mirror of https://github.com/ArduPilot/ardupilot
1613 lines
74 KiB
C++
1613 lines
74 KiB
C++
#include <AP_HAL/AP_HAL.h>
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#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
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#include "AP_NavEKF3.h"
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#include "AP_NavEKF3_core.h"
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#include <AP_AHRS/AP_AHRS.h>
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#include <AP_Vehicle/AP_Vehicle.h>
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#include <GCS_MAVLink/GCS.h>
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#include <AP_RangeFinder/RangeFinder_Backend.h>
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extern const AP_HAL::HAL& hal;
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/********************************************************
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* RESET FUNCTIONS *
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********************************************************/
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// Reset velocity states to last GPS measurement if available or to zero if in constant position mode or if PV aiding is not absolute
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// Do not reset vertical velocity using GPS as there is baro alt available to constrain drift
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void NavEKF3_core::ResetVelocity(void)
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{
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// Store the position before the reset so that we can record the reset delta
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velResetNE.x = stateStruct.velocity.x;
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velResetNE.y = stateStruct.velocity.y;
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// reset the corresponding covariances
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zeroRows(P,4,5);
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zeroCols(P,4,5);
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if (PV_AidingMode != AID_ABSOLUTE) {
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stateStruct.velocity.zero();
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// set the variances using the measurement noise parameter
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P[5][5] = P[4][4] = sq(frontend->_gpsHorizVelNoise);
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} else {
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// reset horizontal velocity states to the GPS velocity if available
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if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && velResetSource == DEFAULT) || velResetSource == GPS) {
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stateStruct.velocity.x = gpsDataNew.vel.x;
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stateStruct.velocity.y = gpsDataNew.vel.y;
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// set the variances using the reported GPS speed accuracy
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P[5][5] = P[4][4] = sq(MAX(frontend->_gpsHorizVelNoise,gpsSpdAccuracy));
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// clear the timeout flags and counters
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velTimeout = false;
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lastVelPassTime_ms = imuSampleTime_ms;
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} else {
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stateStruct.velocity.x = 0.0f;
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stateStruct.velocity.y = 0.0f;
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// set the variances using the likely speed range
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P[5][5] = P[4][4] = sq(25.0f);
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// clear the timeout flags and counters
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velTimeout = false;
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lastVelPassTime_ms = imuSampleTime_ms;
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}
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].velocity.x = stateStruct.velocity.x;
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storedOutput[i].velocity.y = stateStruct.velocity.y;
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}
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outputDataNew.velocity.x = stateStruct.velocity.x;
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outputDataNew.velocity.y = stateStruct.velocity.y;
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outputDataDelayed.velocity.x = stateStruct.velocity.x;
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outputDataDelayed.velocity.y = stateStruct.velocity.y;
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// Calculate the position jump due to the reset
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velResetNE.x = stateStruct.velocity.x - velResetNE.x;
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velResetNE.y = stateStruct.velocity.y - velResetNE.y;
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// store the time of the reset
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lastVelReset_ms = imuSampleTime_ms;
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// clear reset data source preference
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velResetSource = DEFAULT;
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}
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// resets position states to last GPS measurement or to zero if in constant position mode
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void NavEKF3_core::ResetPosition(void)
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{
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// Store the position before the reset so that we can record the reset delta
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posResetNE.x = stateStruct.position.x;
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posResetNE.y = stateStruct.position.y;
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// reset the corresponding covariances
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zeroRows(P,7,8);
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zeroCols(P,7,8);
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if (PV_AidingMode != AID_ABSOLUTE) {
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// reset all position state history to the last known position
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stateStruct.position.x = lastKnownPositionNE.x;
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stateStruct.position.y = lastKnownPositionNE.y;
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// set the variances using the position measurement noise parameter
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P[7][7] = P[8][8] = sq(frontend->_gpsHorizPosNoise);
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} else {
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// Use GPS data as first preference if fresh data is available
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if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && posResetSource == DEFAULT) || posResetSource == GPS) {
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// record the ID of the GPS for the data we are using for the reset
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last_gps_idx = gpsDataNew.sensor_idx;
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// write to state vector and compensate for offset between last GPS measurement and the EKF time horizon
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stateStruct.position.x = gpsDataNew.pos.x + 0.001f*gpsDataNew.vel.x*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
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stateStruct.position.y = gpsDataNew.pos.y + 0.001f*gpsDataNew.vel.y*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
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// set the variances using the position measurement noise parameter
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P[7][7] = P[8][8] = sq(MAX(gpsPosAccuracy,frontend->_gpsHorizPosNoise));
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// clear the timeout flags and counters
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posTimeout = false;
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lastPosPassTime_ms = imuSampleTime_ms;
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} else if ((imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250 && posResetSource == DEFAULT) || posResetSource == RNGBCN) {
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// use the range beacon data as a second preference
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stateStruct.position.x = receiverPos.x;
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stateStruct.position.y = receiverPos.y;
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// set the variances from the beacon alignment filter
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P[7][7] = receiverPosCov[0][0];
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P[8][8] = receiverPosCov[1][1];
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// clear the timeout flags and counters
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rngBcnTimeout = false;
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lastRngBcnPassTime_ms = imuSampleTime_ms;
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}
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.x = stateStruct.position.x;
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storedOutput[i].position.y = stateStruct.position.y;
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}
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outputDataNew.position.x = stateStruct.position.x;
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outputDataNew.position.y = stateStruct.position.y;
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outputDataDelayed.position.x = stateStruct.position.x;
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outputDataDelayed.position.y = stateStruct.position.y;
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// Calculate the position jump due to the reset
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posResetNE.x = stateStruct.position.x - posResetNE.x;
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posResetNE.y = stateStruct.position.y - posResetNE.y;
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// store the time of the reset
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lastPosReset_ms = imuSampleTime_ms;
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// clear reset source preference
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posResetSource = DEFAULT;
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}
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// reset the vertical position state using the last height measurement
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void NavEKF3_core::ResetHeight(void)
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{
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// Store the position before the reset so that we can record the reset delta
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posResetD = stateStruct.position.z;
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// write to the state vector
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stateStruct.position.z = -hgtMea;
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outputDataNew.position.z = stateStruct.position.z;
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outputDataDelayed.position.z = stateStruct.position.z;
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// reset the terrain state height
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if (onGround) {
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// assume vehicle is sitting on the ground
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terrainState = stateStruct.position.z + rngOnGnd;
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} else {
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// can make no assumption other than vehicle is not below ground level
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terrainState = MAX(stateStruct.position.z + rngOnGnd , terrainState);
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.z = stateStruct.position.z;
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}
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// Calculate the position jump due to the reset
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posResetD = stateStruct.position.z - posResetD;
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// store the time of the reset
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lastPosResetD_ms = imuSampleTime_ms;
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// clear the timeout flags and counters
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hgtTimeout = false;
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lastHgtPassTime_ms = imuSampleTime_ms;
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// reset the corresponding covariances
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zeroRows(P,9,9);
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zeroCols(P,9,9);
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// set the variances to the measurement variance
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P[9][9] = posDownObsNoise;
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// Reset the vertical velocity state using GPS vertical velocity if we are airborne
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// Check that GPS vertical velocity data is available and can be used
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if (inFlight && !gpsNotAvailable && frontend->_fusionModeGPS == 0 && !frontend->inhibitGpsVertVelUse) {
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stateStruct.velocity.z = gpsDataNew.vel.z;
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} else if (onGround) {
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stateStruct.velocity.z = 0.0f;
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].velocity.z = stateStruct.velocity.z;
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}
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outputDataNew.velocity.z = stateStruct.velocity.z;
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outputDataDelayed.velocity.z = stateStruct.velocity.z;
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// reset the corresponding covariances
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zeroRows(P,6,6);
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zeroCols(P,6,6);
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// set the variances to the measurement variance
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P[6][6] = sq(frontend->_gpsVertVelNoise);
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}
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// Zero the EKF height datum
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// Return true if the height datum reset has been performed
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bool NavEKF3_core::resetHeightDatum(void)
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{
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if (activeHgtSource == HGT_SOURCE_RNG) {
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// by definition the height datum is at ground level so cannot perform the reset
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return false;
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}
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// record the old height estimate
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float oldHgt = -stateStruct.position.z;
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// reset the barometer so that it reads zero at the current height
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frontend->_baro.update_calibration();
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// reset the height state
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stateStruct.position.z = 0.0f;
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// adjust the height of the EKF origin so that the origin plus baro height before and after the reset is the same
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if (validOrigin) {
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ekfGpsRefHgt += (double)oldHgt;
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}
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// adjust the terrain state
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terrainState += oldHgt;
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return true;
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}
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/********************************************************
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* FUSE MEASURED_DATA *
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********************************************************/
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// select fusion of velocity, position and height measurements
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void NavEKF3_core::SelectVelPosFusion()
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{
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// Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
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// If so, don't fuse measurements on this time step to reduce frame over-runs
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// Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
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if (magFusePerformed && dtIMUavg < 0.005f && !posVelFusionDelayed) {
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posVelFusionDelayed = true;
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return;
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} else {
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posVelFusionDelayed = false;
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}
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// read GPS data from the sensor and check for new data in the buffer
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readGpsData();
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gpsDataToFuse = storedGPS.recall(gpsDataDelayed,imuDataDelayed.time_ms);
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// Determine if we need to fuse position and velocity data on this time step
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if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE) {
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// correct GPS data for position offset of antenna phase centre relative to the IMU
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Vector3f posOffsetBody = _ahrs->get_gps().get_antenna_offset(gpsDataDelayed.sensor_idx) - accelPosOffset;
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if (!posOffsetBody.is_zero()) {
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if (fuseVelData) {
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// TODO use a filtered angular rate with a group delay that matches the GPS delay
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Vector3f angRate = imuDataDelayed.delAng * (1.0f/imuDataDelayed.delAngDT);
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Vector3f velOffsetBody = angRate % posOffsetBody;
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Vector3f velOffsetEarth = prevTnb.mul_transpose(velOffsetBody);
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gpsDataDelayed.vel -= velOffsetEarth;
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}
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Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
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gpsDataDelayed.pos.x -= posOffsetEarth.x;
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gpsDataDelayed.pos.y -= posOffsetEarth.y;
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gpsDataDelayed.hgt += posOffsetEarth.z;
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}
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// Don't fuse velocity data if GPS doesn't support it
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if (frontend->_fusionModeGPS <= 1) {
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fuseVelData = true;
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} else {
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fuseVelData = false;
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}
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fusePosData = true;
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} else {
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fuseVelData = false;
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fusePosData = false;
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}
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// we have GPS data to fuse and a request to align the yaw using the GPS course
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if (gpsYawResetRequest) {
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realignYawGPS();
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}
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// Select height data to be fused from the available baro, range finder and GPS sources
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selectHeightForFusion();
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// if we are using GPS, check for a change in receiver and reset position and height
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if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE && gpsDataDelayed.sensor_idx != last_gps_idx) {
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// record the ID of the GPS that we are using for the reset
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last_gps_idx = gpsDataDelayed.sensor_idx;
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// Store the position before the reset so that we can record the reset delta
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posResetNE.x = stateStruct.position.x;
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posResetNE.y = stateStruct.position.y;
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// Set the position states to the position from the new GPS
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stateStruct.position.x = gpsDataNew.pos.x;
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stateStruct.position.y = gpsDataNew.pos.y;
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// Calculate the position offset due to the reset
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posResetNE.x = stateStruct.position.x - posResetNE.x;
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posResetNE.y = stateStruct.position.y - posResetNE.y;
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// Add the offset to the output observer states
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.x += posResetNE.x;
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storedOutput[i].position.y += posResetNE.y;
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}
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outputDataNew.position.x += posResetNE.x;
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outputDataNew.position.y += posResetNE.y;
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outputDataDelayed.position.x += posResetNE.x;
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outputDataDelayed.position.y += posResetNE.y;
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// store the time of the reset
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lastPosReset_ms = imuSampleTime_ms;
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// If we are alseo using GPS as the height reference, reset the height
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if (activeHgtSource == HGT_SOURCE_GPS) {
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// Store the position before the reset so that we can record the reset delta
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posResetD = stateStruct.position.z;
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// write to the state vector
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stateStruct.position.z = -hgtMea;
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// Calculate the position jump due to the reset
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posResetD = stateStruct.position.z - posResetD;
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// Add the offset to the output observer states
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outputDataNew.position.z += posResetD;
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outputDataDelayed.position.z += posResetD;
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.z += posResetD;
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}
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// store the time of the reset
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lastPosResetD_ms = imuSampleTime_ms;
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}
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}
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// If we are operating without any aiding, fuse in the last known position
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// to constrain tilt drift. This assumes a non-manoeuvring vehicle
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// Do this to coincide with the height fusion
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if (fuseHgtData && PV_AidingMode == AID_NONE) {
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gpsDataDelayed.vel.zero();
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gpsDataDelayed.pos.x = lastKnownPositionNE.x;
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gpsDataDelayed.pos.y = lastKnownPositionNE.y;
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fusePosData = true;
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fuseVelData = false;
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}
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// perform fusion
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if (fuseVelData || fusePosData || fuseHgtData) {
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FuseVelPosNED();
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// clear the flags to prevent repeated fusion of the same data
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fuseVelData = false;
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fuseHgtData = false;
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fusePosData = false;
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}
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}
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// fuse selected position, velocity and height measurements
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void NavEKF3_core::FuseVelPosNED()
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{
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// start performance timer
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hal.util->perf_begin(_perf_FuseVelPosNED);
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// health is set bad until test passed
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velHealth = false;
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posHealth = false;
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hgtHealth = false;
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// declare variables used to check measurement errors
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Vector3f velInnov;
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// declare variables used to control access to arrays
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bool fuseData[6] = {false,false,false,false,false,false};
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uint8_t stateIndex;
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uint8_t obsIndex;
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// declare variables used by state and covariance update calculations
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Vector6 R_OBS; // Measurement variances used for fusion
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Vector6 R_OBS_DATA_CHECKS; // Measurement variances used for data checks only
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Vector6 observation;
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float SK;
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// perform sequential fusion of GPS measurements. This assumes that the
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// errors in the different velocity and position components are
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// uncorrelated which is not true, however in the absence of covariance
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// data from the GPS receiver it is the only assumption we can make
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// so we might as well take advantage of the computational efficiencies
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// associated with sequential fusion
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if (fuseVelData || fusePosData || fuseHgtData) {
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// form the observation vector
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observation[0] = gpsDataDelayed.vel.x;
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observation[1] = gpsDataDelayed.vel.y;
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observation[2] = gpsDataDelayed.vel.z;
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observation[3] = gpsDataDelayed.pos.x;
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observation[4] = gpsDataDelayed.pos.y;
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observation[5] = -hgtMea;
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// calculate additional error in GPS position caused by manoeuvring
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float posErr = frontend->gpsPosVarAccScale * accNavMag;
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// estimate the GPS Velocity, GPS horiz position and height measurement variances.
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// Use different errors if operating without external aiding using an assumed position or velocity of zero
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if (PV_AidingMode == AID_NONE) {
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if (tiltAlignComplete && motorsArmed) {
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// This is a compromise between corrections for gyro errors and reducing effect of manoeuvre accelerations on tilt estimate
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R_OBS[0] = sq(constrain_float(frontend->_noaidHorizNoise, 0.5f, 50.0f));
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} else {
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// Use a smaller value to give faster initial alignment
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R_OBS[0] = sq(0.5f);
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}
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R_OBS[1] = R_OBS[0];
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R_OBS[2] = R_OBS[0];
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R_OBS[3] = R_OBS[0];
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R_OBS[4] = R_OBS[0];
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for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
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} else {
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if (gpsSpdAccuracy > 0.0f) {
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// use GPS receivers reported speed accuracy if available and floor at value set by GPS velocity noise parameter
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R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
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R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
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} else {
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// calculate additional error in GPS velocity caused by manoeuvring
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R_OBS[0] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
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R_OBS[2] = sq(constrain_float(frontend->_gpsVertVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsDVelVarAccScale * accNavMag);
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}
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R_OBS[1] = R_OBS[0];
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// Use GPS reported position accuracy if available and floor at value set by GPS position noise parameter
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if (gpsPosAccuracy > 0.0f) {
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R_OBS[3] = sq(constrain_float(gpsPosAccuracy, frontend->_gpsHorizPosNoise, 100.0f));
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} else {
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R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
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}
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R_OBS[4] = R_OBS[3];
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// For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
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// 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
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// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
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for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
|
|
}
|
|
R_OBS[5] = posDownObsNoise;
|
|
for (uint8_t i=3; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
|
|
|
|
// if vertical GPS velocity data and an independent height source is being used, check to see if the GPS vertical velocity and altimeter
|
|
// 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 && (frontend->_altSource != 2)) {
|
|
// 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[9][9] + R_OBS_DATA_CHECKS[5])) && (sq(velDErr) > 9.0f * (P[6][6] + 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[7][7] + R_OBS_DATA_CHECKS[3];
|
|
varInnovVelPos[4] = P[8][8] + R_OBS_DATA_CHECKS[4];
|
|
// apply an innovation consistency threshold test, but don't fail if bad IMU data
|
|
float maxPosInnov2 = sq(MAX(0.01f * (float)frontend->_gpsPosInnovGate, 1.0f))*(varInnovVelPos[3] + varInnovVelPos[4]);
|
|
posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
|
|
posHealth = ((posTestRatio < 1.0f) || badIMUdata);
|
|
// use position data if healthy or timed out
|
|
if (PV_AidingMode == AID_NONE) {
|
|
posHealth = true;
|
|
lastPosPassTime_ms = imuSampleTime_ms;
|
|
} else if (posHealth || posTimeout) {
|
|
posHealth = true;
|
|
lastPosPassTime_ms = imuSampleTime_ms;
|
|
// if timed out or outside the specified uncertainty radius, reset to the GPS
|
|
if (posTimeout || ((P[8][8] + P[7][7]) > sq(float(frontend->_gpsGlitchRadiusMax)))) {
|
|
// reset the position to the current GPS position
|
|
ResetPosition();
|
|
// reset the velocity to the GPS velocity
|
|
ResetVelocity();
|
|
// don't fuse GPS data on this time step
|
|
fusePosData = false;
|
|
fuseVelData = false;
|
|
// Reset the position variances and corresponding covariances to a value that will pass the checks
|
|
zeroRows(P,7,8);
|
|
zeroCols(P,7,8);
|
|
P[7][7] = sq(float(0.5f*frontend->_gpsGlitchRadiusMax));
|
|
P[8][8] = P[7][7];
|
|
// Reset the normalised innovation to avoid failing the bad fusion tests
|
|
posTestRatio = 0.0f;
|
|
velTestRatio = 0.0f;
|
|
}
|
|
} else {
|
|
posHealth = false;
|
|
}
|
|
}
|
|
|
|
// test velocity measurements
|
|
if (fuseVelData) {
|
|
// test velocity measurements
|
|
uint8_t imax = 2;
|
|
// Don't fuse vertical velocity observations if inhibited by the user or if we are using synthetic data
|
|
if (frontend->_fusionModeGPS > 0 || PV_AidingMode != AID_ABSOLUTE || frontend->inhibitGpsVertVelUse) {
|
|
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 4
|
|
stateIndex = i + 4;
|
|
// 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(MAX(0.01f * (float)frontend->_gpsVelInnovGate, 1.0f)));
|
|
// fail if the ratio is greater than 1
|
|
velHealth = ((velTestRatio < 1.0f) || badIMUdata);
|
|
// use velocity data if healthy, timed out, or in constant position mode
|
|
if (velHealth || velTimeout) {
|
|
velHealth = true;
|
|
// restart the timeout count
|
|
lastVelPassTime_ms = imuSampleTime_ms;
|
|
// If we are doing full aiding and velocity fusion times out, reset to the GPS velocity
|
|
if (PV_AidingMode == AID_ABSOLUTE && velTimeout) {
|
|
// reset the velocity to the GPS velocity
|
|
ResetVelocity();
|
|
// don't fuse GPS velocity data on this time step
|
|
fuseVelData = false;
|
|
// Reset the normalised innovation to avoid failing the bad fusion tests
|
|
velTestRatio = 0.0f;
|
|
}
|
|
} else {
|
|
velHealth = false;
|
|
}
|
|
}
|
|
|
|
// test height measurements
|
|
if (fuseHgtData) {
|
|
// calculate height innovations
|
|
innovVelPos[5] = stateStruct.position.z - observation[5];
|
|
varInnovVelPos[5] = P[9][9] + R_OBS_DATA_CHECKS[5];
|
|
// calculate the innovation consistency test ratio
|
|
hgtTestRatio = sq(innovVelPos[5]) / (sq(MAX(0.01f * (float)frontend->_hgtInnovGate, 1.0f)) * varInnovVelPos[5]);
|
|
// fail if the ratio is > 1, but don't fail if bad IMU data
|
|
hgtHealth = ((hgtTestRatio < 1.0f) || badIMUdata);
|
|
// Fuse height data if healthy or timed out or in constant position mode
|
|
if (hgtHealth || hgtTimeout || (PV_AidingMode == AID_NONE && onGround)) {
|
|
// Calculate a filtered value to be used by pre-flight health checks
|
|
// We need to filter because wind gusts can generate significant baro noise and we want to be able to detect bias errors in the inertial solution
|
|
if (onGround) {
|
|
float dtBaro = (imuSampleTime_ms - lastHgtPassTime_ms)*1.0e-3f;
|
|
const float hgtInnovFiltTC = 2.0f;
|
|
float alpha = constrain_float(dtBaro/(dtBaro+hgtInnovFiltTC),0.0f,1.0f);
|
|
hgtInnovFiltState += (innovVelPos[5]-hgtInnovFiltState)*alpha;
|
|
} else {
|
|
hgtInnovFiltState = 0.0f;
|
|
}
|
|
|
|
// if timed out, reset the height
|
|
if (hgtTimeout) {
|
|
ResetHeight();
|
|
}
|
|
|
|
// If we have got this far then declare the height data as healthy and reset the timeout counter
|
|
hgtHealth = true;
|
|
lastHgtPassTime_ms = imuSampleTime_ms;
|
|
}
|
|
}
|
|
|
|
// 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;
|
|
}
|
|
}
|
|
if (fusePosData && posHealth) {
|
|
fuseData[3] = true;
|
|
fuseData[4] = true;
|
|
}
|
|
if (fuseHgtData && hgtHealth) {
|
|
fuseData[5] = true;
|
|
}
|
|
|
|
// fuse measurements sequentially
|
|
for (obsIndex=0; obsIndex<=5; obsIndex++) {
|
|
if (fuseData[obsIndex]) {
|
|
stateIndex = 4 + 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 if (obsIndex == 5) {
|
|
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
|
|
const float gndMaxBaroErr = 4.0f;
|
|
const float gndBaroInnovFloor = -0.5f;
|
|
|
|
if(getTouchdownExpected() && activeHgtSource == HGT_SOURCE_BARO) {
|
|
// 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<=9; i++) {
|
|
Kfusion[i] = P[i][stateIndex]*SK;
|
|
}
|
|
|
|
// inhibit delta angle bias state estmation by setting Kalman gains to zero
|
|
if (!inhibitDelAngBiasStates) {
|
|
for (uint8_t i = 10; i<=12; i++) {
|
|
Kfusion[i] = P[i][stateIndex]*SK;
|
|
}
|
|
} else {
|
|
// zero indexes 10 to 12 = 3*4 bytes
|
|
memset(&Kfusion[10], 0, 12);
|
|
}
|
|
|
|
// inhibit delta velocity bias state estmation by setting Kalman gains to zero
|
|
if (!inhibitDelVelBiasStates) {
|
|
for (uint8_t i = 13; i<=15; i++) {
|
|
Kfusion[i] = P[i][stateIndex]*SK;
|
|
}
|
|
} else {
|
|
// zero indexes 13 to 15 = 3*4 bytes
|
|
memset(&Kfusion[13], 0, 12);
|
|
}
|
|
|
|
// 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 {
|
|
// zero indexes 16 to 21 = 6*4 bytes
|
|
memset(&Kfusion[16], 0, 24);
|
|
}
|
|
|
|
// 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 {
|
|
// zero indexes 22 to 23 = 2*4 bytes
|
|
memset(&Kfusion[22], 0, 8);
|
|
}
|
|
|
|
// 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];
|
|
}
|
|
}
|
|
// 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-condiioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// update states and renormalise the quaternions
|
|
for (uint8_t i = 0; i<=stateIndexLim; i++) {
|
|
statesArray[i] = statesArray[i] - Kfusion[i] * innovVelPos[obsIndex];
|
|
}
|
|
stateStruct.quat.normalize();
|
|
|
|
// 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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// stop performance timer
|
|
hal.util->perf_end(_perf_FuseVelPosNED);
|
|
}
|
|
|
|
/********************************************************
|
|
* MISC FUNCTIONS *
|
|
********************************************************/
|
|
|
|
// select the height measurement to be fused from the available baro, range finder and GPS sources
|
|
void NavEKF3_core::selectHeightForFusion()
|
|
{
|
|
// Read range finder data and check for new data in the buffer
|
|
// This data is used by both height and optical flow fusion processing
|
|
readRangeFinder();
|
|
rangeDataToFuse = storedRange.recall(rangeDataDelayed,imuDataDelayed.time_ms);
|
|
|
|
// correct range data for the body frame position offset relative to the IMU
|
|
// the corrected reading is the reading that would have been taken if the sensor was
|
|
// co-located with the IMU
|
|
if (rangeDataToFuse) {
|
|
AP_RangeFinder_Backend *sensor = frontend->_rng.get_backend(rangeDataDelayed.sensor_idx);
|
|
if (sensor != nullptr) {
|
|
Vector3f posOffsetBody = sensor->get_pos_offset() - accelPosOffset;
|
|
if (!posOffsetBody.is_zero()) {
|
|
Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
|
|
rangeDataDelayed.rng += posOffsetEarth.z / prevTnb.c.z;
|
|
}
|
|
}
|
|
}
|
|
|
|
// read baro height data from the sensor and check for new data in the buffer
|
|
readBaroData();
|
|
baroDataToFuse = storedBaro.recall(baroDataDelayed, imuDataDelayed.time_ms);
|
|
|
|
// select height source
|
|
if (((frontend->_useRngSwHgt > 0) || (frontend->_altSource == 1)) && (imuSampleTime_ms - rngValidMeaTime_ms < 500)) {
|
|
if (frontend->_altSource == 1) {
|
|
// always use range finder
|
|
activeHgtSource = HGT_SOURCE_RNG;
|
|
} else {
|
|
// determine if we are above or below the height switch region
|
|
float rangeMaxUse = 1e-4f * (float)frontend->_rng.max_distance_cm_orient(ROTATION_PITCH_270) * (float)frontend->_useRngSwHgt;
|
|
bool aboveUpperSwHgt = (terrainState - stateStruct.position.z) > rangeMaxUse;
|
|
bool belowLowerSwHgt = (terrainState - stateStruct.position.z) < 0.7f * rangeMaxUse;
|
|
|
|
// If the terrain height is consistent and we are moving slowly, then it can be
|
|
// used as a height reference in combination with a range finder
|
|
// apply a hysteresis to the speed check to prevent rapid switching
|
|
bool dontTrustTerrain, trustTerrain;
|
|
if (filterStatus.flags.horiz_vel) {
|
|
// We can use the velocity estimate
|
|
float horizSpeed = norm(stateStruct.velocity.x, stateStruct.velocity.y);
|
|
dontTrustTerrain = (horizSpeed > frontend->_useRngSwSpd) || !terrainHgtStable;
|
|
float trust_spd_trigger = MAX((frontend->_useRngSwSpd - 1.0f),(frontend->_useRngSwSpd * 0.5f));
|
|
trustTerrain = (horizSpeed < trust_spd_trigger) && terrainHgtStable;
|
|
} else {
|
|
// We can't use the velocity estimate
|
|
dontTrustTerrain = !terrainHgtStable;
|
|
trustTerrain = terrainHgtStable;
|
|
}
|
|
|
|
/*
|
|
* Switch between range finder and primary height source using height above ground and speed thresholds with
|
|
* hysteresis to avoid rapid switching. Using range finder for height requires a consistent terrain height
|
|
* which cannot be assumed if the vehicle is moving horizontally.
|
|
*/
|
|
if ((aboveUpperSwHgt || dontTrustTerrain) && (activeHgtSource == HGT_SOURCE_RNG)) {
|
|
// cannot trust terrain or range finder so stop using range finder height
|
|
if (frontend->_altSource == 0) {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
} else if (frontend->_altSource == 2) {
|
|
activeHgtSource = HGT_SOURCE_GPS;
|
|
}
|
|
} else if (belowLowerSwHgt && trustTerrain && (activeHgtSource != HGT_SOURCE_RNG)) {
|
|
// reliable terrain and range finder so start using range finder height
|
|
activeHgtSource = HGT_SOURCE_RNG;
|
|
}
|
|
}
|
|
} else if ((frontend->_altSource == 2) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) < 500) && validOrigin && gpsAccuracyGood) {
|
|
activeHgtSource = HGT_SOURCE_GPS;
|
|
} else if ((frontend->_altSource == 3) && validOrigin && rngBcnGoodToAlign) {
|
|
activeHgtSource = HGT_SOURCE_BCN;
|
|
} else {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
}
|
|
|
|
// Use Baro alt as a fallback if we lose range finder or GPS
|
|
bool lostRngHgt = ((activeHgtSource == HGT_SOURCE_RNG) && ((imuSampleTime_ms - rngValidMeaTime_ms) > 500));
|
|
bool lostGpsHgt = ((activeHgtSource == HGT_SOURCE_GPS) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) > 2000));
|
|
if (lostRngHgt || lostGpsHgt) {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
}
|
|
|
|
// if there is new baro data to fuse, calculate filtered baro data required by other processes
|
|
if (baroDataToFuse) {
|
|
// calculate offset to baro data that enables us to switch to Baro height use during operation
|
|
if (activeHgtSource != HGT_SOURCE_BARO) {
|
|
calcFiltBaroOffset();
|
|
}
|
|
// filtered baro data used to provide a reference for takeoff
|
|
// it is is reset to last height measurement on disarming in performArmingChecks()
|
|
if (!getTakeoffExpected()) {
|
|
const float gndHgtFiltTC = 0.5f;
|
|
const float dtBaro = frontend->hgtAvg_ms*1.0e-3f;
|
|
float alpha = constrain_float(dtBaro / (dtBaro+gndHgtFiltTC),0.0f,1.0f);
|
|
meaHgtAtTakeOff += (baroDataDelayed.hgt-meaHgtAtTakeOff)*alpha;
|
|
}
|
|
}
|
|
|
|
// If we are not using GPS as the primary height sensor, correct EKF origin height so that
|
|
// combined local NED position height and origin height remains consistent with the GPS altitude
|
|
// This also enables the GPS height to be used as a backup height source
|
|
if (gpsDataToFuse &&
|
|
(((frontend->_originHgtMode & (1 << 0)) && (activeHgtSource == HGT_SOURCE_BARO)) ||
|
|
((frontend->_originHgtMode & (1 << 1)) && (activeHgtSource == HGT_SOURCE_RNG)))
|
|
) {
|
|
correctEkfOriginHeight();
|
|
}
|
|
|
|
// Select the height measurement source
|
|
if (rangeDataToFuse && (activeHgtSource == HGT_SOURCE_RNG)) {
|
|
// using range finder data
|
|
// correct for tilt using a flat earth model
|
|
if (prevTnb.c.z >= 0.7) {
|
|
// calculate height above ground
|
|
hgtMea = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd);
|
|
// correct for terrain position relative to datum
|
|
hgtMea -= terrainState;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise
|
|
posDownObsNoise = sq(constrain_float(frontend->_rngNoise, 0.1f, 10.0f));
|
|
// add uncertainty created by terrain gradient and vehicle tilt
|
|
posDownObsNoise += sq(rangeDataDelayed.rng * frontend->_terrGradMax) * MAX(0.0f , (1.0f - sq(prevTnb.c.z)));
|
|
} else {
|
|
// disable fusion if tilted too far
|
|
fuseHgtData = false;
|
|
}
|
|
} else if (gpsDataToFuse && (activeHgtSource == HGT_SOURCE_GPS)) {
|
|
// using GPS data
|
|
hgtMea = gpsDataDelayed.hgt;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise using receiver reported accuracy or the horizontal noise scaled for typical VDOP/HDOP ratio
|
|
if (gpsHgtAccuracy > 0.0f) {
|
|
posDownObsNoise = sq(constrain_float(gpsHgtAccuracy, 1.5f * frontend->_gpsHorizPosNoise, 100.0f));
|
|
} else {
|
|
posDownObsNoise = sq(constrain_float(1.5f * frontend->_gpsHorizPosNoise, 0.1f, 10.0f));
|
|
}
|
|
} else if (baroDataToFuse && (activeHgtSource == HGT_SOURCE_BARO)) {
|
|
// using Baro data
|
|
hgtMea = baroDataDelayed.hgt - baroHgtOffset;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise
|
|
posDownObsNoise = 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()) {
|
|
posDownObsNoise *= frontend->gndEffectBaroScaler;
|
|
}
|
|
// If we are in takeoff mode, the height measurement is limited to be no less than the measurement at start of takeoff
|
|
// This prevents negative baro disturbances due to copter downwash corrupting the EKF altitude during initial ascent
|
|
if (motorsArmed && getTakeoffExpected()) {
|
|
hgtMea = MAX(hgtMea, meaHgtAtTakeOff);
|
|
}
|
|
} else {
|
|
fuseHgtData = false;
|
|
}
|
|
|
|
// If we haven't fused 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 - lastHgtPassTime_ms > hgtRetryTime_ms) {
|
|
hgtTimeout = true;
|
|
} else {
|
|
hgtTimeout = false;
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Fuse body frame velocity measurements 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/PX4/ecl/blob/master/matlab/scripts/Inertial%20Nav%20EKF/GenerateNavFilterEquations.m
|
|
*/
|
|
void NavEKF3_core::FuseBodyVel()
|
|
{
|
|
Vector24 H_VEL;
|
|
Vector3f bodyVelPred;
|
|
|
|
// 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;
|
|
|
|
// Fuse X, Y and Z axis measurements sequentially assuming observation errors are uncorrelated
|
|
for (uint8_t obsIndex=0; obsIndex<=2; obsIndex++) {
|
|
|
|
// calculate relative velocity in sensor frame including the relative motion due to rotation
|
|
bodyVelPred = (prevTnb * stateStruct.velocity);
|
|
|
|
// correct sensor offset body frame position offset relative to IMU
|
|
Vector3f posOffsetBody = (*bodyOdmDataDelayed.body_offset) - accelPosOffset;
|
|
|
|
// correct prediction for relative motion due to rotation
|
|
// note - % operator overloaded for cross product
|
|
if (imuDataDelayed.delAngDT > 0.001f) {
|
|
bodyVelPred += (imuDataDelayed.delAng * (1.0f / imuDataDelayed.delAngDT)) % posOffsetBody;
|
|
}
|
|
|
|
// calculate observation jacobians and Kalman gains
|
|
if (obsIndex == 0) {
|
|
// calculate X axis observation Jacobian
|
|
H_VEL[0] = q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f;
|
|
H_VEL[1] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
|
|
H_VEL[2] = q0*vd*-2.0f+q1*ve*2.0f-q2*vn*2.0f;
|
|
H_VEL[3] = q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f;
|
|
H_VEL[4] = q0*q0+q1*q1-q2*q2-q3*q3;
|
|
H_VEL[5] = q0*q3*2.0f+q1*q2*2.0f;
|
|
H_VEL[6] = q0*q2*-2.0f+q1*q3*2.0f;
|
|
for (uint8_t index = 7; index < 24; index++) {
|
|
H_VEL[index] = 0.0f;
|
|
}
|
|
|
|
// calculate intermediate expressions for X axis Kalman gains
|
|
float R_VEL = sq(bodyOdmDataDelayed.velErr);
|
|
float t2 = q0*q3*2.0f;
|
|
float t3 = q1*q2*2.0f;
|
|
float t4 = t2+t3;
|
|
float t5 = q0*q0;
|
|
float t6 = q1*q1;
|
|
float t7 = q2*q2;
|
|
float t8 = q3*q3;
|
|
float t9 = t5+t6-t7-t8;
|
|
float t10 = q0*q2*2.0f;
|
|
float t25 = q1*q3*2.0f;
|
|
float t11 = t10-t25;
|
|
float t12 = q3*ve*2.0f;
|
|
float t13 = q0*vn*2.0f;
|
|
float t26 = q2*vd*2.0f;
|
|
float t14 = t12+t13-t26;
|
|
float t15 = q3*vd*2.0f;
|
|
float t16 = q2*ve*2.0f;
|
|
float t17 = q1*vn*2.0f;
|
|
float t18 = t15+t16+t17;
|
|
float t19 = q0*vd*2.0f;
|
|
float t20 = q2*vn*2.0f;
|
|
float t27 = q1*ve*2.0f;
|
|
float t21 = t19+t20-t27;
|
|
float t22 = q1*vd*2.0f;
|
|
float t23 = q0*ve*2.0f;
|
|
float t28 = q3*vn*2.0f;
|
|
float t24 = t22+t23-t28;
|
|
float t29 = P[0][0]*t14;
|
|
float t30 = P[1][1]*t18;
|
|
float t31 = P[4][5]*t9;
|
|
float t32 = P[5][5]*t4;
|
|
float t33 = P[0][5]*t14;
|
|
float t34 = P[1][5]*t18;
|
|
float t35 = P[3][5]*t24;
|
|
float t79 = P[6][5]*t11;
|
|
float t80 = P[2][5]*t21;
|
|
float t36 = t31+t32+t33+t34+t35-t79-t80;
|
|
float t37 = t4*t36;
|
|
float t38 = P[4][6]*t9;
|
|
float t39 = P[5][6]*t4;
|
|
float t40 = P[0][6]*t14;
|
|
float t41 = P[1][6]*t18;
|
|
float t42 = P[3][6]*t24;
|
|
float t81 = P[6][6]*t11;
|
|
float t82 = P[2][6]*t21;
|
|
float t43 = t38+t39+t40+t41+t42-t81-t82;
|
|
float t44 = P[4][0]*t9;
|
|
float t45 = P[5][0]*t4;
|
|
float t46 = P[1][0]*t18;
|
|
float t47 = P[3][0]*t24;
|
|
float t84 = P[6][0]*t11;
|
|
float t85 = P[2][0]*t21;
|
|
float t48 = t29+t44+t45+t46+t47-t84-t85;
|
|
float t49 = t14*t48;
|
|
float t50 = P[4][1]*t9;
|
|
float t51 = P[5][1]*t4;
|
|
float t52 = P[0][1]*t14;
|
|
float t53 = P[3][1]*t24;
|
|
float t86 = P[6][1]*t11;
|
|
float t87 = P[2][1]*t21;
|
|
float t54 = t30+t50+t51+t52+t53-t86-t87;
|
|
float t55 = t18*t54;
|
|
float t56 = P[4][2]*t9;
|
|
float t57 = P[5][2]*t4;
|
|
float t58 = P[0][2]*t14;
|
|
float t59 = P[1][2]*t18;
|
|
float t60 = P[3][2]*t24;
|
|
float t78 = P[2][2]*t21;
|
|
float t88 = P[6][2]*t11;
|
|
float t61 = t56+t57+t58+t59+t60-t78-t88;
|
|
float t62 = P[4][3]*t9;
|
|
float t63 = P[5][3]*t4;
|
|
float t64 = P[0][3]*t14;
|
|
float t65 = P[1][3]*t18;
|
|
float t66 = P[3][3]*t24;
|
|
float t90 = P[6][3]*t11;
|
|
float t91 = P[2][3]*t21;
|
|
float t67 = t62+t63+t64+t65+t66-t90-t91;
|
|
float t68 = t24*t67;
|
|
float t69 = P[4][4]*t9;
|
|
float t70 = P[5][4]*t4;
|
|
float t71 = P[0][4]*t14;
|
|
float t72 = P[1][4]*t18;
|
|
float t73 = P[3][4]*t24;
|
|
float t92 = P[6][4]*t11;
|
|
float t93 = P[2][4]*t21;
|
|
float t74 = t69+t70+t71+t72+t73-t92-t93;
|
|
float t75 = t9*t74;
|
|
float t83 = t11*t43;
|
|
float t89 = t21*t61;
|
|
float t76 = R_VEL+t37+t49+t55+t68+t75-t83-t89;
|
|
float t77;
|
|
|
|
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
|
|
if (t76 > R_VEL) {
|
|
t77 = 1.0f/t76;
|
|
faultStatus.bad_xvel = false;
|
|
} else {
|
|
t76 = R_VEL;
|
|
t77 = 1.0f/R_VEL;
|
|
faultStatus.bad_xvel = true;
|
|
return;
|
|
}
|
|
varInnovBodyVel[0] = t77;
|
|
|
|
// calculate innovation for X axis observation
|
|
innovBodyVel[0] = bodyVelPred.x - bodyOdmDataDelayed.vel.x;
|
|
|
|
// calculate Kalman gains for X-axis observation
|
|
Kfusion[0] = t77*(t29+P[0][5]*t4+P[0][4]*t9-P[0][6]*t11+P[0][1]*t18-P[0][2]*t21+P[0][3]*t24);
|
|
Kfusion[1] = t77*(t30+P[1][5]*t4+P[1][4]*t9+P[1][0]*t14-P[1][6]*t11-P[1][2]*t21+P[1][3]*t24);
|
|
Kfusion[2] = t77*(-t78+P[2][5]*t4+P[2][4]*t9+P[2][0]*t14-P[2][6]*t11+P[2][1]*t18+P[2][3]*t24);
|
|
Kfusion[3] = t77*(t66+P[3][5]*t4+P[3][4]*t9+P[3][0]*t14-P[3][6]*t11+P[3][1]*t18-P[3][2]*t21);
|
|
Kfusion[4] = t77*(t69+P[4][5]*t4+P[4][0]*t14-P[4][6]*t11+P[4][1]*t18-P[4][2]*t21+P[4][3]*t24);
|
|
Kfusion[5] = t77*(t32+P[5][4]*t9+P[5][0]*t14-P[5][6]*t11+P[5][1]*t18-P[5][2]*t21+P[5][3]*t24);
|
|
Kfusion[6] = t77*(-t81+P[6][5]*t4+P[6][4]*t9+P[6][0]*t14+P[6][1]*t18-P[6][2]*t21+P[6][3]*t24);
|
|
Kfusion[7] = t77*(P[7][5]*t4+P[7][4]*t9+P[7][0]*t14-P[7][6]*t11+P[7][1]*t18-P[7][2]*t21+P[7][3]*t24);
|
|
Kfusion[8] = t77*(P[8][5]*t4+P[8][4]*t9+P[8][0]*t14-P[8][6]*t11+P[8][1]*t18-P[8][2]*t21+P[8][3]*t24);
|
|
Kfusion[9] = t77*(P[9][5]*t4+P[9][4]*t9+P[9][0]*t14-P[9][6]*t11+P[9][1]*t18-P[9][2]*t21+P[9][3]*t24);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = t77*(P[10][5]*t4+P[10][4]*t9+P[10][0]*t14-P[10][6]*t11+P[10][1]*t18-P[10][2]*t21+P[10][3]*t24);
|
|
Kfusion[11] = t77*(P[11][5]*t4+P[11][4]*t9+P[11][0]*t14-P[11][6]*t11+P[11][1]*t18-P[11][2]*t21+P[11][3]*t24);
|
|
Kfusion[12] = t77*(P[12][5]*t4+P[12][4]*t9+P[12][0]*t14-P[12][6]*t11+P[12][1]*t18-P[12][2]*t21+P[12][3]*t24);
|
|
} else {
|
|
// zero indexes 10 to 12 = 3*4 bytes
|
|
memset(&Kfusion[10], 0, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
Kfusion[13] = t77*(P[13][5]*t4+P[13][4]*t9+P[13][0]*t14-P[13][6]*t11+P[13][1]*t18-P[13][2]*t21+P[13][3]*t24);
|
|
Kfusion[14] = t77*(P[14][5]*t4+P[14][4]*t9+P[14][0]*t14-P[14][6]*t11+P[14][1]*t18-P[14][2]*t21+P[14][3]*t24);
|
|
Kfusion[15] = t77*(P[15][5]*t4+P[15][4]*t9+P[15][0]*t14-P[15][6]*t11+P[15][1]*t18-P[15][2]*t21+P[15][3]*t24);
|
|
} else {
|
|
// zero indexes 13 to 15 = 3*4 bytes
|
|
memset(&Kfusion[13], 0, 12);
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = t77*(P[16][5]*t4+P[16][4]*t9+P[16][0]*t14-P[16][6]*t11+P[16][1]*t18-P[16][2]*t21+P[16][3]*t24);
|
|
Kfusion[17] = t77*(P[17][5]*t4+P[17][4]*t9+P[17][0]*t14-P[17][6]*t11+P[17][1]*t18-P[17][2]*t21+P[17][3]*t24);
|
|
Kfusion[18] = t77*(P[18][5]*t4+P[18][4]*t9+P[18][0]*t14-P[18][6]*t11+P[18][1]*t18-P[18][2]*t21+P[18][3]*t24);
|
|
Kfusion[19] = t77*(P[19][5]*t4+P[19][4]*t9+P[19][0]*t14-P[19][6]*t11+P[19][1]*t18-P[19][2]*t21+P[19][3]*t24);
|
|
Kfusion[20] = t77*(P[20][5]*t4+P[20][4]*t9+P[20][0]*t14-P[20][6]*t11+P[20][1]*t18-P[20][2]*t21+P[20][3]*t24);
|
|
Kfusion[21] = t77*(P[21][5]*t4+P[21][4]*t9+P[21][0]*t14-P[21][6]*t11+P[21][1]*t18-P[21][2]*t21+P[21][3]*t24);
|
|
} else {
|
|
// zero indexes 16 to 21 = 6*4 bytes
|
|
memset(&Kfusion[16], 0, 24);
|
|
}
|
|
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = t77*(P[22][5]*t4+P[22][4]*t9+P[22][0]*t14-P[22][6]*t11+P[22][1]*t18-P[22][2]*t21+P[22][3]*t24);
|
|
Kfusion[23] = t77*(P[23][5]*t4+P[23][4]*t9+P[23][0]*t14-P[23][6]*t11+P[23][1]*t18-P[23][2]*t21+P[23][3]*t24);
|
|
} else {
|
|
// zero indexes 22 to 23 = 2*4 bytes
|
|
memset(&Kfusion[22], 0, 8);
|
|
}
|
|
} else if (obsIndex == 1) {
|
|
// calculate Y axis observation Jacobian
|
|
H_VEL[0] = q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f;
|
|
H_VEL[1] = q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f;
|
|
H_VEL[2] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
|
|
H_VEL[3] = q2*vd*2.0f-q3*ve*2.0f-q0*vn*2.0f;
|
|
H_VEL[4] = q0*q3*-2.0f+q1*q2*2.0f;
|
|
H_VEL[5] = q0*q0-q1*q1+q2*q2-q3*q3;
|
|
H_VEL[6] = q0*q1*2.0f+q2*q3*2.0f;
|
|
for (uint8_t index = 7; index < 24; index++) {
|
|
H_VEL[index] = 0.0f;
|
|
}
|
|
|
|
// calculate intermediate expressions for Y axis Kalman gains
|
|
float R_VEL = sq(bodyOdmDataDelayed.velErr);
|
|
float t2 = q0*q3*2.0f;
|
|
float t9 = q1*q2*2.0f;
|
|
float t3 = t2-t9;
|
|
float t4 = q0*q0;
|
|
float t5 = q1*q1;
|
|
float t6 = q2*q2;
|
|
float t7 = q3*q3;
|
|
float t8 = t4-t5+t6-t7;
|
|
float t10 = q0*q1*2.0f;
|
|
float t11 = q2*q3*2.0f;
|
|
float t12 = t10+t11;
|
|
float t13 = q1*vd*2.0f;
|
|
float t14 = q0*ve*2.0f;
|
|
float t26 = q3*vn*2.0f;
|
|
float t15 = t13+t14-t26;
|
|
float t16 = q0*vd*2.0f;
|
|
float t17 = q2*vn*2.0f;
|
|
float t27 = q1*ve*2.0f;
|
|
float t18 = t16+t17-t27;
|
|
float t19 = q3*vd*2.0f;
|
|
float t20 = q2*ve*2.0f;
|
|
float t21 = q1*vn*2.0f;
|
|
float t22 = t19+t20+t21;
|
|
float t23 = q3*ve*2.0f;
|
|
float t24 = q0*vn*2.0f;
|
|
float t28 = q2*vd*2.0f;
|
|
float t25 = t23+t24-t28;
|
|
float t29 = P[0][0]*t15;
|
|
float t30 = P[1][1]*t18;
|
|
float t31 = P[5][4]*t8;
|
|
float t32 = P[6][4]*t12;
|
|
float t33 = P[0][4]*t15;
|
|
float t34 = P[1][4]*t18;
|
|
float t35 = P[2][4]*t22;
|
|
float t78 = P[4][4]*t3;
|
|
float t79 = P[3][4]*t25;
|
|
float t36 = t31+t32+t33+t34+t35-t78-t79;
|
|
float t37 = P[5][6]*t8;
|
|
float t38 = P[6][6]*t12;
|
|
float t39 = P[0][6]*t15;
|
|
float t40 = P[1][6]*t18;
|
|
float t41 = P[2][6]*t22;
|
|
float t81 = P[4][6]*t3;
|
|
float t82 = P[3][6]*t25;
|
|
float t42 = t37+t38+t39+t40+t41-t81-t82;
|
|
float t43 = t12*t42;
|
|
float t44 = P[5][0]*t8;
|
|
float t45 = P[6][0]*t12;
|
|
float t46 = P[1][0]*t18;
|
|
float t47 = P[2][0]*t22;
|
|
float t83 = P[4][0]*t3;
|
|
float t84 = P[3][0]*t25;
|
|
float t48 = t29+t44+t45+t46+t47-t83-t84;
|
|
float t49 = t15*t48;
|
|
float t50 = P[5][1]*t8;
|
|
float t51 = P[6][1]*t12;
|
|
float t52 = P[0][1]*t15;
|
|
float t53 = P[2][1]*t22;
|
|
float t85 = P[4][1]*t3;
|
|
float t86 = P[3][1]*t25;
|
|
float t54 = t30+t50+t51+t52+t53-t85-t86;
|
|
float t55 = t18*t54;
|
|
float t56 = P[5][2]*t8;
|
|
float t57 = P[6][2]*t12;
|
|
float t58 = P[0][2]*t15;
|
|
float t59 = P[1][2]*t18;
|
|
float t60 = P[2][2]*t22;
|
|
float t87 = P[4][2]*t3;
|
|
float t88 = P[3][2]*t25;
|
|
float t61 = t56+t57+t58+t59+t60-t87-t88;
|
|
float t62 = t22*t61;
|
|
float t63 = P[5][3]*t8;
|
|
float t64 = P[6][3]*t12;
|
|
float t65 = P[0][3]*t15;
|
|
float t66 = P[1][3]*t18;
|
|
float t67 = P[2][3]*t22;
|
|
float t89 = P[4][3]*t3;
|
|
float t90 = P[3][3]*t25;
|
|
float t68 = t63+t64+t65+t66+t67-t89-t90;
|
|
float t69 = P[5][5]*t8;
|
|
float t70 = P[6][5]*t12;
|
|
float t71 = P[0][5]*t15;
|
|
float t72 = P[1][5]*t18;
|
|
float t73 = P[2][5]*t22;
|
|
float t92 = P[4][5]*t3;
|
|
float t93 = P[3][5]*t25;
|
|
float t74 = t69+t70+t71+t72+t73-t92-t93;
|
|
float t75 = t8*t74;
|
|
float t80 = t3*t36;
|
|
float t91 = t25*t68;
|
|
float t76 = R_VEL+t43+t49+t55+t62+t75-t80-t91;
|
|
float t77;
|
|
|
|
// calculate innovation variance for Y axis observation and protect against a badly conditioned calculation
|
|
if (t76 > R_VEL) {
|
|
t77 = 1.0f/t76;
|
|
faultStatus.bad_yvel = false;
|
|
} else {
|
|
t76 = R_VEL;
|
|
t77 = 1.0f/R_VEL;
|
|
faultStatus.bad_yvel = true;
|
|
return;
|
|
}
|
|
varInnovBodyVel[1] = t77;
|
|
|
|
// calculate innovation for Y axis observation
|
|
innovBodyVel[1] = bodyVelPred.y - bodyOdmDataDelayed.vel.y;
|
|
|
|
// calculate Kalman gains for Y-axis observation
|
|
Kfusion[0] = t77*(t29-P[0][4]*t3+P[0][5]*t8+P[0][6]*t12+P[0][1]*t18+P[0][2]*t22-P[0][3]*t25);
|
|
Kfusion[1] = t77*(t30-P[1][4]*t3+P[1][5]*t8+P[1][0]*t15+P[1][6]*t12+P[1][2]*t22-P[1][3]*t25);
|
|
Kfusion[2] = t77*(t60-P[2][4]*t3+P[2][5]*t8+P[2][0]*t15+P[2][6]*t12+P[2][1]*t18-P[2][3]*t25);
|
|
Kfusion[3] = t77*(-t90-P[3][4]*t3+P[3][5]*t8+P[3][0]*t15+P[3][6]*t12+P[3][1]*t18+P[3][2]*t22);
|
|
Kfusion[4] = t77*(-t78+P[4][5]*t8+P[4][0]*t15+P[4][6]*t12+P[4][1]*t18+P[4][2]*t22-P[4][3]*t25);
|
|
Kfusion[5] = t77*(t69-P[5][4]*t3+P[5][0]*t15+P[5][6]*t12+P[5][1]*t18+P[5][2]*t22-P[5][3]*t25);
|
|
Kfusion[6] = t77*(t38-P[6][4]*t3+P[6][5]*t8+P[6][0]*t15+P[6][1]*t18+P[6][2]*t22-P[6][3]*t25);
|
|
Kfusion[7] = t77*(-P[7][4]*t3+P[7][5]*t8+P[7][0]*t15+P[7][6]*t12+P[7][1]*t18+P[7][2]*t22-P[7][3]*t25);
|
|
Kfusion[8] = t77*(-P[8][4]*t3+P[8][5]*t8+P[8][0]*t15+P[8][6]*t12+P[8][1]*t18+P[8][2]*t22-P[8][3]*t25);
|
|
Kfusion[9] = t77*(-P[9][4]*t3+P[9][5]*t8+P[9][0]*t15+P[9][6]*t12+P[9][1]*t18+P[9][2]*t22-P[9][3]*t25);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = t77*(-P[10][4]*t3+P[10][5]*t8+P[10][0]*t15+P[10][6]*t12+P[10][1]*t18+P[10][2]*t22-P[10][3]*t25);
|
|
Kfusion[11] = t77*(-P[11][4]*t3+P[11][5]*t8+P[11][0]*t15+P[11][6]*t12+P[11][1]*t18+P[11][2]*t22-P[11][3]*t25);
|
|
Kfusion[12] = t77*(-P[12][4]*t3+P[12][5]*t8+P[12][0]*t15+P[12][6]*t12+P[12][1]*t18+P[12][2]*t22-P[12][3]*t25);
|
|
} else {
|
|
// zero indexes 10 to 12 = 3*4 bytes
|
|
memset(&Kfusion[10], 0, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
Kfusion[13] = t77*(-P[13][4]*t3+P[13][5]*t8+P[13][0]*t15+P[13][6]*t12+P[13][1]*t18+P[13][2]*t22-P[13][3]*t25);
|
|
Kfusion[14] = t77*(-P[14][4]*t3+P[14][5]*t8+P[14][0]*t15+P[14][6]*t12+P[14][1]*t18+P[14][2]*t22-P[14][3]*t25);
|
|
Kfusion[15] = t77*(-P[15][4]*t3+P[15][5]*t8+P[15][0]*t15+P[15][6]*t12+P[15][1]*t18+P[15][2]*t22-P[15][3]*t25);
|
|
} else {
|
|
// zero indexes 13 to 15 = 3*4 bytes
|
|
memset(&Kfusion[13], 0, 12);
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = t77*(-P[16][4]*t3+P[16][5]*t8+P[16][0]*t15+P[16][6]*t12+P[16][1]*t18+P[16][2]*t22-P[16][3]*t25);
|
|
Kfusion[17] = t77*(-P[17][4]*t3+P[17][5]*t8+P[17][0]*t15+P[17][6]*t12+P[17][1]*t18+P[17][2]*t22-P[17][3]*t25);
|
|
Kfusion[18] = t77*(-P[18][4]*t3+P[18][5]*t8+P[18][0]*t15+P[18][6]*t12+P[18][1]*t18+P[18][2]*t22-P[18][3]*t25);
|
|
Kfusion[19] = t77*(-P[19][4]*t3+P[19][5]*t8+P[19][0]*t15+P[19][6]*t12+P[19][1]*t18+P[19][2]*t22-P[19][3]*t25);
|
|
Kfusion[20] = t77*(-P[20][4]*t3+P[20][5]*t8+P[20][0]*t15+P[20][6]*t12+P[20][1]*t18+P[20][2]*t22-P[20][3]*t25);
|
|
Kfusion[21] = t77*(-P[21][4]*t3+P[21][5]*t8+P[21][0]*t15+P[21][6]*t12+P[21][1]*t18+P[21][2]*t22-P[21][3]*t25);
|
|
} else {
|
|
// zero indexes 16 to 21 = 6*4 bytes
|
|
memset(&Kfusion[16], 0, 24);
|
|
}
|
|
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = t77*(-P[22][4]*t3+P[22][5]*t8+P[22][0]*t15+P[22][6]*t12+P[22][1]*t18+P[22][2]*t22-P[22][3]*t25);
|
|
Kfusion[23] = t77*(-P[23][4]*t3+P[23][5]*t8+P[23][0]*t15+P[23][6]*t12+P[23][1]*t18+P[23][2]*t22-P[23][3]*t25);
|
|
} else {
|
|
// zero indexes 22 to 23 = 2*4 bytes
|
|
memset(&Kfusion[22], 0, 8);
|
|
}
|
|
} else if (obsIndex == 2) {
|
|
// calculate Z axis observation Jacobian
|
|
H_VEL[0] = q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f;
|
|
H_VEL[1] = q1*vd*-2.0f-q0*ve*2.0f+q3*vn*2.0f;
|
|
H_VEL[2] = q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f;
|
|
H_VEL[3] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
|
|
H_VEL[4] = q0*q2*2.0f+q1*q3*2.0f;
|
|
H_VEL[5] = q0*q1*-2.0f+q2*q3*2.0f;
|
|
H_VEL[6] = q0*q0-q1*q1-q2*q2+q3*q3;
|
|
for (uint8_t index = 7; index < 24; index++) {
|
|
H_VEL[index] = 0.0f;
|
|
}
|
|
|
|
// calculate intermediate expressions for Z axis Kalman gains
|
|
float R_VEL = sq(bodyOdmDataDelayed.velErr);
|
|
float t2 = q0*q2*2.0f;
|
|
float t3 = q1*q3*2.0f;
|
|
float t4 = t2+t3;
|
|
float t5 = q0*q0;
|
|
float t6 = q1*q1;
|
|
float t7 = q2*q2;
|
|
float t8 = q3*q3;
|
|
float t9 = t5-t6-t7+t8;
|
|
float t10 = q0*q1*2.0f;
|
|
float t25 = q2*q3*2.0f;
|
|
float t11 = t10-t25;
|
|
float t12 = q0*vd*2.0f;
|
|
float t13 = q2*vn*2.0f;
|
|
float t26 = q1*ve*2.0f;
|
|
float t14 = t12+t13-t26;
|
|
float t15 = q1*vd*2.0f;
|
|
float t16 = q0*ve*2.0f;
|
|
float t27 = q3*vn*2.0f;
|
|
float t17 = t15+t16-t27;
|
|
float t18 = q3*ve*2.0f;
|
|
float t19 = q0*vn*2.0f;
|
|
float t28 = q2*vd*2.0f;
|
|
float t20 = t18+t19-t28;
|
|
float t21 = q3*vd*2.0f;
|
|
float t22 = q2*ve*2.0f;
|
|
float t23 = q1*vn*2.0f;
|
|
float t24 = t21+t22+t23;
|
|
float t29 = P[0][0]*t14;
|
|
float t30 = P[6][4]*t9;
|
|
float t31 = P[4][4]*t4;
|
|
float t32 = P[0][4]*t14;
|
|
float t33 = P[2][4]*t20;
|
|
float t34 = P[3][4]*t24;
|
|
float t78 = P[5][4]*t11;
|
|
float t79 = P[1][4]*t17;
|
|
float t35 = t30+t31+t32+t33+t34-t78-t79;
|
|
float t36 = t4*t35;
|
|
float t37 = P[6][5]*t9;
|
|
float t38 = P[4][5]*t4;
|
|
float t39 = P[0][5]*t14;
|
|
float t40 = P[2][5]*t20;
|
|
float t41 = P[3][5]*t24;
|
|
float t80 = P[5][5]*t11;
|
|
float t81 = P[1][5]*t17;
|
|
float t42 = t37+t38+t39+t40+t41-t80-t81;
|
|
float t43 = P[6][0]*t9;
|
|
float t44 = P[4][0]*t4;
|
|
float t45 = P[2][0]*t20;
|
|
float t46 = P[3][0]*t24;
|
|
float t83 = P[5][0]*t11;
|
|
float t84 = P[1][0]*t17;
|
|
float t47 = t29+t43+t44+t45+t46-t83-t84;
|
|
float t48 = t14*t47;
|
|
float t49 = P[6][1]*t9;
|
|
float t50 = P[4][1]*t4;
|
|
float t51 = P[0][1]*t14;
|
|
float t52 = P[2][1]*t20;
|
|
float t53 = P[3][1]*t24;
|
|
float t85 = P[5][1]*t11;
|
|
float t86 = P[1][1]*t17;
|
|
float t54 = t49+t50+t51+t52+t53-t85-t86;
|
|
float t55 = P[6][2]*t9;
|
|
float t56 = P[4][2]*t4;
|
|
float t57 = P[0][2]*t14;
|
|
float t58 = P[2][2]*t20;
|
|
float t59 = P[3][2]*t24;
|
|
float t88 = P[5][2]*t11;
|
|
float t89 = P[1][2]*t17;
|
|
float t60 = t55+t56+t57+t58+t59-t88-t89;
|
|
float t61 = t20*t60;
|
|
float t62 = P[6][3]*t9;
|
|
float t63 = P[4][3]*t4;
|
|
float t64 = P[0][3]*t14;
|
|
float t65 = P[2][3]*t20;
|
|
float t66 = P[3][3]*t24;
|
|
float t90 = P[5][3]*t11;
|
|
float t91 = P[1][3]*t17;
|
|
float t67 = t62+t63+t64+t65+t66-t90-t91;
|
|
float t68 = t24*t67;
|
|
float t69 = P[6][6]*t9;
|
|
float t70 = P[4][6]*t4;
|
|
float t71 = P[0][6]*t14;
|
|
float t72 = P[2][6]*t20;
|
|
float t73 = P[3][6]*t24;
|
|
float t92 = P[5][6]*t11;
|
|
float t93 = P[1][6]*t17;
|
|
float t74 = t69+t70+t71+t72+t73-t92-t93;
|
|
float t75 = t9*t74;
|
|
float t82 = t11*t42;
|
|
float t87 = t17*t54;
|
|
float t76 = R_VEL+t36+t48+t61+t68+t75-t82-t87;
|
|
float t77;
|
|
|
|
// calculate innovation variance for Z axis observation and protect against a badly conditioned calculation
|
|
if (t76 > R_VEL) {
|
|
t77 = 1.0f/t76;
|
|
faultStatus.bad_zvel = false;
|
|
} else {
|
|
t76 = R_VEL;
|
|
t77 = 1.0f/R_VEL;
|
|
faultStatus.bad_zvel = true;
|
|
return;
|
|
}
|
|
varInnovBodyVel[2] = t77;
|
|
|
|
// calculate innovation for Z axis observation
|
|
innovBodyVel[2] = bodyVelPred.z - bodyOdmDataDelayed.vel.z;
|
|
|
|
// calculate Kalman gains for X-axis observation
|
|
Kfusion[0] = t77*(t29+P[0][4]*t4+P[0][6]*t9-P[0][5]*t11-P[0][1]*t17+P[0][2]*t20+P[0][3]*t24);
|
|
Kfusion[1] = t77*(P[1][4]*t4+P[1][0]*t14+P[1][6]*t9-P[1][5]*t11-P[1][1]*t17+P[1][2]*t20+P[1][3]*t24);
|
|
Kfusion[2] = t77*(t58+P[2][4]*t4+P[2][0]*t14+P[2][6]*t9-P[2][5]*t11-P[2][1]*t17+P[2][3]*t24);
|
|
Kfusion[3] = t77*(t66+P[3][4]*t4+P[3][0]*t14+P[3][6]*t9-P[3][5]*t11-P[3][1]*t17+P[3][2]*t20);
|
|
Kfusion[4] = t77*(t31+P[4][0]*t14+P[4][6]*t9-P[4][5]*t11-P[4][1]*t17+P[4][2]*t20+P[4][3]*t24);
|
|
Kfusion[5] = t77*(-t80+P[5][4]*t4+P[5][0]*t14+P[5][6]*t9-P[5][1]*t17+P[5][2]*t20+P[5][3]*t24);
|
|
Kfusion[6] = t77*(t69+P[6][4]*t4+P[6][0]*t14-P[6][5]*t11-P[6][1]*t17+P[6][2]*t20+P[6][3]*t24);
|
|
Kfusion[7] = t77*(P[7][4]*t4+P[7][0]*t14+P[7][6]*t9-P[7][5]*t11-P[7][1]*t17+P[7][2]*t20+P[7][3]*t24);
|
|
Kfusion[8] = t77*(P[8][4]*t4+P[8][0]*t14+P[8][6]*t9-P[8][5]*t11-P[8][1]*t17+P[8][2]*t20+P[8][3]*t24);
|
|
Kfusion[9] = t77*(P[9][4]*t4+P[9][0]*t14+P[9][6]*t9-P[9][5]*t11-P[9][1]*t17+P[9][2]*t20+P[9][3]*t24);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = t77*(P[10][4]*t4+P[10][0]*t14+P[10][6]*t9-P[10][5]*t11-P[10][1]*t17+P[10][2]*t20+P[10][3]*t24);
|
|
Kfusion[11] = t77*(P[11][4]*t4+P[11][0]*t14+P[11][6]*t9-P[11][5]*t11-P[11][1]*t17+P[11][2]*t20+P[11][3]*t24);
|
|
Kfusion[12] = t77*(P[12][4]*t4+P[12][0]*t14+P[12][6]*t9-P[12][5]*t11-P[12][1]*t17+P[12][2]*t20+P[12][3]*t24);
|
|
} else {
|
|
// zero indexes 10 to 12 = 3*4 bytes
|
|
memset(&Kfusion[10], 0, 12);
|
|
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
Kfusion[13] = t77*(P[13][4]*t4+P[13][0]*t14+P[13][6]*t9-P[13][5]*t11-P[13][1]*t17+P[13][2]*t20+P[13][3]*t24);
|
|
Kfusion[14] = t77*(P[14][4]*t4+P[14][0]*t14+P[14][6]*t9-P[14][5]*t11-P[14][1]*t17+P[14][2]*t20+P[14][3]*t24);
|
|
Kfusion[15] = t77*(P[15][4]*t4+P[15][0]*t14+P[15][6]*t9-P[15][5]*t11-P[15][1]*t17+P[15][2]*t20+P[15][3]*t24);
|
|
} else {
|
|
// zero indexes 13 to 15 = 3*4 bytes
|
|
memset(&Kfusion[13], 0, 12);
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = t77*(P[16][4]*t4+P[16][0]*t14+P[16][6]*t9-P[16][5]*t11-P[16][1]*t17+P[16][2]*t20+P[16][3]*t24);
|
|
Kfusion[17] = t77*(P[17][4]*t4+P[17][0]*t14+P[17][6]*t9-P[17][5]*t11-P[17][1]*t17+P[17][2]*t20+P[17][3]*t24);
|
|
Kfusion[18] = t77*(P[18][4]*t4+P[18][0]*t14+P[18][6]*t9-P[18][5]*t11-P[18][1]*t17+P[18][2]*t20+P[18][3]*t24);
|
|
Kfusion[19] = t77*(P[19][4]*t4+P[19][0]*t14+P[19][6]*t9-P[19][5]*t11-P[19][1]*t17+P[19][2]*t20+P[19][3]*t24);
|
|
Kfusion[20] = t77*(P[20][4]*t4+P[20][0]*t14+P[20][6]*t9-P[20][5]*t11-P[20][1]*t17+P[20][2]*t20+P[20][3]*t24);
|
|
Kfusion[21] = t77*(P[21][4]*t4+P[21][0]*t14+P[21][6]*t9-P[21][5]*t11-P[21][1]*t17+P[21][2]*t20+P[21][3]*t24);
|
|
} else {
|
|
// zero indexes 16 to 21 = 6*4 bytes
|
|
memset(&Kfusion[16], 0, 24);
|
|
}
|
|
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = t77*(P[22][4]*t4+P[22][0]*t14+P[22][6]*t9-P[22][5]*t11-P[22][1]*t17+P[22][2]*t20+P[22][3]*t24);
|
|
Kfusion[23] = t77*(P[23][4]*t4+P[23][0]*t14+P[23][6]*t9-P[23][5]*t11-P[23][1]*t17+P[23][2]*t20+P[23][3]*t24);
|
|
} else {
|
|
// zero indexes 22 to 23 = 2*4 bytes
|
|
memset(&Kfusion[22], 0, 8);
|
|
}
|
|
} else {
|
|
return;
|
|
}
|
|
|
|
// calculate the innovation consistency test ratio
|
|
// TODO add tuning parameter for gate
|
|
bodyVelTestRatio[obsIndex] = sq(innovBodyVel[obsIndex]) / (sq(5.0f) * varInnovBodyVel[obsIndex]);
|
|
|
|
// Check the innovation for consistency and don't fuse if out of bounds
|
|
// TODO also apply angular velocity magnitude check
|
|
if ((bodyVelTestRatio[obsIndex]) < 1.0f) {
|
|
// record the last time observations were accepted for fusion
|
|
prevBodyVelFuseTime_ms = imuSampleTime_ms;
|
|
// notify first time only
|
|
if (!bodyVelFusionActive) {
|
|
bodyVelFusionActive = true;
|
|
gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u fusing odometry",(unsigned)imu_index);
|
|
}
|
|
// 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<=6; j++) {
|
|
KH[i][j] = Kfusion[i] * H_VEL[j];
|
|
}
|
|
for (unsigned j = 7; j<=stateIndexLim; 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][6] * P[6][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-condiioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// correct the state vector
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
statesArray[j] = statesArray[j] - Kfusion[j] * innovBodyVel[obsIndex];
|
|
}
|
|
stateStruct.quat.normalize();
|
|
|
|
} else {
|
|
// record bad axis
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_xvel = true;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_yvel = true;
|
|
} else if (obsIndex == 2) {
|
|
faultStatus.bad_zvel = true;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// select fusion of body odometry measurements
|
|
void NavEKF3_core::SelectBodyOdomFusion()
|
|
{
|
|
// 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) && !bodyVelFusionDelayed) {
|
|
bodyVelFusionDelayed = true;
|
|
return;
|
|
} else {
|
|
bodyVelFusionDelayed = false;
|
|
}
|
|
|
|
// Check for data at the fusion time horizon
|
|
if (storedBodyOdm.recall(bodyOdmDataDelayed, imuDataDelayed.time_ms)) {
|
|
|
|
// start performance timer
|
|
hal.util->perf_begin(_perf_FuseBodyOdom);
|
|
|
|
usingWheelSensors = false;
|
|
|
|
// Fuse data into the main filter
|
|
FuseBodyVel();
|
|
|
|
// stop the performance timer
|
|
hal.util->perf_end(_perf_FuseBodyOdom);
|
|
|
|
} else if (storedWheelOdm.recall(wheelOdmDataDelayed, imuDataDelayed.time_ms)) {
|
|
|
|
// check if the delta time is too small to calculate a velocity
|
|
if (wheelOdmDataNew.delTime > EKF_TARGET_DT) {
|
|
|
|
// get the forward velocity
|
|
float fwdSpd = wheelOdmDataNew.delAng * wheelOdmDataNew.radius * (1.0f / wheelOdmDataNew.delTime);
|
|
|
|
// get the unit vector from the projection of the X axis onto the horizontal
|
|
Vector3f unitVec;
|
|
unitVec.x = prevTnb.a.x;
|
|
unitVec.y = prevTnb.a.y;
|
|
unitVec.z = 0.0f;
|
|
unitVec.normalized();
|
|
|
|
// multiply by forward speed to get velocity vector measured by wheel encoders
|
|
Vector3f velNED = unitVec * fwdSpd;
|
|
|
|
// This is a hack to enable use of the existing body frame velocity fusion method
|
|
// TODO write a dedicated observation model for wheel encoders
|
|
usingWheelSensors = true;
|
|
bodyOdmDataDelayed.vel = prevTnb * velNED;
|
|
bodyOdmDataDelayed.body_offset = wheelOdmDataNew.hub_offset;
|
|
bodyOdmDataDelayed.velErr = frontend->_wencOdmVelErr;
|
|
|
|
// Fuse data into the main filter
|
|
FuseBodyVel();
|
|
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
#endif // HAL_CPU_CLASS
|