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AP_NavEKF2: split otp flow from PosVelNED

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Siddharth Bharat Purohit 2015-10-06 13:50:35 -07:00 committed by Randy Mackay
parent 290ea0e1e8
commit 2e388fb2f9
2 changed files with 862 additions and 841 deletions
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libraries/AP_NavEKF2/AP_NavEKF2_OptFlow.cpp Normal file
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/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-
#include <AP_HAL/AP_HAL.h>
#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
/*
optionally turn down optimisation for debugging
*/
// #pragma GCC optimize("O0")
#include "AP_NavEKF2.h"
#include "AP_NavEKF2_core.h"
#include <AP_AHRS/AP_AHRS.h>
#include <AP_Vehicle/AP_Vehicle.h>
#include <stdio.h>
extern const AP_HAL::HAL& hal;
// return data for debugging optical flow fusion
void NavEKF2_core::getFlowDebug(float &varFlow, float &gndOffset, float &flowInnovX, float &flowInnovY, float &auxInnov, float &HAGL, float &rngInnov, float &range, float &gndOffsetErr) const
{
varFlow = max(flowTestRatio[0],flowTestRatio[1]);
gndOffset = terrainState;
flowInnovX = innovOptFlow[0];
flowInnovY = innovOptFlow[1];
auxInnov = auxFlowObsInnov;
HAGL = terrainState - stateStruct.position.z;
rngInnov = innovRng;
range = rngMea;
gndOffsetErr = sqrtf(Popt); // note Popt is constrained to be non-negative in EstimateTerrainOffset()
}
// provides the height limit to be observed by the control loops
// returns false if no height limiting is required
// this is needed to ensure the vehicle does not fly too high when using optical flow navigation
bool NavEKF2_core::getHeightControlLimit(float &height) const
{
// only ask for limiting if we are doing optical flow navigation
if (frontend._fusionModeGPS == 3) {
// If are doing optical flow nav, ensure the height above ground is within range finder limits after accounting for vehicle tilt and control errors
height = max(float(_rng.max_distance_cm()) * 0.007f - 1.0f, 1.0f);
return true;
} else {
return false;
}
}
// Read the range finder and take new measurements if available
// Read at 20Hz and apply a median filter
void NavEKF2_core::readRangeFinder(void)
{
uint8_t midIndex;
uint8_t maxIndex;
uint8_t minIndex;
// get theoretical correct range when the vehicle is on the ground
rngOnGnd = _rng.ground_clearance_cm() * 0.01f;
if (_rng.status() == RangeFinder::RangeFinder_Good && (imuSampleTime_ms - lastRngMeasTime_ms) > 50) {
// store samples and sample time into a ring buffer
rngMeasIndex ++;
if (rngMeasIndex > 2) {
rngMeasIndex = 0;
}
storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms;
storedRngMeas[rngMeasIndex] = _rng.distance_cm() * 0.01f;
// check for three fresh samples and take median
bool sampleFresh[3];
for (uint8_t index = 0; index <= 2; index++) {
sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500;
}
if (sampleFresh[0] && sampleFresh[1] && sampleFresh[2]) {
if (storedRngMeas[0] > storedRngMeas[1]) {
minIndex = 1;
maxIndex = 0;
} else {
maxIndex = 0;
minIndex = 1;
}
if (storedRngMeas[2] > storedRngMeas[maxIndex]) {
midIndex = maxIndex;
} else if (storedRngMeas[2] < storedRngMeas[minIndex]) {
midIndex = minIndex;
} else {
midIndex = 2;
}
rngMea = max(storedRngMeas[midIndex],rngOnGnd);
newDataRng = true;
rngValidMeaTime_ms = imuSampleTime_ms;
} else if (onGround) {
// if on ground and no return, we assume on ground range
rngMea = rngOnGnd;
newDataRng = true;
rngValidMeaTime_ms = imuSampleTime_ms;
} else {
newDataRng = false;
}
lastRngMeasTime_ms = imuSampleTime_ms;
}
}
// write the raw optical flow measurements
// this needs to be called externally.
void NavEKF2_core::writeOptFlowMeas(uint8_t &rawFlowQuality, Vector2f &rawFlowRates, Vector2f &rawGyroRates, uint32_t &msecFlowMeas)
{
// The raw measurements need to be optical flow rates in radians/second averaged across the time since the last update
// The PX4Flow sensor outputs flow rates with the following axis and sign conventions:
// A positive X rate is produced by a positive sensor rotation about the X axis
// A positive Y rate is produced by a positive sensor rotation about the Y axis
// This filter uses a different definition of optical flow rates to the sensor with a positive optical flow rate produced by a
// negative rotation about that axis. For example a positive rotation of the flight vehicle about its X (roll) axis would produce a negative X flow rate
flowMeaTime_ms = imuSampleTime_ms;
// calculate bias errors on flow sensor gyro rates, but protect against spikes in data
// reset the accumulated body delta angle and time
// don't do the calculation if not enough time lapsed for a reliable body rate measurement
if (delTimeOF > 0.01f) {
flowGyroBias.x = 0.99f * flowGyroBias.x + 0.01f * constrain_float((rawGyroRates.x - delAngBodyOF.x/delTimeOF),-0.1f,0.1f);
flowGyroBias.y = 0.99f * flowGyroBias.y + 0.01f * constrain_float((rawGyroRates.y - delAngBodyOF.y/delTimeOF),-0.1f,0.1f);
delAngBodyOF.zero();
delTimeOF = 0.0f;
}
// check for takeoff if relying on optical flow and zero measurements until takeoff detected
// if we haven't taken off - constrain position and velocity states
if (frontend._fusionModeGPS == 3) {
detectOptFlowTakeoff();
}
// calculate rotation matrices at mid sample time for flow observations
stateStruct.quat.rotation_matrix(Tbn_flow);
Tnb_flow = Tbn_flow.transposed();
// don't use data with a low quality indicator or extreme rates (helps catch corrupt sensor data)
if ((rawFlowQuality > 0) && rawFlowRates.length() < 4.2f && rawGyroRates.length() < 4.2f) {
// correct flow sensor rates for bias
omegaAcrossFlowTime.x = rawGyroRates.x - flowGyroBias.x;
omegaAcrossFlowTime.y = rawGyroRates.y - flowGyroBias.y;
// write uncorrected flow rate measurements that will be used by the focal length scale factor estimator
// note correction for different axis and sign conventions used by the px4flow sensor
ofDataNew.flowRadXY = - rawFlowRates; // raw (non motion compensated) optical flow angular rate about the X axis (rad/sec)
// write flow rate measurements corrected for body rates
ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + omegaAcrossFlowTime.x;
ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + omegaAcrossFlowTime.y;
// record time last observation was received so we can detect loss of data elsewhere
flowValidMeaTime_ms = imuSampleTime_ms;
// estimate sample time of the measurement
ofDataNew.time_ms = imuSampleTime_ms - frontend._flowDelay_ms - frontend.flowTimeDeltaAvg_ms/2;
// Save data to buffer
StoreOF();
// Check for data at the fusion time horizon
newDataFlow = RecallOF();
}
}
// store OF data in a history array
void NavEKF2_core::StoreOF()
{
if (ofStoreIndex >= OBS_BUFFER_LENGTH) {
ofStoreIndex = 0;
}
storedOF[ofStoreIndex] = ofDataNew;
ofStoreIndex += 1;
}
// return newest un-used optical flow data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallOF()
{
of_elements dataTemp;
of_elements dataTempZero;
dataTempZero.time_ms = 0;
uint32_t temp_ms = 0;
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
dataTemp = storedOF[i];
// find a measurement older than the fusion time horizon that we haven't checked before
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
// zero the time stamp so we won't use it again
storedOF[i]=dataTempZero;
// Find the most recent non-stale measurement that meets the time horizon criteria
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
ofDataDelayed = dataTemp;
temp_ms = dataTemp.time_ms;
}
}
}
if (temp_ms != 0) {
return true;
} else {
return false;
}
}
// select fusion of optical flow measurements
void NavEKF2_core::SelectFlowFusion()
{
// start performance timer
perf_begin(_perf_FuseOptFlow);
// Perform Data Checks
// Check if the optical flow data is still valid
flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000);
// Check if the optical flow sensor has timed out
bool flowSensorTimeout = ((imuSampleTime_ms - flowValidMeaTime_ms) > 5000);
// Check if the fusion has timed out (flow measurements have been rejected for too long)
bool flowFusionTimeout = ((imuSampleTime_ms - prevFlowFuseTime_ms) > 5000);
// check is the terrain offset estimate is still valid
gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000);
// Perform tilt check
bool tiltOK = (Tnb_flow.c.z > frontend.DCM33FlowMin);
// Constrain measurements to zero if we are using optical flow and are on the ground
if (frontend._fusionModeGPS == 3 && !takeOffDetected && isAiding) {
ofDataDelayed.flowRadXYcomp.zero();
ofDataDelayed.flowRadXY.zero();
flowDataValid = true;
}
// If the flow measurements have been rejected for too long and we are relying on them, then revert to constant position mode
if ((flowSensorTimeout || flowFusionTimeout) && PV_AidingMode == AID_RELATIVE) {
PV_AidingMode = AID_NONE;
// reset the velocity
ResetVelocity();
// store the current position to be used to as a sythetic position measurement
lastKnownPositionNE.x = stateStruct.position.x;
lastKnownPositionNE.y = stateStruct.position.y;
// reset the position
ResetPosition();
}
// if we do have valid flow measurements, fuse data into a 1-state EKF to estimate terrain height
// we don't do terrain height estimation in optical flow only mode as the ground becomes our zero height reference
if ((newDataFlow || newDataRng) && tiltOK) {
// fuse range data into the terrain estimator if available
fuseRngData = newDataRng;
// fuse optical flow data into the terrain estimator if available and if there is no range data (range data is better)
fuseOptFlowData = (newDataFlow && !fuseRngData);
// Estimate the terrain offset (runs a one state EKF)
EstimateTerrainOffset();
// Indicate we have used the range data
newDataRng = false;
}
// Fuse optical flow data into the main filter if not excessively tilted and we are in the correct mode
if (newDataFlow && tiltOK && PV_AidingMode == AID_RELATIVE)
{
// Set the flow noise used by the fusion processes
R_LOS = sq(max(frontend._flowNoise, 0.05f));
// ensure that the covariance prediction is up to date before fusing data
if (!covPredStep) CovariancePrediction();
// 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
newDataFlow = false;
}
// stop the performance timer
perf_end(_perf_FuseOptFlow);
}
/*
Estimation of terrain offset using a single state EKF
The filter can fuse motion compensated optiocal flow rates and range finder measurements
*/
void NavEKF2_core::EstimateTerrainOffset()
{
// start performance timer
perf_begin(_perf_OpticalFlowEKF);
// constrain height above ground to be above range measured on ground
float heightAboveGndEst = max((terrainState - stateStruct.position.z), rngOnGnd);
// calculate a predicted LOS rate squared
float velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y);
float losRateSq = velHorizSq / sq(heightAboveGndEst);
// don't update terrain offset state if there is no range finder and not generating enough LOS rate, or without GPS, as it is poorly observable
if (!fuseRngData && (gpsNotAvailable || PV_AidingMode == AID_RELATIVE || velHorizSq < 25.0f || losRateSq < 0.01f)) {
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 intialisation afer bad gps causing bad numerical conditioning
float 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 a time based error growth that is scaled using the gradient parameter
float timeLapsed = min(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f);
float Pincrement = (distanceTravelledSq * sq(0.01f*float(frontend.gndGradientSigma))) + sq(float(frontend.gndGradientSigma) * timeLapsed);
Popt += Pincrement;
timeAtLastAuxEKF_ms = imuSampleTime_ms;
// fuse range finder data
if (fuseRngData) {
// predict range
float predRngMeas = max((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
// Copy required states to local variable names
float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
float 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
float R_RNG = frontend._rngNoise;
// calculate Kalman gain
float SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3);
float 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 - rngMea;
// calculate the innovation consistency test ratio
auxRngTestRatio = sq(innovRng) / (sq(frontend._rngInnovGate) * varInnovRng);
// Check the innovation for consistency and don't fuse if > 5Sigma
if ((sq(innovRng)*SK_RNG) < 25.0f)
{
// 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 (fuseOptFlowData) {
Vector3f vel; // velocity of sensor relative to ground in NED axes
Vector3f relVelSensor; // velocity of sensor relative to ground in sensor axes
float losPred; // predicted optical flow angular rate measurement
float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
float K_OPT;
float H_OPT;
// Correct velocities for GPS glitch recovery offset
vel.x = stateStruct.velocity[0] - gpsVelGlitchOffset.x;
vel.y = stateStruct.velocity[1] - gpsVelGlitchOffset.y;
vel.z = stateStruct.velocity[2];
// predict range to centre of image
float flowRngPred = max((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
// constrain terrain height to be below the vehicle
terrainState = max(terrainState, stateStruct.position[2] + rngOnGnd);
// calculate relative velocity in sensor frame
relVelSensor = Tnb_flow*vel;
// 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 = relVelSensor.length()/flowRngPred;
// calculate innovations
auxFlowObsInnov = losPred - sqrtf(sq(flowRadXYcomp[0]) + sq(flowRadXYcomp[1]));
// calculate observation jacobian
float t3 = sq(q0);
float t4 = sq(q1);
float t5 = sq(q2);
float t6 = sq(q3);
float t10 = q0*q3*2.0f;
float t11 = q1*q2*2.0f;
float t14 = t3+t4-t5-t6;
float t15 = t14*vel.x;
float t16 = t10+t11;
float t17 = t16*vel.y;
float t18 = q0*q2*2.0f;
float t19 = q1*q3*2.0f;
float t20 = t18-t19;
float t21 = t20*vel.z;
float t2 = t15+t17-t21;
float t7 = t3-t4-t5+t6;
float t8 = stateStruct.position[2]-terrainState;
float t9 = 1.0f/sq(t8);
float t24 = t3-t4+t5-t6;
float t25 = t24*vel.y;
float t26 = t10-t11;
float t27 = t26*vel.x;
float t28 = q0*q1*2.0f;
float t29 = q2*q3*2.0f;
float t30 = t28+t29;
float t31 = t30*vel.z;
float t12 = t25-t27+t31;
float t13 = sq(t7);
float t22 = sq(t2);
float t23 = 1.0f/(t8*t8*t8);
float t32 = sq(t12);
H_OPT = 0.5f*(t13*t22*t23*2.0f+t13*t23*t32*2.0f)/sqrtf(t9*t13*t22+t9*t13*t32);
// calculate innovation variances
auxFlowObsInnovVar = H_OPT*Popt*H_OPT + R_LOS;
// calculate Kalman gain
K_OPT = Popt*H_OPT/auxFlowObsInnovVar;
// calculate the innovation consistency test ratio
auxFlowTestRatio = sq(auxFlowObsInnov) / (sq(frontend._flowInnovGate) * auxFlowObsInnovVar);
// don't fuse if optical flow data is outside valid range
if (max(flowRadXY[0],flowRadXY[1]) < frontend._maxFlowRate) {
// correct the state
terrainState -= K_OPT * auxFlowObsInnov;
// constrain the state
terrainState = max(terrainState, stateStruct.position[2] + rngOnGnd);
// correct the covariance
Popt = Popt - K_OPT * H_OPT * Popt;
// prevent the state variances from becoming negative
Popt = max(Popt,0.0f);
}
}
}
// stop the performance timer
perf_end(_perf_OpticalFlowEKF);
}
/*
Fuse angular motion compensated optical flow rates into the main filter.
Requires a valid terrain height estimate.
*/
void NavEKF2_core::FuseOptFlow()
{
Vector24 H_LOS;
Vector3f velNED_local;
Vector3f relVelSensor;
Vector14 SH_LOS;
Vector2 losPred;
// Copy required states to local variable names
float q0 = stateStruct.quat[0];
float q1 = stateStruct.quat[1];
float q2 = stateStruct.quat[2];
float q3 = stateStruct.quat[3];
float vn = stateStruct.velocity.x;
float ve = stateStruct.velocity.y;
float vd = stateStruct.velocity.z;
float pd = stateStruct.position.z;
// Correct velocities for GPS glitch recovery offset
velNED_local.x = vn - gpsVelGlitchOffset.x;
velNED_local.y = ve - gpsVelGlitchOffset.y;
velNED_local.z = vd;
// constrain height above ground to be above range measured on ground
float heightAboveGndEst = max((terrainState - pd), rngOnGnd);
float 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 plain to centre of sensor fov assuming flat earth
float range = constrain_float((heightAboveGndEst/Tnb_flow.c.z),rngOnGnd,1000.0f);
// calculate relative velocity in sensor frame
relVelSensor = Tnb_flow*velNED_local;
// 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];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[2];
float t5 = SH_LOS[6];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t9 = t2*t4*t5;
float t8 = t7-t9;
float t10 = q0*q3*2.0f;
float t21 = q1*q2*2.0f;
float t11 = t10-t21;
float t101 = t2 * t3 * t11;
float t12 = pd-ptd;
float t13 = 1.0f/(t12*t12);
float t104 = t3 * t4 * t13;
float t14 = SH_LOS[5];
float t102 = t2 * t4 * t14;
float t15 = SH_LOS[1];
float t103 = t2 * t3 * t15;
float t16 = q0*q0;
float t17 = q1*q1;
float t18 = q2*q2;
float t19 = q3*q3;
float t20 = t16-t17+t18-t19;
float t105 = t2 * t3 * t20;
float t22 = P[1][1]*t102;
float t23 = P[3][0]*t101;
float t24 = P[8][0]*t104;
float t25 = P[1][0]*t102;
float t26 = P[2][0]*t103;
float t63 = P[0][0]*t8;
float t64 = P[5][0]*t100;
float t65 = P[4][0]*t105;
float t27 = t23+t24+t25+t26-t63-t64-t65;
float t28 = P[3][3]*t101;
float t29 = P[8][3]*t104;
float t30 = P[1][3]*t102;
float t31 = P[2][3]*t103;
float t67 = P[0][3]*t8;
float t68 = P[5][3]*t100;
float t69 = P[4][3]*t105;
float t32 = t28+t29+t30+t31-t67-t68-t69;
float t33 = t101*t32;
float t34 = P[3][8]*t101;
float t35 = P[8][8]*t104;
float t36 = P[1][8]*t102;
float t37 = P[2][8]*t103;
float t70 = P[0][8]*t8;
float t71 = P[5][8]*t100;
float t72 = P[4][8]*t105;
float t38 = t34+t35+t36+t37-t70-t71-t72;
float t39 = t104*t38;
float t40 = P[3][1]*t101;
float t41 = P[8][1]*t104;
float t42 = P[2][1]*t103;
float t73 = P[0][1]*t8;
float t74 = P[5][1]*t100;
float t75 = P[4][1]*t105;
float t43 = t22+t40+t41+t42-t73-t74-t75;
float t44 = t102*t43;
float t45 = P[3][2]*t101;
float t46 = P[8][2]*t104;
float t47 = P[1][2]*t102;
float t48 = P[2][2]*t103;
float t76 = P[0][2]*t8;
float t77 = P[5][2]*t100;
float t78 = P[4][2]*t105;
float t49 = t45+t46+t47+t48-t76-t77-t78;
float t50 = t103*t49;
float t51 = P[3][5]*t101;
float t52 = P[8][5]*t104;
float t53 = P[1][5]*t102;
float t54 = P[2][5]*t103;
float t79 = P[0][5]*t8;
float t80 = P[5][5]*t100;
float t81 = P[4][5]*t105;
float t55 = t51+t52+t53+t54-t79-t80-t81;
float t56 = P[3][4]*t101;
float t57 = P[8][4]*t104;
float t58 = P[1][4]*t102;
float t59 = P[2][4]*t103;
float t83 = P[0][4]*t8;
float t84 = P[5][4]*t100;
float t85 = P[4][4]*t105;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t66 = t8*t27;
float t82 = t100*t55;
float t86 = t105*t60;
float t61 = R_LOS+t33+t39+t44+t50-t66-t82-t86;
float 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;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
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];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[1];
float t5 = SH_LOS[5];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t8 = t2*t4*t5;
float t9 = t7+t8;
float t10 = q0*q3*2.0f;
float t11 = q1*q2*2.0f;
float t12 = t10+t11;
float t101 = t2 * t3 * t12;
float t13 = pd-ptd;
float t14 = 1.0f/(t13*t13);
float t104 = t3 * t4 * t14;
float t15 = SH_LOS[6];
float t105 = t2 * t4 * t15;
float t16 = SH_LOS[2];
float t102 = t2 * t3 * t16;
float t17 = q0*q0;
float t18 = q1*q1;
float t19 = q2*q2;
float t20 = q3*q3;
float t21 = t17+t18-t19-t20;
float t103 = t2 * t3 * t21;
float t22 = P[0][0]*t105;
float t23 = P[1][1]*t9;
float t24 = P[8][1]*t104;
float t25 = P[0][1]*t105;
float t26 = P[5][1]*t100;
float t64 = P[4][1]*t101;
float t65 = P[2][1]*t102;
float t66 = P[3][1]*t103;
float t27 = t23+t24+t25+t26-t64-t65-t66;
float t28 = t9*t27;
float t29 = P[1][4]*t9;
float t30 = P[8][4]*t104;
float t31 = P[0][4]*t105;
float t32 = P[5][4]*t100;
float t67 = P[4][4]*t101;
float t68 = P[2][4]*t102;
float t69 = P[3][4]*t103;
float t33 = t29+t30+t31+t32-t67-t68-t69;
float t34 = P[1][8]*t9;
float t35 = P[8][8]*t104;
float t36 = P[0][8]*t105;
float t37 = P[5][8]*t100;
float t71 = P[4][8]*t101;
float t72 = P[2][8]*t102;
float t73 = P[3][8]*t103;
float t38 = t34+t35+t36+t37-t71-t72-t73;
float t39 = t104*t38;
float t40 = P[1][0]*t9;
float t41 = P[8][0]*t104;
float t42 = P[5][0]*t100;
float t74 = P[4][0]*t101;
float t75 = P[2][0]*t102;
float t76 = P[3][0]*t103;
float t43 = t22+t40+t41+t42-t74-t75-t76;
float t44 = t105*t43;
float t45 = P[1][2]*t9;
float t46 = P[8][2]*t104;
float t47 = P[0][2]*t105;
float t48 = P[5][2]*t100;
float t63 = P[2][2]*t102;
float t77 = P[4][2]*t101;
float t78 = P[3][2]*t103;
float t49 = t45+t46+t47+t48-t63-t77-t78;
float t50 = P[1][5]*t9;
float t51 = P[8][5]*t104;
float t52 = P[0][5]*t105;
float t53 = P[5][5]*t100;
float t80 = P[4][5]*t101;
float t81 = P[2][5]*t102;
float t82 = P[3][5]*t103;
float t54 = t50+t51+t52+t53-t80-t81-t82;
float t55 = t100*t54;
float t56 = P[1][3]*t9;
float t57 = P[8][3]*t104;
float t58 = P[0][3]*t105;
float t59 = P[5][3]*t100;
float t83 = P[4][3]*t101;
float t84 = P[2][3]*t102;
float t85 = P[3][3]*t103;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t70 = t101*t33;
float t79 = t102*t49;
float t86 = t103*t60;
float t61 = R_LOS+t28+t39+t44+t55-t70-t79-t86;
float 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;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
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(frontend._flowInnovGate) * 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;
// zero the attitude error state - by definition it is assumed to be zero before each observaton 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
// is used to correct the estimated quaternion on the current time step
stateStruct.quat.rotate(stateStruct.angErr);
// correct the covariance P = (I - K*H)*P
// take advantage of the empty columns in KH to reduce the
// number of operations
for (uint8_t i = 0; i<=stateIndexLim; i++) {
for (uint8_t j = 0; j<=5; j++) {
KH[i][j] = Kfusion[i] * H_LOS[j];
}
for (uint8_t j = 6; j<=7; j++) {
KH[i][j] = 0.0f;
}
KH[i][8] = Kfusion[i] * H_LOS[8];
for (uint8_t j = 9; j<=23; j++) {
KH[i][j] = 0.0f;
}
}
for (uint8_t i = 0; i<=stateIndexLim; i++) {
for (uint8_t j = 0; j<=stateIndexLim; j++) {
KHP[i][j] = 0;
for (uint8_t k = 0; k<=5; k++) {
KHP[i][j] = KHP[i][j] + KH[i][k] * P[k][j];
}
KHP[i][j] = KHP[i][j] + KH[i][8] * P[8][j];
}
}
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];
}
}
}
// fix basic numerical errors
ForceSymmetry();
ConstrainVariances();
}
}
#endif // HAL_CPU_CLASS

841
libraries/AP_NavEKF2/AP_NavEKF2_PosVelNED.cpp
View File

@ -253,37 +253,6 @@ bool NavEKF2_core::getHAGL(float &HAGL) const
return !hgtTimeout && gndOffsetValid && healthy(); return !hgtTimeout && gndOffsetValid && healthy();
} }
// return data for debugging optical flow fusion
void NavEKF2_core::getFlowDebug(float &varFlow, float &gndOffset, float &flowInnovX, float &flowInnovY, float &auxInnov, float &HAGL, float &rngInnov, float &range, float &gndOffsetErr) const
{
varFlow = max(flowTestRatio[0],flowTestRatio[1]);
gndOffset = terrainState;
flowInnovX = innovOptFlow[0];
flowInnovY = innovOptFlow[1];
auxInnov = auxFlowObsInnov;
HAGL = terrainState - stateStruct.position.z;
rngInnov = innovRng;
range = rngMea;
gndOffsetErr = sqrtf(Popt); // note Popt is constrained to be non-negative in EstimateTerrainOffset()
}
// provides the height limit to be observed by the control loops
// returns false if no height limiting is required
// this is needed to ensure the vehicle does not fly too high when using optical flow navigation
bool NavEKF2_core::getHeightControlLimit(float &height) const
{
// only ask for limiting if we are doing optical flow navigation
if (frontend._fusionModeGPS == 3) {
// If are doing optical flow nav, ensure the height above ground is within range finder limits after accounting for vehicle tilt and control errors
height = max(float(_rng.max_distance_cm()) * 0.007f - 1.0f, 1.0f);
return true;
} else {
return false;
}
}
/******************************************************** /********************************************************
* SET STATES/PARAMS FUNCTIONS * * SET STATES/PARAMS FUNCTIONS *
********************************************************/ ********************************************************/
@ -629,147 +598,6 @@ bool NavEKF2_core::RecallBaro()
} }
} }
// Read the range finder and take new measurements if available
// Read at 20Hz and apply a median filter
void NavEKF2_core::readRangeFinder(void)
{
uint8_t midIndex;
uint8_t maxIndex;
uint8_t minIndex;
// get theoretical correct range when the vehicle is on the ground
rngOnGnd = _rng.ground_clearance_cm() * 0.01f;
if (_rng.status() == RangeFinder::RangeFinder_Good && (imuSampleTime_ms - lastRngMeasTime_ms) > 50) {
// store samples and sample time into a ring buffer
rngMeasIndex ++;
if (rngMeasIndex > 2) {
rngMeasIndex = 0;
}
storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms;
storedRngMeas[rngMeasIndex] = _rng.distance_cm() * 0.01f;
// check for three fresh samples and take median
bool sampleFresh[3];
for (uint8_t index = 0; index <= 2; index++) {
sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500;
}
if (sampleFresh[0] && sampleFresh[1] && sampleFresh[2]) {
if (storedRngMeas[0] > storedRngMeas[1]) {
minIndex = 1;
maxIndex = 0;
} else {
maxIndex = 0;
minIndex = 1;
}
if (storedRngMeas[2] > storedRngMeas[maxIndex]) {
midIndex = maxIndex;
} else if (storedRngMeas[2] < storedRngMeas[minIndex]) {
midIndex = minIndex;
} else {
midIndex = 2;
}
rngMea = max(storedRngMeas[midIndex],rngOnGnd);
newDataRng = true;
rngValidMeaTime_ms = imuSampleTime_ms;
} else if (onGround) {
// if on ground and no return, we assume on ground range
rngMea = rngOnGnd;
newDataRng = true;
rngValidMeaTime_ms = imuSampleTime_ms;
} else {
newDataRng = false;
}
lastRngMeasTime_ms = imuSampleTime_ms;
}
}
// write the raw optical flow measurements
// this needs to be called externally.
void NavEKF2_core::writeOptFlowMeas(uint8_t &rawFlowQuality, Vector2f &rawFlowRates, Vector2f &rawGyroRates, uint32_t &msecFlowMeas)
{
// The raw measurements need to be optical flow rates in radians/second averaged across the time since the last update
// The PX4Flow sensor outputs flow rates with the following axis and sign conventions:
// A positive X rate is produced by a positive sensor rotation about the X axis
// A positive Y rate is produced by a positive sensor rotation about the Y axis
// This filter uses a different definition of optical flow rates to the sensor with a positive optical flow rate produced by a
// negative rotation about that axis. For example a positive rotation of the flight vehicle about its X (roll) axis would produce a negative X flow rate
flowMeaTime_ms = imuSampleTime_ms;
// calculate bias errors on flow sensor gyro rates, but protect against spikes in data
// reset the accumulated body delta angle and time
// don't do the calculation if not enough time lapsed for a reliable body rate measurement
if (delTimeOF > 0.01f) {
flowGyroBias.x = 0.99f * flowGyroBias.x + 0.01f * constrain_float((rawGyroRates.x - delAngBodyOF.x/delTimeOF),-0.1f,0.1f);
flowGyroBias.y = 0.99f * flowGyroBias.y + 0.01f * constrain_float((rawGyroRates.y - delAngBodyOF.y/delTimeOF),-0.1f,0.1f);
delAngBodyOF.zero();
delTimeOF = 0.0f;
}
// check for takeoff if relying on optical flow and zero measurements until takeoff detected
// if we haven't taken off - constrain position and velocity states
if (frontend._fusionModeGPS == 3) {
detectOptFlowTakeoff();
}
// calculate rotation matrices at mid sample time for flow observations
stateStruct.quat.rotation_matrix(Tbn_flow);
Tnb_flow = Tbn_flow.transposed();
// don't use data with a low quality indicator or extreme rates (helps catch corrupt sensor data)
if ((rawFlowQuality > 0) && rawFlowRates.length() < 4.2f && rawGyroRates.length() < 4.2f) {
// correct flow sensor rates for bias
omegaAcrossFlowTime.x = rawGyroRates.x - flowGyroBias.x;
omegaAcrossFlowTime.y = rawGyroRates.y - flowGyroBias.y;
// write uncorrected flow rate measurements that will be used by the focal length scale factor estimator
// note correction for different axis and sign conventions used by the px4flow sensor
ofDataNew.flowRadXY = - rawFlowRates; // raw (non motion compensated) optical flow angular rate about the X axis (rad/sec)
// write flow rate measurements corrected for body rates
ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + omegaAcrossFlowTime.x;
ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + omegaAcrossFlowTime.y;
// record time last observation was received so we can detect loss of data elsewhere
flowValidMeaTime_ms = imuSampleTime_ms;
// estimate sample time of the measurement
ofDataNew.time_ms = imuSampleTime_ms - frontend._flowDelay_ms - frontend.flowTimeDeltaAvg_ms/2;
// Save data to buffer
StoreOF();
// Check for data at the fusion time horizon
newDataFlow = RecallOF();
}
}
// store OF data in a history array
void NavEKF2_core::StoreOF()
{
if (ofStoreIndex >= OBS_BUFFER_LENGTH) {
ofStoreIndex = 0;
}
storedOF[ofStoreIndex] = ofDataNew;
ofStoreIndex += 1;
}
// return newest un-used optical flow data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallOF()
{
of_elements dataTemp;
of_elements dataTempZero;
dataTempZero.time_ms = 0;
uint32_t temp_ms = 0;
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
dataTemp = storedOF[i];
// find a measurement older than the fusion time horizon that we haven't checked before
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
// zero the time stamp so we won't use it again
storedOF[i]=dataTempZero;
// Find the most recent non-stale measurement that meets the time horizon criteria
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
ofDataDelayed = dataTemp;
temp_ms = dataTemp.time_ms;
}
}
}
if (temp_ms != 0) {
return true;
} else {
return false;
}
}
/******************************************************** /********************************************************
* FUSE MEASURED_DATA * * FUSE MEASURED_DATA *
********************************************************/ ********************************************************/
@ -1154,675 +982,6 @@ void NavEKF2_core::FuseVelPosNED()
perf_end(_perf_FuseVelPosNED); perf_end(_perf_FuseVelPosNED);
} }
// select fusion of optical flow measurements
void NavEKF2_core::SelectFlowFusion()
{
// start performance timer
perf_begin(_perf_FuseOptFlow);
// Perform Data Checks
// Check if the optical flow data is still valid
flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000);
// Check if the optical flow sensor has timed out
bool flowSensorTimeout = ((imuSampleTime_ms - flowValidMeaTime_ms) > 5000);
// Check if the fusion has timed out (flow measurements have been rejected for too long)
bool flowFusionTimeout = ((imuSampleTime_ms - prevFlowFuseTime_ms) > 5000);
// check is the terrain offset estimate is still valid
gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000);
// Perform tilt check
bool tiltOK = (Tnb_flow.c.z > frontend.DCM33FlowMin);
// Constrain measurements to zero if we are using optical flow and are on the ground
if (frontend._fusionModeGPS == 3 && !takeOffDetected && isAiding) {
ofDataDelayed.flowRadXYcomp.zero();
ofDataDelayed.flowRadXY.zero();
flowDataValid = true;
}
// If the flow measurements have been rejected for too long and we are relying on them, then revert to constant position mode
if ((flowSensorTimeout || flowFusionTimeout) && PV_AidingMode == AID_RELATIVE) {
PV_AidingMode = AID_NONE;
// reset the velocity
ResetVelocity();
// store the current position to be used to as a sythetic position measurement
lastKnownPositionNE.x = stateStruct.position.x;
lastKnownPositionNE.y = stateStruct.position.y;
// reset the position
ResetPosition();
}
// if we do have valid flow measurements, fuse data into a 1-state EKF to estimate terrain height
// we don't do terrain height estimation in optical flow only mode as the ground becomes our zero height reference
if ((newDataFlow || newDataRng) && tiltOK) {
// fuse range data into the terrain estimator if available
fuseRngData = newDataRng;
// fuse optical flow data into the terrain estimator if available and if there is no range data (range data is better)
fuseOptFlowData = (newDataFlow && !fuseRngData);
// Estimate the terrain offset (runs a one state EKF)
EstimateTerrainOffset();
// Indicate we have used the range data
newDataRng = false;
}
// Fuse optical flow data into the main filter if not excessively tilted and we are in the correct mode
if (newDataFlow && tiltOK && PV_AidingMode == AID_RELATIVE)
{
// Set the flow noise used by the fusion processes
R_LOS = sq(max(frontend._flowNoise, 0.05f));
// ensure that the covariance prediction is up to date before fusing data
if (!covPredStep) CovariancePrediction();
// 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
newDataFlow = false;
}
// stop the performance timer
perf_end(_perf_FuseOptFlow);
}
/*
Estimation of terrain offset using a single state EKF
The filter can fuse motion compensated optiocal flow rates and range finder measurements
*/
void NavEKF2_core::EstimateTerrainOffset()
{
// start performance timer
perf_begin(_perf_OpticalFlowEKF);
// constrain height above ground to be above range measured on ground
float heightAboveGndEst = max((terrainState - stateStruct.position.z), rngOnGnd);
// calculate a predicted LOS rate squared
float velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y);
float losRateSq = velHorizSq / sq(heightAboveGndEst);
// don't update terrain offset state if there is no range finder and not generating enough LOS rate, or without GPS, as it is poorly observable
if (!fuseRngData && (gpsNotAvailable || PV_AidingMode == AID_RELATIVE || velHorizSq < 25.0f || losRateSq < 0.01f)) {
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 intialisation afer bad gps causing bad numerical conditioning
float 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 a time based error growth that is scaled using the gradient parameter
float timeLapsed = min(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f);
float Pincrement = (distanceTravelledSq * sq(0.01f*float(frontend.gndGradientSigma))) + sq(float(frontend.gndGradientSigma) * timeLapsed);
Popt += Pincrement;
timeAtLastAuxEKF_ms = imuSampleTime_ms;
// fuse range finder data
if (fuseRngData) {
// predict range
float predRngMeas = max((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
// Copy required states to local variable names
float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
float 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
float R_RNG = frontend._rngNoise;
// calculate Kalman gain
float SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3);
float 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 - rngMea;
// calculate the innovation consistency test ratio
auxRngTestRatio = sq(innovRng) / (sq(frontend._rngInnovGate) * varInnovRng);
// Check the innovation for consistency and don't fuse if > 5Sigma
if ((sq(innovRng)*SK_RNG) < 25.0f)
{
// 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 (fuseOptFlowData) {
Vector3f vel; // velocity of sensor relative to ground in NED axes
Vector3f relVelSensor; // velocity of sensor relative to ground in sensor axes
float losPred; // predicted optical flow angular rate measurement
float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
float K_OPT;
float H_OPT;
// Correct velocities for GPS glitch recovery offset
vel.x = stateStruct.velocity[0] - gpsVelGlitchOffset.x;
vel.y = stateStruct.velocity[1] - gpsVelGlitchOffset.y;
vel.z = stateStruct.velocity[2];
// predict range to centre of image
float flowRngPred = max((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
// constrain terrain height to be below the vehicle
terrainState = max(terrainState, stateStruct.position[2] + rngOnGnd);
// calculate relative velocity in sensor frame
relVelSensor = Tnb_flow*vel;
// 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 = relVelSensor.length()/flowRngPred;
// calculate innovations
auxFlowObsInnov = losPred - sqrtf(sq(flowRadXYcomp[0]) + sq(flowRadXYcomp[1]));
// calculate observation jacobian
float t3 = sq(q0);
float t4 = sq(q1);
float t5 = sq(q2);
float t6 = sq(q3);
float t10 = q0*q3*2.0f;
float t11 = q1*q2*2.0f;
float t14 = t3+t4-t5-t6;
float t15 = t14*vel.x;
float t16 = t10+t11;
float t17 = t16*vel.y;
float t18 = q0*q2*2.0f;
float t19 = q1*q3*2.0f;
float t20 = t18-t19;
float t21 = t20*vel.z;
float t2 = t15+t17-t21;
float t7 = t3-t4-t5+t6;
float t8 = stateStruct.position[2]-terrainState;
float t9 = 1.0f/sq(t8);
float t24 = t3-t4+t5-t6;
float t25 = t24*vel.y;
float t26 = t10-t11;
float t27 = t26*vel.x;
float t28 = q0*q1*2.0f;
float t29 = q2*q3*2.0f;
float t30 = t28+t29;
float t31 = t30*vel.z;
float t12 = t25-t27+t31;
float t13 = sq(t7);
float t22 = sq(t2);
float t23 = 1.0f/(t8*t8*t8);
float t32 = sq(t12);
H_OPT = 0.5f*(t13*t22*t23*2.0f+t13*t23*t32*2.0f)/sqrtf(t9*t13*t22+t9*t13*t32);
// calculate innovation variances
auxFlowObsInnovVar = H_OPT*Popt*H_OPT + R_LOS;
// calculate Kalman gain
K_OPT = Popt*H_OPT/auxFlowObsInnovVar;
// calculate the innovation consistency test ratio
auxFlowTestRatio = sq(auxFlowObsInnov) / (sq(frontend._flowInnovGate) * auxFlowObsInnovVar);
// don't fuse if optical flow data is outside valid range
if (max(flowRadXY[0],flowRadXY[1]) < frontend._maxFlowRate) {
// correct the state
terrainState -= K_OPT * auxFlowObsInnov;
// constrain the state
terrainState = max(terrainState, stateStruct.position[2] + rngOnGnd);
// correct the covariance
Popt = Popt - K_OPT * H_OPT * Popt;
// prevent the state variances from becoming negative
Popt = max(Popt,0.0f);
}
}
}
// stop the performance timer
perf_end(_perf_OpticalFlowEKF);
}
/*
Fuse angular motion compensated optical flow rates into the main filter.
Requires a valid terrain height estimate.
*/
void NavEKF2_core::FuseOptFlow()
{
Vector24 H_LOS;
Vector3f velNED_local;
Vector3f relVelSensor;
Vector14 SH_LOS;
Vector2 losPred;
// Copy required states to local variable names
float q0 = stateStruct.quat[0];
float q1 = stateStruct.quat[1];
float q2 = stateStruct.quat[2];
float q3 = stateStruct.quat[3];
float vn = stateStruct.velocity.x;
float ve = stateStruct.velocity.y;
float vd = stateStruct.velocity.z;
float pd = stateStruct.position.z;
// Correct velocities for GPS glitch recovery offset
velNED_local.x = vn - gpsVelGlitchOffset.x;
velNED_local.y = ve - gpsVelGlitchOffset.y;
velNED_local.z = vd;
// constrain height above ground to be above range measured on ground
float heightAboveGndEst = max((terrainState - pd), rngOnGnd);
float 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 plain to centre of sensor fov assuming flat earth
float range = constrain_float((heightAboveGndEst/Tnb_flow.c.z),rngOnGnd,1000.0f);
// calculate relative velocity in sensor frame
relVelSensor = Tnb_flow*velNED_local;
// 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];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[2];
float t5 = SH_LOS[6];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t9 = t2*t4*t5;
float t8 = t7-t9;
float t10 = q0*q3*2.0f;
float t21 = q1*q2*2.0f;
float t11 = t10-t21;
float t101 = t2 * t3 * t11;
float t12 = pd-ptd;
float t13 = 1.0f/(t12*t12);
float t104 = t3 * t4 * t13;
float t14 = SH_LOS[5];
float t102 = t2 * t4 * t14;
float t15 = SH_LOS[1];
float t103 = t2 * t3 * t15;
float t16 = q0*q0;
float t17 = q1*q1;
float t18 = q2*q2;
float t19 = q3*q3;
float t20 = t16-t17+t18-t19;
float t105 = t2 * t3 * t20;
float t22 = P[1][1]*t102;
float t23 = P[3][0]*t101;
float t24 = P[8][0]*t104;
float t25 = P[1][0]*t102;
float t26 = P[2][0]*t103;
float t63 = P[0][0]*t8;
float t64 = P[5][0]*t100;
float t65 = P[4][0]*t105;
float t27 = t23+t24+t25+t26-t63-t64-t65;
float t28 = P[3][3]*t101;
float t29 = P[8][3]*t104;
float t30 = P[1][3]*t102;
float t31 = P[2][3]*t103;
float t67 = P[0][3]*t8;
float t68 = P[5][3]*t100;
float t69 = P[4][3]*t105;
float t32 = t28+t29+t30+t31-t67-t68-t69;
float t33 = t101*t32;
float t34 = P[3][8]*t101;
float t35 = P[8][8]*t104;
float t36 = P[1][8]*t102;
float t37 = P[2][8]*t103;
float t70 = P[0][8]*t8;
float t71 = P[5][8]*t100;
float t72 = P[4][8]*t105;
float t38 = t34+t35+t36+t37-t70-t71-t72;
float t39 = t104*t38;
float t40 = P[3][1]*t101;
float t41 = P[8][1]*t104;
float t42 = P[2][1]*t103;
float t73 = P[0][1]*t8;
float t74 = P[5][1]*t100;
float t75 = P[4][1]*t105;
float t43 = t22+t40+t41+t42-t73-t74-t75;
float t44 = t102*t43;
float t45 = P[3][2]*t101;
float t46 = P[8][2]*t104;
float t47 = P[1][2]*t102;
float t48 = P[2][2]*t103;
float t76 = P[0][2]*t8;
float t77 = P[5][2]*t100;
float t78 = P[4][2]*t105;
float t49 = t45+t46+t47+t48-t76-t77-t78;
float t50 = t103*t49;
float t51 = P[3][5]*t101;
float t52 = P[8][5]*t104;
float t53 = P[1][5]*t102;
float t54 = P[2][5]*t103;
float t79 = P[0][5]*t8;
float t80 = P[5][5]*t100;
float t81 = P[4][5]*t105;
float t55 = t51+t52+t53+t54-t79-t80-t81;
float t56 = P[3][4]*t101;
float t57 = P[8][4]*t104;
float t58 = P[1][4]*t102;
float t59 = P[2][4]*t103;
float t83 = P[0][4]*t8;
float t84 = P[5][4]*t100;
float t85 = P[4][4]*t105;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t66 = t8*t27;
float t82 = t100*t55;
float t86 = t105*t60;
float t61 = R_LOS+t33+t39+t44+t50-t66-t82-t86;
float 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;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
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];
float t2 = SH_LOS[3];
float t3 = SH_LOS[0];
float t4 = SH_LOS[1];
float t5 = SH_LOS[5];
float t100 = t2 * t3 * t5;
float t6 = SH_LOS[4];
float t7 = t2*t3*t6;
float t8 = t2*t4*t5;
float t9 = t7+t8;
float t10 = q0*q3*2.0f;
float t11 = q1*q2*2.0f;
float t12 = t10+t11;
float t101 = t2 * t3 * t12;
float t13 = pd-ptd;
float t14 = 1.0f/(t13*t13);
float t104 = t3 * t4 * t14;
float t15 = SH_LOS[6];
float t105 = t2 * t4 * t15;
float t16 = SH_LOS[2];
float t102 = t2 * t3 * t16;
float t17 = q0*q0;
float t18 = q1*q1;
float t19 = q2*q2;
float t20 = q3*q3;
float t21 = t17+t18-t19-t20;
float t103 = t2 * t3 * t21;
float t22 = P[0][0]*t105;
float t23 = P[1][1]*t9;
float t24 = P[8][1]*t104;
float t25 = P[0][1]*t105;
float t26 = P[5][1]*t100;
float t64 = P[4][1]*t101;
float t65 = P[2][1]*t102;
float t66 = P[3][1]*t103;
float t27 = t23+t24+t25+t26-t64-t65-t66;
float t28 = t9*t27;
float t29 = P[1][4]*t9;
float t30 = P[8][4]*t104;
float t31 = P[0][4]*t105;
float t32 = P[5][4]*t100;
float t67 = P[4][4]*t101;
float t68 = P[2][4]*t102;
float t69 = P[3][4]*t103;
float t33 = t29+t30+t31+t32-t67-t68-t69;
float t34 = P[1][8]*t9;
float t35 = P[8][8]*t104;
float t36 = P[0][8]*t105;
float t37 = P[5][8]*t100;
float t71 = P[4][8]*t101;
float t72 = P[2][8]*t102;
float t73 = P[3][8]*t103;
float t38 = t34+t35+t36+t37-t71-t72-t73;
float t39 = t104*t38;
float t40 = P[1][0]*t9;
float t41 = P[8][0]*t104;
float t42 = P[5][0]*t100;
float t74 = P[4][0]*t101;
float t75 = P[2][0]*t102;
float t76 = P[3][0]*t103;
float t43 = t22+t40+t41+t42-t74-t75-t76;
float t44 = t105*t43;
float t45 = P[1][2]*t9;
float t46 = P[8][2]*t104;
float t47 = P[0][2]*t105;
float t48 = P[5][2]*t100;
float t63 = P[2][2]*t102;
float t77 = P[4][2]*t101;
float t78 = P[3][2]*t103;
float t49 = t45+t46+t47+t48-t63-t77-t78;
float t50 = P[1][5]*t9;
float t51 = P[8][5]*t104;
float t52 = P[0][5]*t105;
float t53 = P[5][5]*t100;
float t80 = P[4][5]*t101;
float t81 = P[2][5]*t102;
float t82 = P[3][5]*t103;
float t54 = t50+t51+t52+t53-t80-t81-t82;
float t55 = t100*t54;
float t56 = P[1][3]*t9;
float t57 = P[8][3]*t104;
float t58 = P[0][3]*t105;
float t59 = P[5][3]*t100;
float t83 = P[4][3]*t101;
float t84 = P[2][3]*t102;
float t85 = P[3][3]*t103;
float t60 = t56+t57+t58+t59-t83-t84-t85;
float t70 = t101*t33;
float t79 = t102*t49;
float t86 = t103*t60;
float t61 = R_LOS+t28+t39+t44+t55-t70-t79-t86;
float 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;
} else {
t61 = 0.0f;
t62 = 1.0f/R_LOS;
}
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(frontend._flowInnovGate) * 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;
// zero the attitude error state - by definition it is assumed to be zero before each observaton 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
// is used to correct the estimated quaternion on the current time step
stateStruct.quat.rotate(stateStruct.angErr);
// correct the covariance P = (I - K*H)*P
// take advantage of the empty columns in KH to reduce the
// number of operations
for (uint8_t i = 0; i<=stateIndexLim; i++) {
for (uint8_t j = 0; j<=5; j++) {
KH[i][j] = Kfusion[i] * H_LOS[j];
}
for (uint8_t j = 6; j<=7; j++) {
KH[i][j] = 0.0f;
}
KH[i][8] = Kfusion[i] * H_LOS[8];
for (uint8_t j = 9; j<=23; j++) {
KH[i][j] = 0.0f;
}
}
for (uint8_t i = 0; i<=stateIndexLim; i++) {
for (uint8_t j = 0; j<=stateIndexLim; j++) {
KHP[i][j] = 0;
for (uint8_t k = 0; k<=5; k++) {
KHP[i][j] = KHP[i][j] + KH[i][k] * P[k][j];
}
KHP[i][j] = KHP[i][j] + KH[i][8] * P[8][j];
}
}
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];
}
}
}
// fix basic numerical errors
ForceSymmetry();
ConstrainVariances();
}
}
/******************************************************** /********************************************************
* MISC FUNCTIONS * * MISC FUNCTIONS *
********************************************************/ ********************************************************/