18][1]*t102+P[18][2]*t103+P[18][8]*t104-P[18][4]*t105);
Kfusion[19] = t62*(-P[19][0]*t8-P[19][5]*t100+P[19][3]*t101+P[19][1]*t102+P[19][2]*t103+P[19][8]*t104-P[19][4]*t105);
Kfusion[20] = t62*(-P[20][0]*t8-P[20][5]*t100+P[20][3]*t101+P[20][1]*t102+P[20][2]*t103+P[20][8]*t104-P[20][4]*t105);
Kfusion[21] = t62*(-P[21][0]*t8-P[21][5]*t100+P[21][3]*t101+P[21][1]*t102+P[21][2]*t103+P[21][8]*t104-P[21][4]*t105);
} else {
for (uint8_t i = 16; i <= 21; i++) {
Kfusion[i] = 0.0f;
}
}
} else {
H_LOS[0] = -SH_LOS[3]*SH_LOS[6]*SH_LOS[1];
H_LOS[1] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[4]-SH_LOS[3]*SH_LOS[1]*SH_LOS[5];
H_LOS[2] = SH_LOS[3]*SH_LOS[2]*SH_LOS[0];
H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]+SH_LOS[8]-SH_LOS[9]-SH_LOS[10]);
H_LOS[4] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]+q1*q2*2.0f);
H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[5];
H_LOS[8] = -SH_LOS[0]*SH_LOS[1]*SH_LOS[13];
ftype t2 = SH_LOS[3];
ftype t3 = SH_LOS[0];
ftype t4 = SH_LOS[1];
ftype t5 = SH_LOS[5];
ftype t100 = t2 * t3 * t5;
ftype t6 = SH_LOS[4];
ftype t7 = t2*t3*t6;
ftype t8 = t2*t4*t5;
ftype t9 = t7+t8;
ftype t10 = q0*q3*2.0f;
ftype t11 = q1*q2*2.0f;
ftype t12 = t10+t11;
ftype t101 = t2 * t3 * t12;
ftype t13 = pd-ptd;
ftype t14 = 1.0f/(t13*t13);
ftype t104 = t3 * t4 * t14;
ftype t15 = SH_LOS[6];
ftype t105 = t2 * t4 * t15;
ftype t16 = SH_LOS[2];
ftype t102 = t2 * t3 * t16;
ftype t17 = q0*q0;
ftype t18 = q1*q1;
ftype t19 = q2*q2;
ftype t20 = q3*q3;
ftype t21 = t17+t18-t19-t20;
ftype t103 = t2 * t3 * t21;
ftype t22 = P[0][0]*t105;
ftype t23 = P[1][1]*t9;
ftype t24 = P[8][1]*t104;
ftype t25 = P[0][1]*t105;
ftype t26 = P[5][1]*t100;
ftype t64 = P[4][1]*t101;
ftype t65 = P[2][1]*t102;
ftype t66 = P[3][1]*t103;
ftype t27 = t23+t24+t25+t26-t64-t65-t66;
ftype t28 = t9*t27;
ftype t29 = P[1][4]*t9;
ftype t30 = P[8][4]*t104;
ftype t31 = P[0][4]*t105;
ftype t32 = P[5][4]*t100;
ftype t67 = P[4][4]*t101;
ftype t68 = P[2][4]*t102;
ftype t69 = P[3][4]*t103;
ftype t33 = t29+t30+t31+t32-t67-t68-t69;
ftype t34 = P[1][8]*t9;
ftype t35 = P[8][8]*t104;
ftype t36 = P[0][8]*t105;
ftype t37 = P[5][8]*t100;
ftype t71 = P[4][8]*t101;
ftype t72 = P[2][8]*t102;
ftype t73 = P[3][8]*t103;
ftype t38 = t34+t35+t36+t37-t71-t72-t73;
ftype t39 = t104*t38;
ftype t40 = P[1][0]*t9;
ftype t41 = P[8][0]*t104;
ftype t42 = P[5][0]*t100;
ftype t74 = P[4][0]*t101;
ftype t75 = P[2][0]*t102;
ftype t76 = P[3][0]*t103;
ftype t43 = t22+t40+t41+t42-t74-t75-t76;
ftype t44 = t105*t43;
ftype t45 = P[1][2]*t9;
ftype t46 = P[8][2]*t104;
ftype t47 = P[0][2]*t105;
ftype t48 = P[5][2]*t100;
ftype t63 = P[2][2]*t102;
ftype t77 = P[4][2]*t101;
ftype t78 = P[3][2]*t103;
ftype t49 = t45+t46+t47+t48-t63-t77-t78;
ftype t50 = P[1][5]*t9;
ftype t51 = P[8][5]*t104;
ftype t52 = P[0][5]*t105;
ftype t53 = P[5][5]*t100;
ftype t80 = P[4][5]*t101;
ftype t81 = P[2][5]*t102;
ftype t82 = P[3][5]*t103;
ftype t54 = t50+t51+t52+t53-t80-t81-t82;
ftype t55 = t100*t54;
ftype t56 = P[1][3]*t9;
ftype t57 = P[8][3]*t104;
ftype t58 = P[0][3]*t105;
ftype t59 = P[5][3]*t100;
ftype t83 = P[4][3]*t101;
ftype t84 = P[2][3]*t102;
ftype t85 = P[3][3]*t103;
ftype t60 = t56+t57+t58+t59-t83-t84-t85;
ftype t70 = t101*t33;
ftype t79 = t102*t49;
ftype t86 = t103*t60;
ftype t61 = R_LOS+t28+t39+t44+t55-t70-t79-t86;
ftype t62 = 1.0f/t61;
// calculate innovation variance for Y axis observation and protect against a badly conditioned calculation
if (t61 > R_LOS) {
t62 = 1.0f/t61;
faultStatus.bad_yflow = false;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
faultStatus.bad_yflow = true;
return;
}
varInnovOptFlow[1] = t61;
// calculate innovation for Y observation
innovOptFlow[1] = losPred[1] - ofDataDelayed.flowRadXYcomp.y;
// calculate Kalman gains for the Y-axis observation
Kfusion[0] = -t62*(t22+P[0][1]*t9+P[0][5]*t100-P[0][4]*t101-P[0][2]*t102-P[0][3]*t103+P[0][8]*t104);
Kfusion[1] = -t62*(t23+P[1][5]*t100+P[1][0]*t105-P[1][4]*t101-P[1][2]*t102-P[1][3]*t103+P[1][8]*t104);
Kfusion[2] = -t62*(-t63+P[2][1]*t9+P[2][5]*t100+P[2][0]*t105-P[2][4]*t101-P[2][3]*t103+P[2][8]*t104);
Kfusion[3] = -t62*(-t85+P[3][1]*t9+P[3][5]*t100+P[3][0]*t105-P[3][4]*t101-P[3][2]*t102+P[3][8]*t104);
Kfusion[4] = -t62*(-t67+P[4][1]*t9+P[4][5]*t100+P[4][0]*t105-P[4][2]*t102-P[4][3]*t103+P[4][8]*t104);
Kfusion[5] = -t62*(t53+P[5][1]*t9+P[5][0]*t105-P[5][4]*t101-P[5][2]*t102-P[5][3]*t103+P[5][8]*t104);
Kfusion[6] = -t62*(P[6][1]*t9+P[6][5]*t100+P[6][0]*t105-P[6][4]*t101-P[6][2]*t102-P[6][3]*t103+P[6][8]*t104);
Kfusion[7] = -t62*(P[7][1]*t9+P[7][5]*t100+P[7][0]*t105-P[7][4]*t101-P[7][2]*t102-P[7][3]*t103+P[7][8]*t104);
Kfusion[8] = -t62*(t35+P[8][1]*t9+P[8][5]*t100+P[8][0]*t105-P[8][4]*t101-P[8][2]*t102-P[8][3]*t103);
Kfusion[9] = -t62*(P[9][1]*t9+P[9][5]*t100+P[9][0]*t105-P[9][4]*t101-P[9][2]*t102-P[9][3]*t103+P[9][8]*t104);
Kfusion[10] = -t62*(P[10][1]*t9+P[10][5]*t100+P[10][0]*t105-P[10][4]*t101-P[10][2]*t102-P[10][3]*t103+P[10][8]*t104);
Kfusion[11] = -t62*(P[11][1]*t9+P[11][5]*t100+P[11][0]*t105-P[11][4]*t101-P[11][2]*t102-P[11][3]*t103+P[11][8]*t104);
Kfusion[12] = -t62*(P[12][1]*t9+P[12][5]*t100+P[12][0]*t105-P[12][4]*t101-P[12][2]*t102-P[12][3]*t103+P[12][8]*t104);
Kfusion[13] = -t62*(P[13][1]*t9+P[13][5]*t100+P[13][0]*t105-P[13][4]*t101-P[13][2]*t102-P[13][3]*t103+P[13][8]*t104);
Kfusion[14] = -t62*(P[14][1]*t9+P[14][5]*t100+P[14][0]*t105-P[14][4]*t101-P[14][2]*t102-P[14][3]*t103+P[14][8]*t104);
Kfusion[15] = -t62*(P[15][1]*t9+P[15][5]*t100+P[15][0]*t105-P[15][4]*t101-P[15][2]*t102-P[15][3]*t103+P[15][8]*t104);
if (!inhibitWindStates) {
Kfusion[22] = -t62*(P[22][1]*t9+P[22][5]*t100+P[22][0]*t105-P[22][4]*t101-P[22][2]*t102-P[22][3]*t103+P[22][8]*t104);
Kfusion[23] = -t62*(P[23][1]*t9+P[23][5]*t100+P[23][0]*t105-P[23][4]*t101-P[23][2]*t102-P[23][3]*t103+P[23][8]*t104);
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
if (!inhibitMagStates) {
Kfusion[16] = -t62*(P[16][1]*t9+P[16][5]*t100+P[16][0]*t105-P[16][4]*t101-P[16][2]*t102-P[16][3]*t103+P[16][8]*t104);
Kfusion[17] = -t62*(P[17][1]*t9+P[17][5]*t100+P[17][0]*t105-P[17][4]*t101-P[17][2]*t102-P[17][3]*t103+P[17][8]*t104);
Kfusion[18] = -t62*(P[18][1]*t9+P[18][5]*t100+P[18][0]*t105-P[18][4]*t101-P[18][2]*t102-P[18][3]*t103+P[18][8]*t104);
Kfusion[19] = -t62*(P[19][1]*t9+P[19][5]*t100+P[19][0]*t105-P[19][4]*t101-P[19][2]*t102-P[19][3]*t103+P[19][8]*t104);
Kfusion[20] = -t62*(P[20][1]*t9+P[20][5]*t100+P[20][0]*t105-P[20][4]*t101-P[20][2]*t102-P[20][3]*t103+P[20][8]*t104);
Kfusion[21] = -t62*(P[21][1]*t9+P[21][5]*t100+P[21][0]*t105-P[21][4]*t101-P[21][2]*t102-P[21][3]*t103+P[21][8]*t104);
} else {
for (uint8_t i = 16; i <= 21; i++) {
Kfusion[i] = 0.0f;
}
}
}
// calculate the innovation consistency test ratio
flowTestRatio[obsIndex] = sq(innovOptFlow[obsIndex]) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * varInnovOptFlow[obsIndex]);
// Check the innovation for consistency and don't fuse if out of bounds or flow is too fast to be reliable
if ((flowTestRatio[obsIndex]) < 1.0f && (ofDataDelayed.flowRadXY.x < frontend->_maxFlowRate) && (ofDataDelayed.flowRadXY.y < frontend->_maxFlowRate)) {
// record the last time observations were accepted for fusion
prevFlowFuseTime_ms = imuSampleTime_ms;
// correct the covariance P = (I - K*H)*P
// take advantage of the empty columns in KH to reduce the
// number of operations
for (unsigned i = 0; i<=stateIndexLim; i++) {
for (unsigned j = 0; j<=5; j++) {
KH[i][j] = Kfusion[i] * H_LOS[j];
}
for (unsigned j = 6; j<=7; j++) {
KH[i][j] = 0.0f;
}
KH[i][8] = Kfusion[i] * H_LOS[8];
for (unsigned j = 9; j<=23; j++) {
KH[i][j] = 0.0f;
}
}
for (unsigned j = 0; j<=stateIndexLim; j++) {
for (unsigned i = 0; i<=stateIndexLim; i++) {
ftype res = 0;
res += KH[i][0] * P[0][j];
res += KH[i][1] * P[1][j];
res += KH[i][2] * P[2][j];
res += KH[i][3] * P[3][j];
res += KH[i][4] * P[4][j];
res += KH[i][5] * P[5][j];
res += KH[i][8] * P[8][j];
KHP[i][j] = res;
}
}
// Check that we are not going to drive any variances negative and skip the update if so
bool healthyFusion = true;
for (uint8_t i= 0; i<=stateIndexLim; i++) {
if (KHP[i][i] > P[i][i]) {
healthyFusion = false;
}
}
if (healthyFusion) {
// update the covariance matrix
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
ForceSymmetry();
ConstrainVariances();
// zero the attitude error state - by definition it is assumed to be zero before each observation fusion
stateStruct.angErr.zero();
// correct the state vector
for (uint8_t j= 0; j<=stateIndexLim; j++) {
statesArray[j] = statesArray[j] - Kfusion[j] * innovOptFlow[obsIndex];
}
// the first 3 states represent the angular misalignment vector.
// This is used to correct the estimated quaternion on the current time step
stateStruct.quat.rotate(stateStruct.angErr);
} else {
// record bad axis
if (obsIndex == 0) {
faultStatus.bad_xflow = true;
} else if (obsIndex == 1) {
faultStatus.bad_yflow = true;
}
}
}
}
}
/********************************************************
* MISC FUNCTIONS *
********************************************************/