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ardupilot/libraries/AP_NavEKF2/AP_NavEKF2_PosVelFusion.cpp

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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
#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;
/********************************************************
* RESET FUNCTIONS *
********************************************************/
// Reset velocity states to last GPS measurement if available or to zero if in constant position mode or if PV aiding is not absolute
// Do not reset vertical velocity using GPS as there is baro alt available to constrain drift
void NavEKF2_core::ResetVelocity(void)
{
if (PV_AidingMode != AID_ABSOLUTE) {
stateStruct.velocity.zero();
} else if (!gpsNotAvailable) {
// reset horizontal velocity states, applying an offset to the GPS velocity to prevent the GPS position being rejected when the GPS position offset is being decayed to zero.
stateStruct.velocity.x = gpsDataNew.vel.x + gpsVelGlitchOffset.x; // north velocity from blended accel data
stateStruct.velocity.y = gpsDataNew.vel.y + gpsVelGlitchOffset.y; // east velocity from blended accel data
}
for (uint8_t i=0; i<IMU_BUFFER_LENGTH; i++) {
storedOutput[i].velocity.x = stateStruct.velocity.x;
storedOutput[i].velocity.y = stateStruct.velocity.y;
}
outputDataNew.velocity.x = stateStruct.velocity.x;
outputDataNew.velocity.y = stateStruct.velocity.y;
outputDataDelayed.velocity.x = stateStruct.velocity.x;
outputDataDelayed.velocity.y = stateStruct.velocity.y;
}
// resets position states to last GPS measurement or to zero if in constant position mode
void NavEKF2_core::ResetPosition(void)
{
if (PV_AidingMode != AID_ABSOLUTE) {
// reset all position state history to the last known position
stateStruct.position.x = lastKnownPositionNE.x;
stateStruct.position.y = lastKnownPositionNE.y;
} else if (!gpsNotAvailable) {
// write to state vector and compensate for offset between last GPs measurement and the EKF time horizon
stateStruct.position.x = gpsDataNew.pos.x + gpsPosGlitchOffsetNE.x + 0.001f*gpsDataNew.vel.x*(float(imuDataDelayed.time_ms) - float(lastTimeGpsReceived_ms));
stateStruct.position.y = gpsDataNew.pos.y + gpsPosGlitchOffsetNE.y + 0.001f*gpsDataNew.vel.y*(float(imuDataDelayed.time_ms) - float(lastTimeGpsReceived_ms));
}
for (uint8_t i=0; i<IMU_BUFFER_LENGTH; i++) {
storedOutput[i].position.x = stateStruct.position.x;
storedOutput[i].position.y = stateStruct.position.y;
}
outputDataNew.position.x = stateStruct.position.x;
outputDataNew.position.y = stateStruct.position.y;
outputDataDelayed.position.x = stateStruct.position.x;
outputDataDelayed.position.y = stateStruct.position.y;
}
// reset the vertical position state using the last height measurement
void NavEKF2_core::ResetHeight(void)
{
// read the altimeter
readHgtData();
// write to the state vector
stateStruct.position.z = -baroDataNew.hgt; // down position from blended accel data
terrainState = stateStruct.position.z + rngOnGnd;
for (uint8_t i=0; i<IMU_BUFFER_LENGTH; i++) {
storedOutput[i].position.z = stateStruct.position.z;
}
outputDataNew.position.z = stateStruct.position.z;
outputDataDelayed.position.z = stateStruct.position.z;
}
// Reset the baro so that it reads zero at the current height
// Reset the EKF height to zero
// Adjust the EKf origin height so that the EKF height + origin height is the same as before
// Return true if the height datum reset has been performed
// If using a range finder for height do not reset and return false
bool NavEKF2_core::resetHeightDatum(void)
{
// if we are using a range finder for height, return false
if (frontend._altSource == 1) {
return false;
}
// record the old height estimate
float oldHgt = -stateStruct.position.z;
// reset the barometer so that it reads zero at the current height
_baro.update_calibration();
// reset the height state
stateStruct.position.z = 0.0f;
// adjust the height of the EKF origin so that the origin plus baro height before and afer the reset is the same
if (validOrigin) {
EKF_origin.alt += oldHgt*100;
}
return true;
}
/********************************************************
* FUSE MEASURED_DATA *
********************************************************/
// select fusion of velocity, position and height measurements
void NavEKF2_core::SelectVelPosFusion()
{
// 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 && !posVelFusionDelayed) {
posVelFusionDelayed = true;
return;
} else {
posVelFusionDelayed = false;
}
// check for and read new GPS data
readGpsData();
// Determine if we need to fuse position and velocity data on this time step
if (RecallGPS() && PV_AidingMode != AID_RELATIVE) {
// Don't fuse velocity data if GPS doesn't support it
// If no aiding is avaialble, then we use zeroed GPS position and elocity data to constrain
// tilt errors assuming that the vehicle is not accelerating
if (frontend._fusionModeGPS <= 1 || PV_AidingMode == AID_NONE) {
fuseVelData = true;
} else {
fuseVelData = false;
}
fusePosData = true;
} else {
fuseVelData = false;
fusePosData = false;
}
// check for and read new height data
readHgtData();
// If we haven't received height data for a while, then declare the height data as being timed out
// set timeout period based on whether we have vertical GPS velocity available to constrain drift
hgtRetryTime_ms = (useGpsVertVel && !velTimeout) ? frontend.hgtRetryTimeMode0_ms : frontend.hgtRetryTimeMode12_ms;
if (imuSampleTime_ms - lastHgtReceived_ms > hgtRetryTime_ms) {
hgtTimeout = true;
}
// command fusion of height data
// wait until the EKF time horizon catches up with the measurement
if (RecallBaro()) {
// enable fusion
fuseHgtData = true;
}
// perform fusion
if (fuseVelData || fusePosData || fuseHgtData) {
// ensure that the covariance prediction is up to date before fusing data
if (!covPredStep) CovariancePrediction();
FuseVelPosNED();
}
}
// fuse selected position, velocity and height measurements
void NavEKF2_core::FuseVelPosNED()
{
// start performance timer
hal.util->perf_begin(_perf_FuseVelPosNED);
// health is set bad until test passed
velHealth = false;
posHealth = false;
hgtHealth = false;
// declare variables used to check measurement errors
Vector3f velInnov;
// declare variables used to control access to arrays
bool fuseData[6] = {false,false,false,false,false,false};
uint8_t stateIndex;
uint8_t obsIndex;
// declare variables used by state and covariance update calculations
float posErr;
Vector6 R_OBS; // Measurement variances used for fusion
Vector6 R_OBS_DATA_CHECKS; // Measurement variances used for data checks only
Vector6 observation;
float SK;
// perform sequential fusion of GPS measurements. This assumes that the
// errors in the different velocity and position components are
// uncorrelated which is not true, however in the absence of covariance
// data from the GPS receiver it is the only assumption we can make
// so we might as well take advantage of the computational efficiencies
// associated with sequential fusion
if (fuseVelData || fusePosData || fuseHgtData) {
// set the GPS data timeout depending on whether airspeed data is present
uint32_t gpsRetryTime;
if (useAirspeed()) gpsRetryTime = frontend.gpsRetryTimeUseTAS_ms;
else gpsRetryTime = frontend.gpsRetryTimeNoTAS_ms;
// form the observation vector and zero velocity and horizontal position observations if in constant position mode
// If in constant velocity mode, hold the last known horizontal velocity vector
if (PV_AidingMode == AID_ABSOLUTE) {
observation[0] = gpsDataDelayed.vel.x + gpsVelGlitchOffset.x;
observation[1] = gpsDataDelayed.vel.y + gpsVelGlitchOffset.y;
observation[2] = gpsDataDelayed.vel.z;
observation[3] = gpsDataDelayed.pos.x + gpsPosGlitchOffsetNE.x;
observation[4] = gpsDataDelayed.pos.y + gpsPosGlitchOffsetNE.y;
} else if (PV_AidingMode == AID_NONE) {
for (uint8_t i=0; i<=4; i++) observation[i] = 0.0f;
}
observation[5] = -baroDataDelayed.hgt;
// calculate additional error in GPS position caused by manoeuvring
posErr = frontend.gpsPosVarAccScale * accNavMag;
// estimate the GPS Velocity, GPS horiz position and height measurement variances.
// if the GPS is able to report a speed error, we use it to adjust the observation noise for GPS velocity
// otherwise we scale it using manoeuvre acceleration
if (gpsSpdAccuracy > 0.0f) {
// use GPS receivers reported speed accuracy - floor at value set by gps noise parameter
R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend._gpsHorizVelNoise, 50.0f));
R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend._gpsVertVelNoise, 50.0f));
} else {
// calculate additional error in GPS velocity caused by manoeuvring
R_OBS[0] = sq(constrain_float(frontend._gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend.gpsNEVelVarAccScale * accNavMag);
R_OBS[2] = sq(constrain_float(frontend._gpsVertVelNoise, 0.05f, 5.0f)) + sq(frontend.gpsDVelVarAccScale * accNavMag);
}
R_OBS[1] = R_OBS[0];
R_OBS[3] = sq(constrain_float(frontend._gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
R_OBS[4] = R_OBS[3];
R_OBS[5] = sq(constrain_float(frontend._baroAltNoise, 0.1f, 10.0f));
// reduce weighting (increase observation noise) on baro if we are likely to be in ground effect
if (getTakeoffExpected() || getTouchdownExpected()) {
R_OBS[5] *= frontend.gndEffectBaroScaler;
}
// For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
// For horizontal GPs velocity we don't want the acceptance radius to increase with reported GPS accuracy so we use a value based on best GPs perfomrance
// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
for (uint8_t i=0; i<=1; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend._gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend.gpsNEVelVarAccScale * accNavMag);
for (uint8_t i=2; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
// if vertical GPS velocity data is being used, check to see if the GPS vertical velocity and barometer
// innovations have the same sign and are outside limits. If so, then it is likely aliasing is affecting
// the accelerometers and we should disable the GPS and barometer innovation consistency checks.
if (useGpsVertVel && fuseVelData && (imuSampleTime_ms - lastHgtReceived_ms) < (2 * frontend.hgtAvg_ms)) {
// calculate innovations for height and vertical GPS vel measurements
float hgtErr = stateStruct.position.z - observation[5];
float velDErr = stateStruct.velocity.z - observation[2];
// check if they are the same sign and both more than 3-sigma out of bounds
if ((hgtErr*velDErr > 0.0f) && (sq(hgtErr) > 9.0f * (P[8][8] + R_OBS_DATA_CHECKS[5])) && (sq(velDErr) > 9.0f * (P[5][5] + R_OBS_DATA_CHECKS[2]))) {
badIMUdata = true;
} else {
badIMUdata = false;
}
}
// calculate innovations and check GPS data validity using an innovation consistency check
// test position measurements
if (fusePosData) {
// test horizontal position measurements
innovVelPos[3] = stateStruct.position.x - observation[3];
innovVelPos[4] = stateStruct.position.y - observation[4];
varInnovVelPos[3] = P[6][6] + R_OBS_DATA_CHECKS[3];
varInnovVelPos[4] = P[7][7] + R_OBS_DATA_CHECKS[4];
// apply an innovation consistency threshold test, but don't fail if bad IMU data
float maxPosInnov2 = sq(frontend._gpsPosInnovGate)*(varInnovVelPos[3] + varInnovVelPos[4]);
posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
posHealth = ((posTestRatio < 1.0f) || badIMUdata);
// declare a timeout condition if we have been too long without data or not aiding
posTimeout = (((imuSampleTime_ms - lastPosPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// use position data if healthy, timed out, or in constant position mode
if (posHealth || posTimeout || (PV_AidingMode == AID_NONE)) {
posHealth = true;
// only reset the failed time and do glitch timeout checks if we are doing full aiding
if (PV_AidingMode == AID_ABSOLUTE) {
lastPosPassTime_ms = imuSampleTime_ms;
// if timed out or outside the specified uncertainty radius, increment the offset applied to GPS data to compensate for large GPS position jumps
if (posTimeout || ((varInnovVelPos[3] + varInnovVelPos[4]) > sq(float(frontend._gpsGlitchRadiusMax)))) {
gpsPosGlitchOffsetNE.x += innovVelPos[3];
gpsPosGlitchOffsetNE.y += innovVelPos[4];
// limit the radius of the offset and decay the offset to zero radially
decayGpsOffset();
// reset the position to the current GPS position which will include the glitch correction offset
ResetPosition();
// reset the velocity to the GPS velocity
ResetVelocity();
// don't fuse data on this time step
fusePosData = false;
// Reset the normalised innovation to avoid false failing the bad position fusion test
posTestRatio = 0.0f;
velTestRatio = 0.0f;
}
}
} else {
posHealth = false;
}
}
// test velocity measurements
if (fuseVelData) {
// test velocity measurements
uint8_t imax = 2;
if (frontend._fusionModeGPS == 1) {
imax = 1;
}
float innovVelSumSq = 0; // sum of squares of velocity innovations
float varVelSum = 0; // sum of velocity innovation variances
for (uint8_t i = 0; i<=imax; i++) {
// velocity states start at index 3
stateIndex = i + 3;
// calculate innovations using blended and single IMU predicted states
velInnov[i] = stateStruct.velocity[i] - observation[i]; // blended
// calculate innovation variance
varInnovVelPos[i] = P[stateIndex][stateIndex] + R_OBS_DATA_CHECKS[i];
// sum the innovation and innovation variances
innovVelSumSq += sq(velInnov[i]);
varVelSum += varInnovVelPos[i];
}
// apply an innovation consistency threshold test, but don't fail if bad IMU data
// calculate the test ratio
velTestRatio = innovVelSumSq / (varVelSum * sq(frontend._gpsVelInnovGate));
// fail if the ratio is greater than 1
velHealth = ((velTestRatio < 1.0f) || badIMUdata);
// declare a timeout if we have not fused velocity data for too long or not aiding
velTimeout = (((imuSampleTime_ms - lastVelPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// if data is healthy or in constant velocity mode we fuse it
if (velHealth || velTimeout) {
velHealth = true;
// restart the timeout count
lastVelPassTime_ms = imuSampleTime_ms;
} else if (velTimeout && !posHealth && PV_AidingMode == AID_ABSOLUTE) {
// if data is not healthy and timed out and position is unhealthy and we are using aiding, we reset the velocity, but do not fuse data on this time step
ResetVelocity();
fuseVelData = false;
// Reset the normalised innovation to avoid false failing the bad position fusion test
velTestRatio = 0.0f;
} else {
// if data is unhealthy and position is healthy, we do not fuse it
velHealth = false;
}
}
// test height measurements
if (fuseHgtData) {
// calculate height innovations
innovVelPos[5] = stateStruct.position.z - observation[5];
varInnovVelPos[5] = P[8][8] + R_OBS_DATA_CHECKS[5];
// calculate the innovation consistency test ratio
hgtTestRatio = sq(innovVelPos[5]) / (sq(frontend._hgtInnovGate) * varInnovVelPos[5]);
// fail if the ratio is > 1, but don't fail if bad IMU data
hgtHealth = ((hgtTestRatio < 1.0f) || badIMUdata);
hgtTimeout = (imuSampleTime_ms - lastHgtPassTime_ms) > hgtRetryTime_ms;
// Fuse height data if healthy or timed out or in constant position mode
if (hgtHealth || hgtTimeout || (PV_AidingMode == AID_NONE)) {
hgtHealth = true;
lastHgtPassTime_ms = imuSampleTime_ms;
// if timed out, reset the height, but do not fuse data on this time step
if (hgtTimeout) {
ResetHeight();
fuseHgtData = false;
}
}
else {
hgtHealth = false;
}
}
// set range for sequential fusion of velocity and position measurements depending on which data is available and its health
if (fuseVelData && velHealth) {
fuseData[0] = true;
fuseData[1] = true;
if (useGpsVertVel) {
fuseData[2] = true;
}
tiltErrVec.zero();
}
if (fusePosData && posHealth) {
fuseData[3] = true;
fuseData[4] = true;
tiltErrVec.zero();
}
if (fuseHgtData && hgtHealth) {
fuseData[5] = true;
}
// fuse measurements sequentially
for (obsIndex=0; obsIndex<=5; obsIndex++) {
if (fuseData[obsIndex]) {
stateIndex = 3 + obsIndex;
// calculate the measurement innovation, using states from a different time coordinate if fusing height data
// adjust scaling on GPS measurement noise variances if not enough satellites
if (obsIndex <= 2)
{
innovVelPos[obsIndex] = stateStruct.velocity[obsIndex] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
}
else if (obsIndex == 3 || obsIndex == 4) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
} else {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
if (obsIndex == 5) {
const float gndMaxBaroErr = 4.0f;
const float gndBaroInnovFloor = -0.5f;
if(getTouchdownExpected()) {
// when a touchdown is expected, floor the barometer innovation at gndBaroInnovFloor
// constrain the correction between 0 and gndBaroInnovFloor+gndMaxBaroErr
// this function looks like this:
// |/
//---------|---------
// ____/|
// / |
// / |
innovVelPos[5] += constrain_float(-innovVelPos[5]+gndBaroInnovFloor, 0.0f, gndBaroInnovFloor+gndMaxBaroErr);
}
}
}
// calculate the Kalman gain and calculate innovation variances
varInnovVelPos[obsIndex] = P[stateIndex][stateIndex] + R_OBS[obsIndex];
SK = 1.0f/varInnovVelPos[obsIndex];
for (uint8_t i= 0; i<=15; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
// inhibit magnetic field state estimation by setting Kalman gains to zero
if (!inhibitMagStates) {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
} else {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = 0.0f;
}
}
// inhibit wind state estimation by setting Kalman gains to zero
if (!inhibitWindStates) {
Kfusion[22] = P[22][stateIndex]*SK;
Kfusion[23] = P[23][stateIndex]*SK;
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
// zero the attitude error state - by definition it is assumed to be zero before each observaton fusion
stateStruct.angErr.zero();
// calculate state corrections and re-normalise the quaternions for states predicted using the blended IMU data
// Don't apply corrections to Z bias state as this has been done already as part of the single IMU calculations
for (uint8_t i = 0; i<=stateIndexLim; i++) {
statesArray[i] = statesArray[i] - Kfusion[i] * innovVelPos[obsIndex];
}
// the first 3 states represent the angular misalignment vector. This is
// is used to correct the estimated quaternion
stateStruct.quat.rotate(stateStruct.angErr);
// sum the attitude error from velocity and position fusion only
// used as a metric for convergence monitoring
if (obsIndex != 5) {
tiltErrVec += stateStruct.angErr;
}
// update the covariance - take advantage of direct observation of a single state at index = stateIndex to reduce computations
// this is a numerically optimised implementation of standard equation P = (I - K*H)*P;
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++)
{
KHP[i][j] = Kfusion[i] * P[stateIndex][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];
}
}
}
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-condiioning.
ForceSymmetry();
ConstrainVariances();
// stop performance timer
hal.util->perf_end(_perf_FuseVelPosNED);
}
/********************************************************
* MISC FUNCTIONS *
********************************************************/
#endif // HAL_CPU_CLASS