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https://github.com/ArduPilot/ardupilot.git
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923 lines
40 KiB
C++
923 lines
40 KiB
C++
#include <AP_HAL/AP_HAL.h>
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#include "AP_NavEKF3.h"
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#include "AP_NavEKF3_core.h"
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#if EK3_FEATURE_OPTFLOW_FUSION
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#include <GCS_MAVLink/GCS.h>
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#include <AP_DAL/AP_DAL.h>
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/********************************************************
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* RESET FUNCTIONS *
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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 optical flow measurements
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void NavEKF3_core::SelectFlowFusion()
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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 && !optFlowFusionDelayed) {
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optFlowFusionDelayed = true;
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return;
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} else {
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optFlowFusionDelayed = false;
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}
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of_elements ofDataDelayed; // OF data at the fusion time horizon
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// Check for data at the fusion time horizon
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const bool flowDataToFuse = storedOF.recall(ofDataDelayed, imuDataDelayed.time_ms);
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// Perform Data Checks
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// Check if the optical flow data is still valid
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flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000);
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// check is the terrain offset estimate is still valid - if we are using range finder as the main height reference, the ground is assumed to be at 0
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gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000) || (activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER);
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// Perform tilt check
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bool tiltOK = (prevTnb.c.z > frontend->DCM33FlowMin);
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// Constrain measurements to zero if takeoff is not detected and the height above ground
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// is insufficient to achieve acceptable focus. This allows the vehicle to be picked up
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// and carried to test optical flow operation
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if (!takeOffDetected && ((terrainState - stateStruct.position.z) < 0.5f)) {
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ofDataDelayed.flowRadXYcomp.zero();
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ofDataDelayed.flowRadXY.zero();
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flowDataValid = true;
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}
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// if have valid flow or range measurements, fuse data into a 1-state EKF to estimate terrain height
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if (((flowDataToFuse && (frontend->_flowUse == FLOW_USE_TERRAIN)) || rangeDataToFuse) && tiltOK) {
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// Estimate the terrain offset (runs a one state EKF)
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EstimateTerrainOffset(ofDataDelayed);
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}
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#if EK3_FEATURE_OPTFLOW_AGL_KF
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// Update the IMU-aided AGL KF every IMU step when enabled, regardless of flow/RF data presence.
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if (frontend->option_is_enabled(NavEKF3::Option::AglKfForOptflow)) {
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UpdateAglKf();
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}
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#endif
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// Fuse optical flow data into the main filter
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if (flowDataToFuse && tiltOK) {
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const bool fuse_optflow = (frontend->_flowUse == FLOW_USE_NAV) && frontend->sources.useVelXYSource(AP_NavEKF_Source::SourceXY::OPTFLOW, core_index);
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// Set the flow noise used by the fusion processes
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R_LOS = sq(MAX(frontend->_flowNoise, 0.05f));
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// Fuse the optical flow X and Y axis data into the main filter sequentially
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FuseOptFlow(ofDataDelayed, fuse_optflow);
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}
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}
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/*
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Estimation of terrain offset using a single state EKF
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The filter can fuse motion compensated optical flow rates and range finder measurements
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Equations generated using https://github.com/PX4/ecl/tree/master/EKF/matlab/scripts/Terrain%20Estimator
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*/
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void NavEKF3_core::EstimateTerrainOffset(const of_elements &ofDataDelayed)
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{
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// horizontal velocity squared
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ftype velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y);
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// don't fuse flow data if LOS rate is misaligned, without GPS, or insufficient velocity, as it is poorly observable
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// don't fuse flow data if it exceeds validity limits
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// don't update terrain offset if ground is being used as the zero height datum in the main filter
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bool cantFuseFlowData = ((frontend->_flowUse != FLOW_USE_TERRAIN)
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|| !gpsIsInUse
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|| PV_AidingMode == AID_RELATIVE
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|| velHorizSq < 25.0f
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|| (MAX(ofDataDelayed.flowRadXY[0],ofDataDelayed.flowRadXY[1]) > frontend->_maxFlowRate));
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if ((!rangeDataToFuse && cantFuseFlowData) || (activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER)) {
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// skip update
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inhibitGndState = true;
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} else {
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inhibitGndState = false;
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// propagate ground position state noise each time this is called using the difference in position since the last observations and an RMS gradient assumption
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// limit distance to prevent intialisation after bad gps causing bad numerical conditioning
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ftype distanceTravelledSq = sq(stateStruct.position[0] - prevPosN) + sq(stateStruct.position[1] - prevPosE);
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distanceTravelledSq = MIN(distanceTravelledSq, 100.0f);
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prevPosN = stateStruct.position[0];
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prevPosE = stateStruct.position[1];
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// in addition to a terrain gradient error model, we also have the growth in uncertainty due to the copter's vertical velocity
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ftype timeLapsed = MIN(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f);
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ftype Pincrement = (distanceTravelledSq * sq(frontend->_terrGradMax)) + sq(timeLapsed)*P[6][6];
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Popt += Pincrement;
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timeAtLastAuxEKF_ms = imuSampleTime_ms;
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// fuse range finder data
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if (rangeDataToFuse) {
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// reset terrain state if rangefinder data not fused for 5 seconds
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if (imuSampleTime_ms - gndHgtValidTime_ms > 5000) {
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terrainState = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd) + stateStruct.position.z;
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}
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// predict range
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ftype predRngMeas = MAX((terrainState - stateStruct.position[2]),rngOnGnd) / prevTnb.c.z;
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// Copy required states to local variable names
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ftype q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
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ftype q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
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ftype q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
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ftype q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
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// Set range finder measurement noise variance. TODO make this a function of range and tilt to allow for sensor, alignment and AHRS errors
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ftype R_RNG = frontend->_rngNoise.get();
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// calculate Kalman gain
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ftype SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3);
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ftype K_RNG = Popt/(SK_RNG*(R_RNG + Popt/sq(SK_RNG)));
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// Calculate the innovation variance for data logging
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varInnovRng = (R_RNG + Popt/sq(SK_RNG));
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// constrain terrain height to be below the vehicle
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// Calculate the measurement innovation
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innovRng = predRngMeas - rangeDataDelayed.rng;
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// calculate the innovation consistency test ratio
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auxRngTestRatio = sq(innovRng) / (sq(MAX(0.01f * (ftype)frontend->_rngInnovGate, 1.0f)) * varInnovRng);
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// Check the innovation test ratio and don't fuse if too large
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if (auxRngTestRatio < 1.0f) {
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// correct the state
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terrainState -= K_RNG * innovRng;
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// constrain the state
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// correct the covariance
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Popt = Popt - sq(Popt)/(SK_RNG*(R_RNG + Popt/sq(SK_RNG))*(sq(q0) - sq(q1) - sq(q2) + sq(q3)));
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// prevent the state variance from becoming negative
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Popt = MAX(Popt,0.0f);
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// record the time we last updated the terrain offset state
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gndHgtValidTime_ms = imuSampleTime_ms;
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}
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}
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if (!cantFuseFlowData) {
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Vector3F relVelSensor; // velocity of sensor relative to ground in sensor axes
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Vector2F losPred; // predicted optical flow angular rate measurement
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ftype q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
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ftype q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
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ftype q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
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ftype q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
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ftype K_OPT;
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ftype H_OPT;
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Vector2F auxFlowObsInnovVar;
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// predict range to centre of image
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ftype flowRngPred = MAX((terrainState - stateStruct.position.z),rngOnGnd) / prevTnb.c.z;
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// constrain terrain height to be below the vehicle
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terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
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// calculate relative velocity in sensor frame
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relVelSensor = prevTnb*stateStruct.velocity;
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// divide velocity by range, subtract body rates and apply scale factor to
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// get predicted sensed angular optical rates relative to X and Y sensor axes
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losPred.x = relVelSensor.y / flowRngPred;
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losPred.y = - relVelSensor.x / flowRngPred;
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// calculate innovations
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auxFlowObsInnov = losPred - ofDataDelayed.flowRadXYcomp;
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// calculate observation jacobians
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ftype t2 = q0*q0;
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ftype t3 = q1*q1;
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ftype t4 = q2*q2;
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ftype t5 = q3*q3;
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ftype t6 = stateStruct.position.z - terrainState;
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ftype t7 = 1.0f / (t6*t6);
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ftype t8 = q0*q3*2.0f;
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ftype t9 = t2-t3-t4+t5;
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// prevent the state variances from becoming badly conditioned
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Popt = MAX(Popt,1E-6f);
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// calculate observation noise variance from parameter
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ftype flow_noise_variance = sq(MAX(frontend->_flowNoise, 0.05f));
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// Fuse Y axis data
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// Calculate observation partial derivative
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H_OPT = t7*t9*(-stateStruct.velocity.z*(q0*q2*2.0-q1*q3*2.0)+stateStruct.velocity.x*(t2+t3-t4-t5)+stateStruct.velocity.y*(t8+q1*q2*2.0));
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// calculate innovation variance
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auxFlowObsInnovVar.y = H_OPT * Popt * H_OPT + flow_noise_variance;
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// calculate Kalman gain
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K_OPT = Popt * H_OPT / auxFlowObsInnovVar.y;
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// calculate the innovation consistency test ratio
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auxFlowTestRatio.y = sq(auxFlowObsInnov.y) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.y);
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// don't fuse if optical flow data is outside valid range
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if (auxFlowTestRatio.y < 1.0f) {
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// correct the state
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terrainState -= K_OPT * auxFlowObsInnov.y;
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// constrain the state
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terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
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// update intermediate variables used when fusing the X axis
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t6 = stateStruct.position.z - terrainState;
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t7 = 1.0f / (t6*t6);
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// correct the covariance
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Popt = Popt - K_OPT * H_OPT * Popt;
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// prevent the state variances from becoming badly conditioned
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Popt = MAX(Popt,1E-6f);
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// record the time we last updated the terrain offset state
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gndHgtValidTime_ms = imuSampleTime_ms;
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}
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// fuse X axis data
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H_OPT = -t7*t9*(stateStruct.velocity.z*(q0*q1*2.0+q2*q3*2.0)+stateStruct.velocity.y*(t2-t3+t4-t5)-stateStruct.velocity.x*(t8-q1*q2*2.0));
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// calculate innovation variances
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auxFlowObsInnovVar.x = H_OPT * Popt * H_OPT + flow_noise_variance;
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// calculate Kalman gain
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K_OPT = Popt * H_OPT / auxFlowObsInnovVar.x;
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// calculate the innovation consistency test ratio
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auxFlowTestRatio.x = sq(auxFlowObsInnov.x) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar.x);
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// don't fuse if optical flow data is outside valid range
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if (auxFlowTestRatio.x < 1.0f) {
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// correct the state
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terrainState -= K_OPT * auxFlowObsInnov.x;
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// constrain the state
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terrainState = MAX(terrainState, stateStruct.position.z + rngOnGnd);
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// correct the covariance
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Popt = Popt - K_OPT * H_OPT * Popt;
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// prevent the state variances from becoming badly conditioned
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Popt = MAX(Popt,1E-6f);
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}
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}
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}
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}
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/*
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* Fuse angular motion compensated optical flow rates using explicit algebraic equations generated with Matlab symbolic toolbox.
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* The script file used to generate these and other equations in this filter can be found here:
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* https://github.com/PX4/ecl/blob/master/matlab/scripts/Inertial%20Nav%20EKF/GenerateNavFilterEquations.m
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* Requires a valid terrain height estimate.
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*
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* really_fuse should be true to actually fuse into the main filter, false to only calculate variances
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*/
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void NavEKF3_core::FuseOptFlow(const of_elements &ofDataDelayed, bool really_fuse)
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{
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Vector24 H_LOS;
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Vector2 losPred;
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// Copy required states to local variable names
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ftype q0 = stateStruct.quat[0];
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ftype q1 = stateStruct.quat[1];
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ftype q2 = stateStruct.quat[2];
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ftype q3 = stateStruct.quat[3];
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ftype vn = stateStruct.velocity.x;
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ftype ve = stateStruct.velocity.y;
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ftype vd = stateStruct.velocity.z;
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ftype pd = stateStruct.position.z;
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// Default is the terrain estimator AGL (terrainState - pd, where pd is the main filter's vertical position)
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// constrain height above ground to be above range measured on ground
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ftype heightAboveGndEst = MAX((terrainState - pd), rngOnGnd);
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#if EK3_FEATURE_OPTFLOW_SRTM
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// if ground offset (aka terrainState) is not valid, use SRTM altitude
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terrain_srtm_alt_valid = ((imuSampleTime_ms - terrain_srtm_alt_ms) < 5000);
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if (!gndOffsetValid && terrain_srtm_alt_valid) {
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heightAboveGndEst = MAX((terrain_srtm_alt - pd), rngOnGnd);
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}
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#endif
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#if EK3_FEATURE_OPTFLOW_AGL_KF
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// AGL KF override: use the IMU-aided AGL KF estimate when enabled and valid,
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// instead of terrainState-pd which can drift when the main filter's vertical position
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// state is unreliable (e.g. poor altitude source, sensor outage, or ground effect).
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if (frontend->option_is_enabled(NavEKF3::Option::AglKfForOptflow) && aglKfValid) {
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heightAboveGndEst = MAX(aglKfH, rngOnGnd);
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}
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#endif
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// calculate range from ground plain to centre of sensor fov assuming flat earth
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ftype range = constrain_ftype((heightAboveGndEst/prevTnb.c.z),rngOnGnd,1000.0f);
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// correct range for flow sensor offset body frame position offset
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// the corrected value is the predicted range from the sensor focal point to the
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// centre of the image on the ground assuming flat terrain
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Vector3F posOffsetBody = ofDataDelayed.body_offset - accelPosOffset;
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if (!posOffsetBody.is_zero()) {
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Vector3F posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
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range -= posOffsetEarth.z / prevTnb.c.z;
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}
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#if APM_BUILD_TYPE(APM_BUILD_Rover)
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// override with user specified height (if given, for rover)
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if (ofDataDelayed.heightOverride > 0) {
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range = ofDataDelayed.heightOverride;
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}
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#endif
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// Fuse X and Y axis measurements sequentially assuming observation errors are uncorrelated
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for (uint8_t obsIndex=0; obsIndex<=1; obsIndex++) { // fuse X axis data first
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// calculate relative velocity in sensor frame including the relative motion due to rotation
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const Vector3F relVelSensor = (prevTnb * stateStruct.velocity) + (ofDataDelayed.bodyRadXYZ % posOffsetBody);
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// divide velocity by range to get predicted angular LOS rates relative to X and Y axes
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losPred[0] = relVelSensor.y/range;
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losPred[1] = -relVelSensor.x/range;
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// calculate observation jacobians and Kalman gains
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memset(&H_LOS[0], 0, sizeof(H_LOS));
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if (obsIndex == 0) {
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// calculate X axis observation Jacobian
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ftype t2 = 1.0f / range;
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H_LOS[0] = t2*(q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f);
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H_LOS[1] = t2*(q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f);
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H_LOS[2] = t2*(q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f);
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H_LOS[3] = -t2*(q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f);
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H_LOS[4] = -t2*(q0*q3*2.0f-q1*q2*2.0f);
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H_LOS[5] = t2*(q0*q0-q1*q1+q2*q2-q3*q3);
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H_LOS[6] = t2*(q0*q1*2.0f+q2*q3*2.0f);
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// calculate intermediate variables for the X observation innovation variance and Kalman gains
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ftype t3 = q1*vd*2.0f;
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ftype t4 = q0*ve*2.0f;
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ftype t11 = q3*vn*2.0f;
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ftype t5 = t3+t4-t11;
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ftype t6 = q0*q3*2.0f;
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ftype t29 = q1*q2*2.0f;
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ftype t7 = t6-t29;
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ftype t8 = q0*q1*2.0f;
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ftype t9 = q2*q3*2.0f;
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ftype t10 = t8+t9;
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ftype t12 = P[0][0]*t2*t5;
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ftype t13 = q0*vd*2.0f;
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ftype t14 = q2*vn*2.0f;
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ftype t28 = q1*ve*2.0f;
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ftype t15 = t13+t14-t28;
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ftype t16 = q3*vd*2.0f;
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ftype t17 = q2*ve*2.0f;
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ftype t18 = q1*vn*2.0f;
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ftype t19 = t16+t17+t18;
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ftype t20 = q3*ve*2.0f;
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ftype t21 = q0*vn*2.0f;
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ftype t30 = q2*vd*2.0f;
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ftype t22 = t20+t21-t30;
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ftype t23 = q0*q0;
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ftype t24 = q1*q1;
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ftype t25 = q2*q2;
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ftype t26 = q3*q3;
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ftype t27 = t23-t24+t25-t26;
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ftype t31 = P[1][1]*t2*t15;
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ftype t32 = P[6][0]*t2*t10;
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ftype t33 = P[1][0]*t2*t15;
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ftype t34 = P[2][0]*t2*t19;
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ftype t35 = P[5][0]*t2*t27;
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ftype t79 = P[4][0]*t2*t7;
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ftype t80 = P[3][0]*t2*t22;
|
|
ftype t36 = t12+t32+t33+t34+t35-t79-t80;
|
|
ftype t37 = t2*t5*t36;
|
|
ftype t38 = P[6][1]*t2*t10;
|
|
ftype t39 = P[0][1]*t2*t5;
|
|
ftype t40 = P[2][1]*t2*t19;
|
|
ftype t41 = P[5][1]*t2*t27;
|
|
ftype t81 = P[4][1]*t2*t7;
|
|
ftype t82 = P[3][1]*t2*t22;
|
|
ftype t42 = t31+t38+t39+t40+t41-t81-t82;
|
|
ftype t43 = t2*t15*t42;
|
|
ftype t44 = P[6][2]*t2*t10;
|
|
ftype t45 = P[0][2]*t2*t5;
|
|
ftype t46 = P[1][2]*t2*t15;
|
|
ftype t47 = P[2][2]*t2*t19;
|
|
ftype t48 = P[5][2]*t2*t27;
|
|
ftype t83 = P[4][2]*t2*t7;
|
|
ftype t84 = P[3][2]*t2*t22;
|
|
ftype t49 = t44+t45+t46+t47+t48-t83-t84;
|
|
ftype t50 = t2*t19*t49;
|
|
ftype t51 = P[6][3]*t2*t10;
|
|
ftype t52 = P[0][3]*t2*t5;
|
|
ftype t53 = P[1][3]*t2*t15;
|
|
ftype t54 = P[2][3]*t2*t19;
|
|
ftype t55 = P[5][3]*t2*t27;
|
|
ftype t85 = P[4][3]*t2*t7;
|
|
ftype t86 = P[3][3]*t2*t22;
|
|
ftype t56 = t51+t52+t53+t54+t55-t85-t86;
|
|
ftype t57 = P[6][5]*t2*t10;
|
|
ftype t58 = P[0][5]*t2*t5;
|
|
ftype t59 = P[1][5]*t2*t15;
|
|
ftype t60 = P[2][5]*t2*t19;
|
|
ftype t61 = P[5][5]*t2*t27;
|
|
ftype t88 = P[4][5]*t2*t7;
|
|
ftype t89 = P[3][5]*t2*t22;
|
|
ftype t62 = t57+t58+t59+t60+t61-t88-t89;
|
|
ftype t63 = t2*t27*t62;
|
|
ftype t64 = P[6][4]*t2*t10;
|
|
ftype t65 = P[0][4]*t2*t5;
|
|
ftype t66 = P[1][4]*t2*t15;
|
|
ftype t67 = P[2][4]*t2*t19;
|
|
ftype t68 = P[5][4]*t2*t27;
|
|
ftype t90 = P[4][4]*t2*t7;
|
|
ftype t91 = P[3][4]*t2*t22;
|
|
ftype t69 = t64+t65+t66+t67+t68-t90-t91;
|
|
ftype t70 = P[6][6]*t2*t10;
|
|
ftype t71 = P[0][6]*t2*t5;
|
|
ftype t72 = P[1][6]*t2*t15;
|
|
ftype t73 = P[2][6]*t2*t19;
|
|
ftype t74 = P[5][6]*t2*t27;
|
|
ftype t93 = P[4][6]*t2*t7;
|
|
ftype t94 = P[3][6]*t2*t22;
|
|
ftype t75 = t70+t71+t72+t73+t74-t93-t94;
|
|
ftype t76 = t2*t10*t75;
|
|
ftype t87 = t2*t22*t56;
|
|
ftype t92 = t2*t7*t69;
|
|
ftype t77 = R_LOS+t37+t43+t50+t63+t76-t87-t92;
|
|
ftype t78;
|
|
|
|
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
|
|
if (t77 > R_LOS) {
|
|
t78 = 1.0f/t77;
|
|
faultStatus.bad_xflow = false;
|
|
} else {
|
|
t77 = R_LOS;
|
|
t78 = 1.0f/R_LOS;
|
|
faultStatus.bad_xflow = true;
|
|
return;
|
|
}
|
|
flowVarInnov[0] = t77;
|
|
|
|
// calculate innovation for X axis observation
|
|
// flowInnovTime_ms will be updated when Y-axis innovations are calculated
|
|
flowInnov[0] = losPred[0] - ofDataDelayed.flowRadXYcomp.x;
|
|
|
|
// calculate Kalman gains for X-axis observation
|
|
Kfusion[0] = t78*(t12-P[0][4]*t2*t7+P[0][1]*t2*t15+P[0][6]*t2*t10+P[0][2]*t2*t19-P[0][3]*t2*t22+P[0][5]*t2*t27);
|
|
Kfusion[1] = t78*(t31+P[1][0]*t2*t5-P[1][4]*t2*t7+P[1][6]*t2*t10+P[1][2]*t2*t19-P[1][3]*t2*t22+P[1][5]*t2*t27);
|
|
Kfusion[2] = t78*(t47+P[2][0]*t2*t5-P[2][4]*t2*t7+P[2][1]*t2*t15+P[2][6]*t2*t10-P[2][3]*t2*t22+P[2][5]*t2*t27);
|
|
Kfusion[3] = t78*(-t86+P[3][0]*t2*t5-P[3][4]*t2*t7+P[3][1]*t2*t15+P[3][6]*t2*t10+P[3][2]*t2*t19+P[3][5]*t2*t27);
|
|
Kfusion[4] = t78*(-t90+P[4][0]*t2*t5+P[4][1]*t2*t15+P[4][6]*t2*t10+P[4][2]*t2*t19-P[4][3]*t2*t22+P[4][5]*t2*t27);
|
|
Kfusion[5] = t78*(t61+P[5][0]*t2*t5-P[5][4]*t2*t7+P[5][1]*t2*t15+P[5][6]*t2*t10+P[5][2]*t2*t19-P[5][3]*t2*t22);
|
|
Kfusion[6] = t78*(t70+P[6][0]*t2*t5-P[6][4]*t2*t7+P[6][1]*t2*t15+P[6][2]*t2*t19-P[6][3]*t2*t22+P[6][5]*t2*t27);
|
|
|
|
// values to calculate in Kfusion (others are set to zero, indices 0-6 ignored)
|
|
uint32_t kalman_mask = (1<<7) | (1<<8) | (1<<9);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
kalman_mask |= (1<<10) | (1<<11) | (1<<12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates && !badIMUdata) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
kalman_mask |= (1<<stateIndex);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
kalman_mask |= (1<<16) | (1<<17) | (1<<18) | (1<<19) | (1<<20) | (1<<21);
|
|
}
|
|
|
|
if (!inhibitWindStates && !treatWindStatesAsTruth) {
|
|
kalman_mask |= (1<<22) | (1<<23);
|
|
}
|
|
|
|
for (auto i=7; i<24; i++) { // 0-6 are already computed
|
|
ftype res = 0;
|
|
if (kalman_mask & (1<<i)) {
|
|
res = t78*(P[i][0]*t2*t5-P[i][4]*t2*t7+P[i][1]*t2*t15+P[i][6]*t2*t10+P[i][2]*t2*t19-P[i][3]*t2*t22+P[i][5]*t2*t27);
|
|
}
|
|
Kfusion[i] = res;
|
|
}
|
|
} else {
|
|
|
|
// calculate Y axis observation Jacobian
|
|
ftype t2 = 1.0f / range;
|
|
H_LOS[0] = -t2*(q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f);
|
|
H_LOS[1] = -t2*(q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f);
|
|
H_LOS[2] = t2*(q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f);
|
|
H_LOS[3] = -t2*(q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f);
|
|
H_LOS[4] = -t2*(q0*q0+q1*q1-q2*q2-q3*q3);
|
|
H_LOS[5] = -t2*(q0*q3*2.0f+q1*q2*2.0f);
|
|
H_LOS[6] = t2*(q0*q2*2.0f-q1*q3*2.0f);
|
|
|
|
// calculate intermediate variables for the Y observation innovation variance and Kalman gains
|
|
ftype t3 = q3*ve*2.0f;
|
|
ftype t4 = q0*vn*2.0f;
|
|
ftype t11 = q2*vd*2.0f;
|
|
ftype t5 = t3+t4-t11;
|
|
ftype t6 = q0*q3*2.0f;
|
|
ftype t7 = q1*q2*2.0f;
|
|
ftype t8 = t6+t7;
|
|
ftype t9 = q0*q2*2.0f;
|
|
ftype t28 = q1*q3*2.0f;
|
|
ftype t10 = t9-t28;
|
|
ftype t12 = P[0][0]*t2*t5;
|
|
ftype t13 = q3*vd*2.0f;
|
|
ftype t14 = q2*ve*2.0f;
|
|
ftype t15 = q1*vn*2.0f;
|
|
ftype t16 = t13+t14+t15;
|
|
ftype t17 = q0*vd*2.0f;
|
|
ftype t18 = q2*vn*2.0f;
|
|
ftype t29 = q1*ve*2.0f;
|
|
ftype t19 = t17+t18-t29;
|
|
ftype t20 = q1*vd*2.0f;
|
|
ftype t21 = q0*ve*2.0f;
|
|
ftype t30 = q3*vn*2.0f;
|
|
ftype t22 = t20+t21-t30;
|
|
ftype t23 = q0*q0;
|
|
ftype t24 = q1*q1;
|
|
ftype t25 = q2*q2;
|
|
ftype t26 = q3*q3;
|
|
ftype t27 = t23+t24-t25-t26;
|
|
ftype t31 = P[1][1]*t2*t16;
|
|
ftype t32 = P[5][0]*t2*t8;
|
|
ftype t33 = P[1][0]*t2*t16;
|
|
ftype t34 = P[3][0]*t2*t22;
|
|
ftype t35 = P[4][0]*t2*t27;
|
|
ftype t80 = P[6][0]*t2*t10;
|
|
ftype t81 = P[2][0]*t2*t19;
|
|
ftype t36 = t12+t32+t33+t34+t35-t80-t81;
|
|
ftype t37 = t2*t5*t36;
|
|
ftype t38 = P[5][1]*t2*t8;
|
|
ftype t39 = P[0][1]*t2*t5;
|
|
ftype t40 = P[3][1]*t2*t22;
|
|
ftype t41 = P[4][1]*t2*t27;
|
|
ftype t82 = P[6][1]*t2*t10;
|
|
ftype t83 = P[2][1]*t2*t19;
|
|
ftype t42 = t31+t38+t39+t40+t41-t82-t83;
|
|
ftype t43 = t2*t16*t42;
|
|
ftype t44 = P[5][2]*t2*t8;
|
|
ftype t45 = P[0][2]*t2*t5;
|
|
ftype t46 = P[1][2]*t2*t16;
|
|
ftype t47 = P[3][2]*t2*t22;
|
|
ftype t48 = P[4][2]*t2*t27;
|
|
ftype t79 = P[2][2]*t2*t19;
|
|
ftype t84 = P[6][2]*t2*t10;
|
|
ftype t49 = t44+t45+t46+t47+t48-t79-t84;
|
|
ftype t50 = P[5][3]*t2*t8;
|
|
ftype t51 = P[0][3]*t2*t5;
|
|
ftype t52 = P[1][3]*t2*t16;
|
|
ftype t53 = P[3][3]*t2*t22;
|
|
ftype t54 = P[4][3]*t2*t27;
|
|
ftype t86 = P[6][3]*t2*t10;
|
|
ftype t87 = P[2][3]*t2*t19;
|
|
ftype t55 = t50+t51+t52+t53+t54-t86-t87;
|
|
ftype t56 = t2*t22*t55;
|
|
ftype t57 = P[5][4]*t2*t8;
|
|
ftype t58 = P[0][4]*t2*t5;
|
|
ftype t59 = P[1][4]*t2*t16;
|
|
ftype t60 = P[3][4]*t2*t22;
|
|
ftype t61 = P[4][4]*t2*t27;
|
|
ftype t88 = P[6][4]*t2*t10;
|
|
ftype t89 = P[2][4]*t2*t19;
|
|
ftype t62 = t57+t58+t59+t60+t61-t88-t89;
|
|
ftype t63 = t2*t27*t62;
|
|
ftype t64 = P[5][5]*t2*t8;
|
|
ftype t65 = P[0][5]*t2*t5;
|
|
ftype t66 = P[1][5]*t2*t16;
|
|
ftype t67 = P[3][5]*t2*t22;
|
|
ftype t68 = P[4][5]*t2*t27;
|
|
ftype t90 = P[6][5]*t2*t10;
|
|
ftype t91 = P[2][5]*t2*t19;
|
|
ftype t69 = t64+t65+t66+t67+t68-t90-t91;
|
|
ftype t70 = t2*t8*t69;
|
|
ftype t71 = P[5][6]*t2*t8;
|
|
ftype t72 = P[0][6]*t2*t5;
|
|
ftype t73 = P[1][6]*t2*t16;
|
|
ftype t74 = P[3][6]*t2*t22;
|
|
ftype t75 = P[4][6]*t2*t27;
|
|
ftype t92 = P[6][6]*t2*t10;
|
|
ftype t93 = P[2][6]*t2*t19;
|
|
ftype t76 = t71+t72+t73+t74+t75-t92-t93;
|
|
ftype t85 = t2*t19*t49;
|
|
ftype t94 = t2*t10*t76;
|
|
ftype t77 = R_LOS+t37+t43+t56+t63+t70-t85-t94;
|
|
ftype t78;
|
|
|
|
// calculate innovation variance for Y axis observation and protect against a badly conditioned calculation
|
|
if (t77 > R_LOS) {
|
|
t78 = 1.0f/t77;
|
|
faultStatus.bad_yflow = false;
|
|
} else {
|
|
t77 = R_LOS;
|
|
t78 = 1.0f/R_LOS;
|
|
faultStatus.bad_yflow = true;
|
|
return;
|
|
}
|
|
flowVarInnov[1] = t77;
|
|
|
|
// calculate innovation for Y observation
|
|
flowInnov[1] = losPred[1] - ofDataDelayed.flowRadXYcomp.y;
|
|
flowInnovTime_ms = dal.millis();
|
|
|
|
// calculate Kalman gains for the Y-axis observation
|
|
Kfusion[0] = -t78*(t12+P[0][5]*t2*t8-P[0][6]*t2*t10+P[0][1]*t2*t16-P[0][2]*t2*t19+P[0][3]*t2*t22+P[0][4]*t2*t27);
|
|
Kfusion[1] = -t78*(t31+P[1][0]*t2*t5+P[1][5]*t2*t8-P[1][6]*t2*t10-P[1][2]*t2*t19+P[1][3]*t2*t22+P[1][4]*t2*t27);
|
|
Kfusion[2] = -t78*(-t79+P[2][0]*t2*t5+P[2][5]*t2*t8-P[2][6]*t2*t10+P[2][1]*t2*t16+P[2][3]*t2*t22+P[2][4]*t2*t27);
|
|
Kfusion[3] = -t78*(t53+P[3][0]*t2*t5+P[3][5]*t2*t8-P[3][6]*t2*t10+P[3][1]*t2*t16-P[3][2]*t2*t19+P[3][4]*t2*t27);
|
|
Kfusion[4] = -t78*(t61+P[4][0]*t2*t5+P[4][5]*t2*t8-P[4][6]*t2*t10+P[4][1]*t2*t16-P[4][2]*t2*t19+P[4][3]*t2*t22);
|
|
Kfusion[5] = -t78*(t64+P[5][0]*t2*t5-P[5][6]*t2*t10+P[5][1]*t2*t16-P[5][2]*t2*t19+P[5][3]*t2*t22+P[5][4]*t2*t27);
|
|
Kfusion[6] = -t78*(-t92+P[6][0]*t2*t5+P[6][5]*t2*t8+P[6][1]*t2*t16-P[6][2]*t2*t19+P[6][3]*t2*t22+P[6][4]*t2*t27);
|
|
|
|
// values to calculate in Kfusion (others are set to zero, indices 0-6 ignored)
|
|
uint32_t kalman_mask = (1<<7) | (1<<8) | (1<<9);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
kalman_mask |= (1<<10) | (1<<11) | (1<<12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates && !badIMUdata) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
kalman_mask |= (1<<stateIndex);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
kalman_mask |= (1<<16) | (1<<17) | (1<<18) | (1<<19) | (1<<20) | (1<<21);
|
|
}
|
|
|
|
if (!inhibitWindStates && !treatWindStatesAsTruth) {
|
|
kalman_mask |= (1<<22) | (1<<23);
|
|
}
|
|
|
|
for (auto i=7; i<24; i++) { // 0-6 are already computed
|
|
ftype res = 0;
|
|
if (kalman_mask & (1<<i)) {
|
|
res = -t78*(P[i][0]*t2*t5+P[i][5]*t2*t8-P[i][6]*t2*t10+P[i][1]*t2*t16-P[i][2]*t2*t19+P[i][3]*t2*t22+P[i][4]*t2*t27);
|
|
}
|
|
Kfusion[i] = res;
|
|
}
|
|
}
|
|
|
|
// calculate the innovation consistency test ratio
|
|
flowTestRatio[obsIndex] = sq(flowInnov[obsIndex]) / (sq(MAX(0.01f * (ftype)frontend->_flowInnovGate, 1.0f)) * flowVarInnov[obsIndex]);
|
|
|
|
// Check the innovation for consistency and don't fuse if out of bounds or flow is too fast to be reliable
|
|
if (really_fuse && (flowTestRatio[obsIndex]) < 1.0f && (ofDataDelayed.flowRadXY.x < frontend->_maxFlowRate) && (ofDataDelayed.flowRadXY.y < frontend->_maxFlowRate)) {
|
|
// record the last time observations were accepted for fusion
|
|
prevFlowFuseTime_ms = imuSampleTime_ms;
|
|
// notify first time only
|
|
if (!flowFusionActive) {
|
|
flowFusionActive = true;
|
|
GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u fusing optical flow",(unsigned)imu_index);
|
|
}
|
|
|
|
// correct the covariance P = (I - K*H)*P = P - K*H*P. take advantage of
|
|
// the zero elements of H to reduce the number of operations.
|
|
for (unsigned i = 0; i<=stateIndexLim; i++) {
|
|
// j as the inner loop allows the compiler to hoist the KH product
|
|
// to save computation, and do the inner indexing more efficiently.
|
|
for (unsigned j = 0; j<=stateIndexLim; j++) {
|
|
ftype res = 0;
|
|
res += (Kfusion[i] * H_LOS[0]) * P[0][j];
|
|
res += (Kfusion[i] * H_LOS[1]) * P[1][j];
|
|
res += (Kfusion[i] * H_LOS[2]) * P[2][j];
|
|
res += (Kfusion[i] * H_LOS[3]) * P[3][j];
|
|
res += (Kfusion[i] * H_LOS[4]) * P[4][j];
|
|
res += (Kfusion[i] * H_LOS[5]) * P[5][j];
|
|
res += (Kfusion[i] * H_LOS[6]) * P[6][j];
|
|
KHP[i][j] = res;
|
|
}
|
|
}
|
|
|
|
// finish fusion from KHP and Kfusion
|
|
if (FinishFusion(flowInnov[obsIndex])) {
|
|
// fault, record bad axis
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_xflow = true;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_yflow = true;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
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|
|
|
// store optical flow rates for use in external calibration
|
|
flowCalSample.timestamp_ms = imuSampleTime_ms;
|
|
flowCalSample.flowRate.x = ofDataDelayed.flowRadXY.x;
|
|
flowCalSample.flowRate.y = ofDataDelayed.flowRadXY.y;
|
|
flowCalSample.bodyRate.x = ofDataDelayed.bodyRadXYZ.x;
|
|
flowCalSample.bodyRate.y = ofDataDelayed.bodyRadXYZ.y;
|
|
flowCalSample.losPred.x = losPred[0];
|
|
flowCalSample.losPred.y = losPred[1];
|
|
}
|
|
|
|
// retrieve latest corrected optical flow samples (used for calibration)
|
|
bool NavEKF3_core::getOptFlowSample(uint32_t& timestamp_ms, Vector2f& flowRate, Vector2f& bodyRate, Vector2f& losPred) const
|
|
{
|
|
if (flowCalSample.timestamp_ms != 0) {
|
|
timestamp_ms = flowCalSample.timestamp_ms;
|
|
flowRate = flowCalSample.flowRate;
|
|
bodyRate = flowCalSample.bodyRate;
|
|
losPred = flowCalSample.losPred;
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
/********************************************************
|
|
* MISC FUNCTIONS *
|
|
********************************************************/
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|
|
|
#if EK3_FEATURE_OPTFLOW_AGL_KF
|
|
/*
|
|
* 2-state IMU-aided AGL Kalman filter
|
|
*
|
|
* State: x = [h_agl (m), v_agl (m/s)]' (AGL is "up")
|
|
* Transition: F = [[1, imuDt], [0, 1]]
|
|
* Input: u = -velDotNED.z * imuDt (gravity-included accel)
|
|
* Noise: Qvel = sq(EK3_ACC_P_NSE * imuDt)
|
|
* Qhgt = sq(EK3_TERR_GRAD) * horizDist²
|
|
*
|
|
* Prediction: x(k+1) = F*x(k) + [0, u]'
|
|
* P(k+1) = F*P*F' + Q
|
|
*
|
|
* Observation: z = rng * prevTnb.c.z, H = [1, 0], R = sq(EK3_RNG_M_NSE)
|
|
* Update: innov = z - H*x, innovVar = H*P*H' + R
|
|
* K = P*H' / innovVar
|
|
* x += K * innov
|
|
* P = (I-KH)*P*(I-KH)' + K*R*K'
|
|
*/
|
|
void NavEKF3_core::UpdateAglKf()
|
|
{
|
|
const ftype imuDt = imuDataDelayed.delVelDT;
|
|
const uint32_t aglKfRngTimeout_ms = 5000; // mark filter invalid / hard-reset after this gap without RF fusion
|
|
|
|
// h_agl(k+1) = h_agl(k) + v_agl(k)*imuDt
|
|
aglKfH += aglKfV * imuDt;
|
|
|
|
// v_agl(k+1) = v_agl(k) - velDotNED.z*imuDt
|
|
// velDotNED.z is NED-down acceleration (positive = downward, includes gravity).
|
|
// Negate: downward acceleration reduces AGL rate.
|
|
aglKfV -= velDotNED.z * imuDt;
|
|
|
|
// First-order decay of v_agl toward zero when RF is absent (tau = 2 s).
|
|
// Without range measurements v_agl is unobservable; accumulated IMU bias
|
|
// error will cause it to drift, pulling h_agl to the floor during
|
|
// subsequent climbs. The decay limits that drift.
|
|
// At the aglKfRngTimeout_ms validity timeout (5 s), |v| is at most
|
|
// exp(-5/2) ~ 8% of its value at last RF fusion, so the hard reset finds v near zero.
|
|
if (!rangeDataToFuse) {
|
|
const ftype tauV = 2.0f;
|
|
aglKfV *= expf(-imuDt / tauV);
|
|
}
|
|
|
|
// AGL cannot go below the on-ground sensor reading
|
|
aglKfH = MAX(aglKfH, rngOnGnd);
|
|
|
|
// ----- Covariance prediction: P = F*P*F' + Q -----
|
|
//
|
|
// F = [[1, imuDt], state-transition matrix
|
|
// [0, 1]]
|
|
//
|
|
// Process noise Q:
|
|
//
|
|
// Qhgt — terrain-induced AGL uncertainty during forward flight.
|
|
// As the vehicle moves horizontally by dist over terrain with unknown gradient "g",
|
|
// the true AGL changes by ~g * dist. Since "g" is unknown, we treat it as zero-mean
|
|
// with std-dev terrGradMax, giving variance: Qhgt = terrGradMax² * horizDist²
|
|
// horizDist² = (vx²+vy²)*imuDt² is the squared horizontal distance travelled this step.
|
|
// Capped at 1 m² so a single large-velocity step can't blow up the covariance.
|
|
// The intended effect is that P[0][0] grows quickly during fast horizontal flight over rough terrain,
|
|
// allowing the RF measurement to pull h_agl back when the next reading arrives.
|
|
//
|
|
// Qvel — unmodelled vertical accelerations (IMU noise, vibration, model error).
|
|
// Uses the same accNoise parameter as CovariancePrediction (sq(imuDt*accNoise)),
|
|
// so the velocity uncertainty budget is consistent with the main EKF.
|
|
// The intended effect is that P[1][1] grows every step when RF is absent, reflecting accumulating IMU integration error in v_agl.
|
|
//
|
|
const ftype horizDistSq = MIN(sq(stateStruct.velocity.x * imuDt)
|
|
+ sq(stateStruct.velocity.y * imuDt), 1.0f); // cap at 1 m²
|
|
const ftype Qvel = sq(frontend->_accNoise * imuDt); // matches CovariancePrediction: sq(imuDt*accNoise)
|
|
const ftype Qhgt = sq(frontend->_terrGradMax) * horizDistSq;
|
|
|
|
// Capture before overwrite (P is symmetric, so P[0][1] == P[1][0])
|
|
const ftype P00 = aglKfP[0][0];
|
|
const ftype P01 = aglKfP[0][1]; // == P[1][0]
|
|
const ftype P11 = aglKfP[1][1];
|
|
|
|
// Expanded F*P*F' + Q:
|
|
aglKfP[0][0] = P00 + imuDt * (P01 + P01) + sq(imuDt) * P11 + Qhgt;
|
|
aglKfP[0][1] = aglKfP[1][0] = P01 + imuDt * P11;
|
|
aglKfP[1][1] = P11 + Qvel;
|
|
|
|
// Cap covariance to prevent runaway during prolonged RF absence
|
|
aglKfP[0][0] = MIN(aglKfP[0][0], 100.0f); // 10 m std-dev cap
|
|
aglKfP[1][1] = MIN(aglKfP[1][1], 100.0f); // 10 m/s std-dev cap
|
|
|
|
// mark invalid if RF has been absent too long
|
|
if (!rangeDataToFuse) {
|
|
if (imuSampleTime_ms - lastAglRngFuseTime_ms > aglKfRngTimeout_ms) {
|
|
aglKfValid = false;
|
|
}
|
|
return;
|
|
}
|
|
|
|
// Only fuse when vehicle tilt is within acceptable limits
|
|
if (prevTnb.c.z < frontend->DCM33FlowMin) {
|
|
return;
|
|
}
|
|
|
|
// After the timeout of IMU-only propagation, vertical velocity drift makes the
|
|
// prediction unreliable. Re-initialise directly from the rangefinder.
|
|
if (imuSampleTime_ms - lastAglRngFuseTime_ms > aglKfRngTimeout_ms) {
|
|
// Tilt-corrected AGL directly from rangefinder reading
|
|
aglKfH = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd);
|
|
aglKfV = 0.0f; // assume stationary on reset
|
|
aglKfP[0][0] = sq(frontend->_rngNoise); // initialise h uncertainty to RF noise
|
|
aglKfP[0][1] = aglKfP[1][0] = 0.0f;
|
|
aglKfP[1][1] = 1.0f; // 1 m/s velocity uncertainty after reset
|
|
lastAglRngFuseTime_ms = imuSampleTime_ms;
|
|
aglKfValid = true;
|
|
return; // skip measurement update this cycle (just used the reading for reset)
|
|
}
|
|
|
|
// Measurement update — fuse tilt-corrected rangefinder reading
|
|
//
|
|
// Observation model: z = h_agl, H = [1, 0]
|
|
// z_meas = rng * cos(tilt) = rng * prevTnb.c.z
|
|
//
|
|
const ftype hgtMeas = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd);
|
|
|
|
// Measurement noise variance R (reuse EK3_RNG_M_NSE)
|
|
const ftype measNoiseVar = sq(frontend->_rngNoise);
|
|
|
|
// Innovation and innovation covariance
|
|
// hgtInnov = hgtMeas - H*x = hgtMeas - h_agl
|
|
// innovVar = H*P*H' + R = P[0][0] + R
|
|
const ftype hgtInnov = hgtMeas - aglKfH;
|
|
const ftype innovVar = aglKfP[0][0] + measNoiseVar;
|
|
|
|
// reject outliers (RF glitches, specular reflections, etc.)
|
|
// gate is expressed as a multiplier on the 1-sigma bound.
|
|
const ftype innovGate = MAX(0.01f * (ftype)frontend->_rngInnovGate, 1.0f);
|
|
if (sq(hgtInnov) > sq(innovGate) * innovVar) {
|
|
// Innovation too large, likely a glitch. Inflate both height and velocity
|
|
// uncertainty so the next valid reading can correct both states more aggressively.
|
|
aglKfP[0][0] = MIN(aglKfP[0][0] * 2.0f, 100.0f);
|
|
aglKfP[1][1] = MIN(aglKfP[1][1] * 2.0f, 100.0f);
|
|
return;
|
|
}
|
|
|
|
// Kalman gain: K = P*H' / innovVar = [P[0][0]/innovVar, P[1][0]/innovVar]'
|
|
// (H = [1, 0], so P*H' = first column of P)
|
|
const ftype Kh = aglKfP[0][0] / innovVar;
|
|
const ftype Kv = aglKfP[1][0] / innovVar;
|
|
|
|
// State update: x += K * hgtInnov
|
|
aglKfH += Kh * hgtInnov;
|
|
aglKfV += Kv * hgtInnov;
|
|
aglKfH = MAX(aglKfH, rngOnGnd); // enforce physical constraint after update
|
|
|
|
// Covariance update P = (I-KH)*P*(I-KH)' + K*R*K'
|
|
// With H = [1, 0], (I-KH) = [[1-Kh, 0], [-Kv, 1]]:
|
|
// P[0][0] = (1-Kh)²*Phh + Kh²*R
|
|
// P[0][1] = (1-Kh)*(Phv - Kv*Phh) + Kh*Kv*R
|
|
// P[1][1] = Pvv - 2*Kv*Phv + Kv²*innovVar
|
|
const ftype oneMinusKh = 1.0f - Kh;
|
|
const ftype Phh = aglKfP[0][0];
|
|
const ftype Phv = aglKfP[0][1];
|
|
const ftype Pvv = aglKfP[1][1];
|
|
|
|
aglKfP[0][0] = MAX(sq(oneMinusKh) * Phh + sq(Kh) * measNoiseVar, 0.0f);
|
|
aglKfP[0][1] = aglKfP[1][0] = oneMinusKh * (Phv - Kv * Phh) + Kh * Kv * measNoiseVar;
|
|
aglKfP[1][1] = MAX(Pvv - 2.0f * Kv * Phv + sq(Kv) * innovVar, 0.0f);
|
|
|
|
lastAglRngFuseTime_ms = imuSampleTime_ms;
|
|
aglKfValid = true;
|
|
}
|
|
|
|
#endif // EK3_FEATURE_OPTFLOW_AGL_KF
|
|
|
|
#endif // EK3_FEATURE_OPTFLOW_FUSION
|