Mirror/ardupilot
5
0
Fork 0
mirror of https://github.com/ArduPilot/ardupilot synced 2025-01-03 14:38:30 -04:00
Code Issues Packages Projects Releases Wiki Activity

AP_NavEKF2: Add error checking and isolation to pos vel fusion

Browse Source
This commit is contained in:
Paul Riseborough 2016-05-10 17:03:53 +10:00 committed by Andrew Tridgell
parent b1e9207c0f
commit 51a3df0319
1 changed files with 64 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

88
libraries/AP_NavEKF2/AP_NavEKF2_PosVelFusion.cpp
View File

@ -495,24 +495,6 @@ void NavEKF2_core::FuseVelPosNED()
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
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++) {
@ -521,19 +503,77 @@ void NavEKF2_core::FuseVelPosNED()
KHP[i][j] = Kfusion[i] * P[stateIndex][j];
}
}
// 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++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
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-condiioning.
ForceSymmetry();
ConstrainVariances();
// update the states
// 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
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;
}
// record good fusion status
if (obsIndex == 0) {
faultStatus.bad_nvel = false;
} else if (obsIndex == 1) {
faultStatus.bad_evel = false;
} else if (obsIndex == 2) {
faultStatus.bad_dvel = false;
} else if (obsIndex == 3) {
faultStatus.bad_npos = false;
} else if (obsIndex == 4) {
faultStatus.bad_epos = false;
} else if (obsIndex == 5) {
faultStatus.bad_dpos = false;
}
} else {
// record bad fusion status
if (obsIndex == 0) {
faultStatus.bad_nvel = true;
} else if (obsIndex == 1) {
faultStatus.bad_evel = true;
} else if (obsIndex == 2) {
faultStatus.bad_dvel = true;
} else if (obsIndex == 3) {
faultStatus.bad_npos = true;
} else if (obsIndex == 4) {
faultStatus.bad_epos = true;
} else if (obsIndex == 5) {
faultStatus.bad_dpos = true;
}
}
}
}
}
// 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);
}