mirror of https://github.com/ArduPilot/ardupilot
714 lines
31 KiB
C++
714 lines
31 KiB
C++
/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-
|
||
|
||
#include <AP_HAL/AP_HAL.h>
|
||
|
||
#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
|
||
#include "AP_NavEKF2.h"
|
||
#include "AP_NavEKF2_core.h"
|
||
#include <AP_AHRS/AP_AHRS.h>
|
||
#include <AP_Vehicle/AP_Vehicle.h>
|
||
|
||
#include <stdio.h>
|
||
|
||
extern const AP_HAL::HAL& hal;
|
||
|
||
|
||
/********************************************************
|
||
* OPT FLOW AND RANGE FINDER *
|
||
********************************************************/
|
||
|
||
// Read the range finder and take new measurements if available
|
||
// Read at 20Hz and apply a median filter
|
||
void NavEKF2_core::readRangeFinder(void)
|
||
{
|
||
uint8_t midIndex;
|
||
uint8_t maxIndex;
|
||
uint8_t minIndex;
|
||
// get theoretical correct range when the vehicle is on the ground
|
||
rngOnGnd = frontend->_rng.ground_clearance_cm() * 0.01f;
|
||
if (frontend->_rng.status() == RangeFinder::RangeFinder_Good && (imuSampleTime_ms - lastRngMeasTime_ms) > 50) {
|
||
// store samples and sample time into a ring buffer
|
||
rngMeasIndex ++;
|
||
if (rngMeasIndex > 2) {
|
||
rngMeasIndex = 0;
|
||
}
|
||
storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms;
|
||
storedRngMeas[rngMeasIndex] = frontend->_rng.distance_cm() * 0.01f;
|
||
// check for three fresh samples and take median
|
||
bool sampleFresh[3];
|
||
for (uint8_t index = 0; index <= 2; index++) {
|
||
sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500;
|
||
}
|
||
if (sampleFresh[0] && sampleFresh[1] && sampleFresh[2]) {
|
||
if (storedRngMeas[0] > storedRngMeas[1]) {
|
||
minIndex = 1;
|
||
maxIndex = 0;
|
||
} else {
|
||
maxIndex = 0;
|
||
minIndex = 1;
|
||
}
|
||
if (storedRngMeas[2] > storedRngMeas[maxIndex]) {
|
||
midIndex = maxIndex;
|
||
} else if (storedRngMeas[2] < storedRngMeas[minIndex]) {
|
||
midIndex = minIndex;
|
||
} else {
|
||
midIndex = 2;
|
||
}
|
||
rngMea = max(storedRngMeas[midIndex],rngOnGnd);
|
||
newDataRng = true;
|
||
rngValidMeaTime_ms = imuSampleTime_ms;
|
||
} else if (onGround) {
|
||
// if on ground and no return, we assume on ground range
|
||
rngMea = rngOnGnd;
|
||
newDataRng = true;
|
||
rngValidMeaTime_ms = imuSampleTime_ms;
|
||
} else {
|
||
newDataRng = false;
|
||
}
|
||
lastRngMeasTime_ms = imuSampleTime_ms;
|
||
}
|
||
}
|
||
|
||
// write the raw optical flow measurements
|
||
// this needs to be called externally.
|
||
void NavEKF2_core::writeOptFlowMeas(uint8_t &rawFlowQuality, Vector2f &rawFlowRates, Vector2f &rawGyroRates, uint32_t &msecFlowMeas)
|
||
{
|
||
// The raw measurements need to be optical flow rates in radians/second averaged across the time since the last update
|
||
// The PX4Flow sensor outputs flow rates with the following axis and sign conventions:
|
||
// A positive X rate is produced by a positive sensor rotation about the X axis
|
||
// A positive Y rate is produced by a positive sensor rotation about the Y axis
|
||
// This filter uses a different definition of optical flow rates to the sensor with a positive optical flow rate produced by a
|
||
// negative rotation about that axis. For example a positive rotation of the flight vehicle about its X (roll) axis would produce a negative X flow rate
|
||
flowMeaTime_ms = imuSampleTime_ms;
|
||
// calculate bias errors on flow sensor gyro rates, but protect against spikes in data
|
||
// reset the accumulated body delta angle and time
|
||
// don't do the calculation if not enough time lapsed for a reliable body rate measurement
|
||
if (delTimeOF > 0.01f) {
|
||
flowGyroBias.x = 0.99f * flowGyroBias.x + 0.01f * constrain_float((rawGyroRates.x - delAngBodyOF.x/delTimeOF),-0.1f,0.1f);
|
||
flowGyroBias.y = 0.99f * flowGyroBias.y + 0.01f * constrain_float((rawGyroRates.y - delAngBodyOF.y/delTimeOF),-0.1f,0.1f);
|
||
delAngBodyOF.zero();
|
||
delTimeOF = 0.0f;
|
||
}
|
||
// check for takeoff if relying on optical flow and zero measurements until takeoff detected
|
||
// if we haven't taken off - constrain position and velocity states
|
||
if (frontend->_fusionModeGPS == 3) {
|
||
detectOptFlowTakeoff();
|
||
}
|
||
// calculate rotation matrices at mid sample time for flow observations
|
||
stateStruct.quat.rotation_matrix(Tbn_flow);
|
||
Tnb_flow = Tbn_flow.transposed();
|
||
// don't use data with a low quality indicator or extreme rates (helps catch corrupt sensor data)
|
||
if ((rawFlowQuality > 0) && rawFlowRates.length() < 4.2f && rawGyroRates.length() < 4.2f) {
|
||
// correct flow sensor rates for bias
|
||
omegaAcrossFlowTime.x = rawGyroRates.x - flowGyroBias.x;
|
||
omegaAcrossFlowTime.y = rawGyroRates.y - flowGyroBias.y;
|
||
// write uncorrected flow rate measurements that will be used by the focal length scale factor estimator
|
||
// note correction for different axis and sign conventions used by the px4flow sensor
|
||
ofDataNew.flowRadXY = - rawFlowRates; // raw (non motion compensated) optical flow angular rate about the X axis (rad/sec)
|
||
// write flow rate measurements corrected for body rates
|
||
ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + omegaAcrossFlowTime.x;
|
||
ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + omegaAcrossFlowTime.y;
|
||
// record time last observation was received so we can detect loss of data elsewhere
|
||
flowValidMeaTime_ms = imuSampleTime_ms;
|
||
// estimate sample time of the measurement
|
||
ofDataNew.time_ms = imuSampleTime_ms - frontend->_flowDelay_ms - frontend->flowTimeDeltaAvg_ms/2;
|
||
// Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
|
||
// This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
|
||
ofDataNew.time_ms = roundToNearest(ofDataNew.time_ms, frontend->fusionTimeStep_ms);
|
||
// Prevent time delay exceeding age of oldest IMU data in the buffer
|
||
ofDataNew.time_ms = max(ofDataNew.time_ms,imuDataDelayed.time_ms);
|
||
// Save data to buffer
|
||
StoreOF();
|
||
// Check for data at the fusion time horizon
|
||
newDataFlow = RecallOF();
|
||
}
|
||
}
|
||
|
||
// store OF data in a history array
|
||
void NavEKF2_core::StoreOF()
|
||
{
|
||
if (ofStoreIndex >= OBS_BUFFER_LENGTH) {
|
||
ofStoreIndex = 0;
|
||
}
|
||
storedOF[ofStoreIndex] = ofDataNew;
|
||
ofStoreIndex += 1;
|
||
}
|
||
|
||
// return newest un-used optical flow data that has fallen behind the fusion time horizon
|
||
// if no un-used data is available behind the fusion horizon, return false
|
||
bool NavEKF2_core::RecallOF()
|
||
{
|
||
of_elements dataTemp;
|
||
of_elements dataTempZero;
|
||
dataTempZero.time_ms = 0;
|
||
uint32_t temp_ms = 0;
|
||
uint8_t bestIndex = 0;
|
||
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
|
||
dataTemp = storedOF[i];
|
||
// find a measurement older than the fusion time horizon that we haven't checked before
|
||
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
|
||
// Find the most recent non-stale measurement that meets the time horizon criteria
|
||
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
|
||
ofDataDelayed = dataTemp;
|
||
temp_ms = dataTemp.time_ms;
|
||
bestIndex = i;
|
||
}
|
||
}
|
||
}
|
||
if (temp_ms != 0) {
|
||
// zero the time stamp for that piece of data so we won't use it again
|
||
storedOF[bestIndex]=dataTempZero;
|
||
return true;
|
||
} else {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
|
||
|
||
/********************************************************
|
||
* MAGNETOMETER *
|
||
********************************************************/
|
||
|
||
// return magnetometer offsets
|
||
// return true if offsets are valid
|
||
bool NavEKF2_core::getMagOffsets(Vector3f &magOffsets) const
|
||
{
|
||
// compass offsets are valid if we have finalised magnetic field initialisation and magnetic field learning is not prohibited and primary compass is valid
|
||
if (firstMagYawInit && (frontend->_magCal != 2) && _ahrs->get_compass()->healthy(0)) {
|
||
magOffsets = _ahrs->get_compass()->get_offsets(0) - stateStruct.body_magfield*1000.0f;
|
||
return true;
|
||
} else {
|
||
magOffsets = _ahrs->get_compass()->get_offsets(0);
|
||
return false;
|
||
}
|
||
}
|
||
|
||
// check for new magnetometer data and update store measurements if available
|
||
void NavEKF2_core::readMagData()
|
||
{
|
||
// do not accept new compass data faster than 14Hz (nominal rate is 10Hz) to prevent high processor loading
|
||
// because magnetometer fusion is an expensive step and we could overflow the FIFO buffer
|
||
if (use_compass() && _ahrs->get_compass()->last_update_usec() - lastMagUpdate_us > 70000) {
|
||
// store time of last measurement update
|
||
lastMagUpdate_us = _ahrs->get_compass()->last_update_usec();
|
||
|
||
// estimate of time magnetometer measurement was taken, allowing for delays
|
||
magDataNew.time_ms = imuSampleTime_ms - frontend->magDelay_ms;
|
||
|
||
// Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
|
||
// This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
|
||
magDataNew.time_ms = roundToNearest(magDataNew.time_ms, frontend->fusionTimeStep_ms);
|
||
|
||
// read compass data and scale to improve numerical conditioning
|
||
magDataNew.mag = _ahrs->get_compass()->get_field() * 0.001f;
|
||
|
||
// check for consistent data between magnetometers
|
||
consistentMagData = _ahrs->get_compass()->consistent();
|
||
|
||
// check if compass offsets have been changed and adjust EKF bias states to maintain consistent innovations
|
||
if (_ahrs->get_compass()->healthy(0)) {
|
||
Vector3f nowMagOffsets = _ahrs->get_compass()->get_offsets(0);
|
||
bool changeDetected = (!is_equal(nowMagOffsets.x,lastMagOffsets.x) || !is_equal(nowMagOffsets.y,lastMagOffsets.y) || !is_equal(nowMagOffsets.z,lastMagOffsets.z));
|
||
// Ignore bias changes before final mag field and yaw initialisation, as there may have been a compass calibration
|
||
if (changeDetected && firstMagYawInit) {
|
||
stateStruct.body_magfield.x += (nowMagOffsets.x - lastMagOffsets.x) * 0.001f;
|
||
stateStruct.body_magfield.y += (nowMagOffsets.y - lastMagOffsets.y) * 0.001f;
|
||
stateStruct.body_magfield.z += (nowMagOffsets.z - lastMagOffsets.z) * 0.001f;
|
||
}
|
||
lastMagOffsets = nowMagOffsets;
|
||
}
|
||
|
||
// save magnetometer measurement to buffer to be fused later
|
||
StoreMag();
|
||
}
|
||
}
|
||
// store magnetometer data in a history array
|
||
void NavEKF2_core::StoreMag()
|
||
{
|
||
if (magStoreIndex >= OBS_BUFFER_LENGTH) {
|
||
magStoreIndex = 0;
|
||
}
|
||
storedMag[magStoreIndex] = magDataNew;
|
||
magStoreIndex += 1;
|
||
}
|
||
|
||
// return newest un-used magnetometer data that has fallen behind the fusion time horizon
|
||
// if no un-used data is available behind the fusion horizon, return false
|
||
bool NavEKF2_core::RecallMag()
|
||
{
|
||
mag_elements dataTemp;
|
||
mag_elements dataTempZero;
|
||
dataTempZero.time_ms = 0;
|
||
uint32_t temp_ms = 0;
|
||
uint8_t bestIndex = 0;
|
||
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
|
||
dataTemp = storedMag[i];
|
||
// find a measurement older than the fusion time horizon that we haven't checked before
|
||
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
|
||
// Find the most recent non-stale measurement that meets the time horizon criteria
|
||
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
|
||
magDataDelayed = dataTemp;
|
||
temp_ms = dataTemp.time_ms;
|
||
bestIndex = i;
|
||
}
|
||
}
|
||
}
|
||
if (temp_ms != 0) {
|
||
// zero the time stamp for that piece of data so we won't use it again
|
||
storedMag[bestIndex]=dataTempZero;
|
||
return true;
|
||
} else {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
|
||
|
||
|
||
/********************************************************
|
||
* Inertial Measurements *
|
||
********************************************************/
|
||
|
||
// update IMU delta angle and delta velocity measurements
|
||
void NavEKF2_core::readIMUData()
|
||
{
|
||
const AP_InertialSensor &ins = _ahrs->get_ins();
|
||
|
||
// average IMU sampling rate
|
||
dtIMUavg = 1.0f/ins.get_sample_rate();
|
||
|
||
// the imu sample time is used as a common time reference throughout the filter
|
||
imuSampleTime_ms = hal.scheduler->millis();
|
||
|
||
// use the nominated imu or primary if not available
|
||
if (ins.use_accel(imu_index)) {
|
||
readDeltaVelocity(imu_index, imuDataNew.delVel, imuDataNew.delVelDT);
|
||
} else {
|
||
readDeltaVelocity(ins.get_primary_accel(), imuDataNew.delVel, imuDataNew.delVelDT);
|
||
}
|
||
|
||
// Get delta angle data from primary gyro or primary if not available
|
||
if (ins.use_gyro(imu_index)) {
|
||
readDeltaAngle(imu_index, imuDataNew.delAng);
|
||
} else {
|
||
readDeltaAngle(ins.get_primary_gyro(), imuDataNew.delAng);
|
||
}
|
||
imuDataNew.delAngDT = max(ins.get_delta_time(),1.0e-4f);
|
||
|
||
// get current time stamp
|
||
imuDataNew.time_ms = imuSampleTime_ms;
|
||
|
||
// save data in the FIFO buffer
|
||
StoreIMU();
|
||
|
||
// extract the oldest available data from the FIFO buffer
|
||
imuDataDelayed = storedIMU[fifoIndexDelayed];
|
||
|
||
}
|
||
|
||
// store imu in the FIFO
|
||
void NavEKF2_core::StoreIMU()
|
||
{
|
||
// increment the index and write new data
|
||
fifoIndexNow = fifoIndexNow + 1;
|
||
if (fifoIndexNow >= IMU_BUFFER_LENGTH) {
|
||
fifoIndexNow = 0;
|
||
}
|
||
storedIMU[fifoIndexNow] = imuDataNew;
|
||
// set the index required to access the oldest data, applying an offset to the fusion time horizon that is used to
|
||
// prevent the same fusion operation being performed on the same frame across multiple EKF's
|
||
fifoIndexDelayed = fifoIndexNow + 1 + fusionHorizonOffset;
|
||
if (fifoIndexDelayed >= IMU_BUFFER_LENGTH) {
|
||
fifoIndexDelayed = 0;
|
||
}
|
||
}
|
||
|
||
// reset the stored imu history and store the current value
|
||
void NavEKF2_core::StoreIMU_reset()
|
||
{
|
||
// write current measurement to entire table
|
||
for (uint8_t i=0; i<IMU_BUFFER_LENGTH; i++) {
|
||
storedIMU[i] = imuDataNew;
|
||
}
|
||
imuDataDelayed = imuDataNew;
|
||
fifoIndexDelayed = fifoIndexNow+1;
|
||
if (fifoIndexDelayed >= IMU_BUFFER_LENGTH) {
|
||
fifoIndexDelayed = 0;
|
||
}
|
||
}
|
||
|
||
// recall IMU data from the FIFO
|
||
void NavEKF2_core::RecallIMU()
|
||
{
|
||
imuDataDelayed = storedIMU[fifoIndexDelayed];
|
||
// make sure that the delta time used for the delta angles and velocities are is no less than 10% of dtIMUavg to prevent
|
||
// divide by zero problems when converting to rates or acceleration
|
||
float minDT = 0.1f*dtIMUavg;
|
||
imuDataDelayed.delAngDT = max(imuDataDelayed.delAngDT,minDT);
|
||
imuDataDelayed.delVelDT = max(imuDataDelayed.delVelDT,minDT);
|
||
}
|
||
|
||
// read the delta velocity and corresponding time interval from the IMU
|
||
// return false if data is not available
|
||
bool NavEKF2_core::readDeltaVelocity(uint8_t ins_index, Vector3f &dVel, float &dVel_dt) {
|
||
const AP_InertialSensor &ins = _ahrs->get_ins();
|
||
|
||
if (ins_index < ins.get_accel_count()) {
|
||
ins.get_delta_velocity(ins_index,dVel);
|
||
dVel_dt = max(ins.get_delta_velocity_dt(ins_index),1.0e-4f);
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
/********************************************************
|
||
* Global Position Measurement *
|
||
********************************************************/
|
||
|
||
// check for new valid GPS data and update stored measurement if available
|
||
void NavEKF2_core::readGpsData()
|
||
{
|
||
// check for new GPS data
|
||
// do not accept data at a faster rate than 14Hz to avoid overflowing the FIFO buffer
|
||
if (_ahrs->get_gps().last_message_time_ms() - lastTimeGpsReceived_ms > 70) {
|
||
if (_ahrs->get_gps().status() >= AP_GPS::GPS_OK_FIX_3D) {
|
||
// report GPS fix status
|
||
gpsCheckStatus.bad_fix = false;
|
||
|
||
// store fix time from previous read
|
||
secondLastGpsTime_ms = lastTimeGpsReceived_ms;
|
||
|
||
// get current fix time
|
||
lastTimeGpsReceived_ms = _ahrs->get_gps().last_message_time_ms();
|
||
|
||
// estimate when the GPS fix was valid, allowing for GPS processing and other delays
|
||
// ideally we should be using a timing signal from the GPS receiver to set this time
|
||
gpsDataNew.time_ms = lastTimeGpsReceived_ms - frontend->_gpsDelay_ms;
|
||
|
||
// Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
|
||
// This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
|
||
gpsDataNew.time_ms = roundToNearest(gpsDataNew.time_ms, frontend->fusionTimeStep_ms);
|
||
|
||
// Prevent time delay exceeding age of oldest IMU data in the buffer
|
||
gpsDataNew.time_ms = max(gpsDataNew.time_ms,imuDataDelayed.time_ms);
|
||
|
||
// read the NED velocity from the GPS
|
||
gpsDataNew.vel = _ahrs->get_gps().velocity();
|
||
|
||
// Use the speed accuracy from the GPS if available, otherwise set it to zero.
|
||
// Apply a decaying envelope filter with a 5 second time constant to the raw speed accuracy data
|
||
float alpha = constrain_float(0.0002f * (lastTimeGpsReceived_ms - secondLastGpsTime_ms),0.0f,1.0f);
|
||
gpsSpdAccuracy *= (1.0f - alpha);
|
||
float gpsSpdAccRaw;
|
||
if (!_ahrs->get_gps().speed_accuracy(gpsSpdAccRaw)) {
|
||
gpsSpdAccuracy = 0.0f;
|
||
} else {
|
||
gpsSpdAccuracy = max(gpsSpdAccuracy,gpsSpdAccRaw);
|
||
}
|
||
|
||
// check if we have enough GPS satellites and increase the gps noise scaler if we don't
|
||
if (_ahrs->get_gps().num_sats() >= 6 && (PV_AidingMode == AID_ABSOLUTE)) {
|
||
gpsNoiseScaler = 1.0f;
|
||
} else if (_ahrs->get_gps().num_sats() == 5 && (PV_AidingMode == AID_ABSOLUTE)) {
|
||
gpsNoiseScaler = 1.4f;
|
||
} else { // <= 4 satellites or in constant position mode
|
||
gpsNoiseScaler = 2.0f;
|
||
}
|
||
|
||
// Check if GPS can output vertical velocity and set GPS fusion mode accordingly
|
||
if (_ahrs->get_gps().have_vertical_velocity() && frontend->_fusionModeGPS == 0) {
|
||
useGpsVertVel = true;
|
||
} else {
|
||
useGpsVertVel = false;
|
||
}
|
||
|
||
// Monitor quality of the GPS velocity data before and after alignment using separate checks
|
||
if (PV_AidingMode != AID_ABSOLUTE) {
|
||
// Pre-alignment checks
|
||
gpsGoodToAlign = calcGpsGoodToAlign();
|
||
} else {
|
||
// Post-alignment checks
|
||
calcGpsGoodForFlight();
|
||
}
|
||
|
||
// Read the GPS locaton in WGS-84 lat,long,height coordinates
|
||
const struct Location &gpsloc = _ahrs->get_gps().location();
|
||
|
||
// Set the EKF origin and magnetic field declination if not previously set and GPS checks have passed
|
||
if (gpsGoodToAlign && !validOrigin) {
|
||
setOrigin();
|
||
// Now we know the location we have an estimate for the magnetic field declination and adjust the earth field accordingly
|
||
alignMagStateDeclination();
|
||
// Set the height of the NED origin to ‘height of baro height datum relative to GPS height datum'
|
||
EKF_origin.alt = gpsloc.alt - baroDataNew.hgt;
|
||
}
|
||
|
||
// convert GPS measurements to local NED and save to buffer to be fused later if we have a valid origin
|
||
if (validOrigin) {
|
||
gpsDataNew.pos = location_diff(EKF_origin, gpsloc);
|
||
StoreGPS();
|
||
// declare GPS available for use
|
||
gpsNotAvailable = false;
|
||
}
|
||
|
||
// Commence GPS aiding when able to
|
||
if (readyToUseGPS() && PV_AidingMode != AID_ABSOLUTE) {
|
||
PV_AidingMode = AID_ABSOLUTE;
|
||
// Initialise EKF position and velocity states to last GPS measurement
|
||
ResetPosition();
|
||
ResetVelocity();
|
||
}
|
||
|
||
} else {
|
||
// report GPS fix status
|
||
gpsCheckStatus.bad_fix = true;
|
||
}
|
||
}
|
||
|
||
// We need to handle the case where GPS is lost for a period of time that is too long to dead-reckon
|
||
// If that happens we need to put the filter into a constant position mode, reset the velocity states to zero
|
||
// and use the last estimated position as a synthetic GPS position
|
||
|
||
// check if we can use opticalflow as a backup
|
||
bool optFlowBackupAvailable = (flowDataValid && !hgtTimeout);
|
||
|
||
// Set GPS time-out threshold depending on whether we have an airspeed sensor to constrain drift
|
||
uint16_t gpsRetryTimeout_ms = useAirspeed() ? frontend->gpsRetryTimeUseTAS_ms : frontend->gpsRetryTimeNoTAS_ms;
|
||
|
||
// Set the time that copters will fly without a GPS lock before failing the GPS and switching to a non GPS mode
|
||
uint16_t gpsFailTimeout_ms = optFlowBackupAvailable ? frontend->gpsFailTimeWithFlow_ms : gpsRetryTimeout_ms;
|
||
|
||
// If we haven't received GPS data for a while and we are using it for aiding, then declare the position and velocity data as being timed out
|
||
if (imuSampleTime_ms - lastTimeGpsReceived_ms > gpsFailTimeout_ms) {
|
||
|
||
// Let other processes know that GPS is not available and that a timeout has occurred
|
||
posTimeout = true;
|
||
velTimeout = true;
|
||
gpsNotAvailable = true;
|
||
|
||
// If we are totally reliant on GPS for navigation, then we need to switch to a non-GPS mode of operation
|
||
// If we don't have airspeed or sideslip assumption or optical flow to constrain drift, then go into constant position mode.
|
||
// If we can do optical flow nav (valid flow data and height above ground estimate), then go into flow nav mode.
|
||
if (PV_AidingMode == AID_ABSOLUTE && !useAirspeed() && !assume_zero_sideslip()) {
|
||
if (optFlowBackupAvailable) {
|
||
// we can do optical flow only nav
|
||
frontend->_fusionModeGPS = 3;
|
||
PV_AidingMode = AID_RELATIVE;
|
||
} else {
|
||
// store the current position
|
||
lastKnownPositionNE.x = stateStruct.position.x;
|
||
lastKnownPositionNE.y = stateStruct.position.y;
|
||
|
||
// put the filter into constant position mode
|
||
PV_AidingMode = AID_NONE;
|
||
|
||
// Reset the velocity and position states
|
||
ResetVelocity();
|
||
ResetPosition();
|
||
|
||
// Reset the normalised innovation to avoid false failing bad fusion tests
|
||
velTestRatio = 0.0f;
|
||
posTestRatio = 0.0f;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
// store GPS data in a history array
|
||
void NavEKF2_core::StoreGPS()
|
||
{
|
||
if (gpsStoreIndex >= OBS_BUFFER_LENGTH) {
|
||
gpsStoreIndex = 0;
|
||
}
|
||
storedGPS[gpsStoreIndex] = gpsDataNew;
|
||
gpsStoreIndex += 1;
|
||
}
|
||
|
||
// return newest un-used GPS data that has fallen behind the fusion time horizon
|
||
// if no un-used data is available behind the fusion horizon, return false
|
||
bool NavEKF2_core::RecallGPS()
|
||
{
|
||
gps_elements dataTemp;
|
||
gps_elements dataTempZero;
|
||
dataTempZero.time_ms = 0;
|
||
uint32_t temp_ms = 0;
|
||
uint8_t bestIndex;
|
||
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
|
||
dataTemp = storedGPS[i];
|
||
// find a measurement older than the fusion time horizon that we haven't checked before
|
||
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
|
||
// Find the most recent non-stale measurement that meets the time horizon criteria
|
||
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
|
||
gpsDataDelayed = dataTemp;
|
||
temp_ms = dataTemp.time_ms;
|
||
bestIndex = i;
|
||
}
|
||
}
|
||
}
|
||
if (temp_ms != 0) {
|
||
// zero the time stamp for that piece of data so we won't use it again
|
||
storedGPS[bestIndex]=dataTempZero;
|
||
return true;
|
||
} else {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
// read the delta angle and corresponding time interval from the IMU
|
||
// return false if data is not available
|
||
bool NavEKF2_core::readDeltaAngle(uint8_t ins_index, Vector3f &dAng) {
|
||
const AP_InertialSensor &ins = _ahrs->get_ins();
|
||
|
||
if (ins_index < ins.get_gyro_count()) {
|
||
ins.get_delta_angle(ins_index,dAng);
|
||
return true;
|
||
}
|
||
return false;
|
||
}
|
||
|
||
|
||
/********************************************************
|
||
* Height Measurements *
|
||
********************************************************/
|
||
|
||
// check for new altitude measurement data and update stored measurement if available
|
||
void NavEKF2_core::readHgtData()
|
||
{
|
||
// check to see if baro measurement has changed so we know if a new measurement has arrived
|
||
// do not accept data at a faster rate than 14Hz to avoid overflowing the FIFO buffer
|
||
if (frontend->_baro.get_last_update() - lastHgtReceived_ms > 70) {
|
||
// Don't use Baro height if operating in optical flow mode as we use range finder instead
|
||
if (frontend->_fusionModeGPS == 3 && frontend->_altSource == 1) {
|
||
if ((imuSampleTime_ms - rngValidMeaTime_ms) < 2000) {
|
||
// adjust range finder measurement to allow for effect of vehicle tilt and height of sensor
|
||
baroDataNew.hgt = max(rngMea * Tnb_flow.c.z, rngOnGnd);
|
||
// calculate offset to baro data that enables baro to be used as a backup
|
||
// filter offset to reduce effect of baro noise and other transient errors on estimate
|
||
baroHgtOffset = 0.1f * (frontend->_baro.get_altitude() + stateStruct.position.z) + 0.9f * baroHgtOffset;
|
||
} else if (isAiding && takeOffDetected) {
|
||
// we have lost range finder measurements and are in optical flow flight
|
||
// use baro measurement and correct for baro offset - failsafe use only as baro will drift
|
||
baroDataNew.hgt = max(frontend->_baro.get_altitude() - baroHgtOffset, rngOnGnd);
|
||
} else {
|
||
// If we are on ground and have no range finder reading, assume the nominal on-ground height
|
||
baroDataNew.hgt = rngOnGnd;
|
||
// calculate offset to baro data that enables baro to be used as a backup
|
||
// filter offset to reduce effect of baro noise and other transient errors on estimate
|
||
baroHgtOffset = 0.1f * (frontend->_baro.get_altitude() + stateStruct.position.z) + 0.9f * baroHgtOffset;
|
||
}
|
||
} else {
|
||
// Normal operation is to use baro measurement
|
||
baroDataNew.hgt = frontend->_baro.get_altitude();
|
||
}
|
||
|
||
// 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;
|
||
} else if (isAiding && getTakeoffExpected()) {
|
||
// 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
|
||
baroDataNew.hgt = max(baroDataNew.hgt, meaHgtAtTakeOff);
|
||
}
|
||
|
||
// time stamp used to check for new measurement
|
||
lastHgtReceived_ms = frontend->_baro.get_last_update();
|
||
|
||
// estimate of time height measurement was taken, allowing for delays
|
||
baroDataNew.time_ms = lastHgtReceived_ms - frontend->_hgtDelay_ms;
|
||
|
||
// Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
|
||
// This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
|
||
baroDataNew.time_ms = roundToNearest(baroDataNew.time_ms, frontend->fusionTimeStep_ms);
|
||
|
||
// Prevent time delay exceeding age of oldest IMU data in the buffer
|
||
baroDataNew.time_ms = max(baroDataNew.time_ms,imuDataDelayed.time_ms);
|
||
|
||
// save baro measurement to buffer to be fused later
|
||
StoreBaro();
|
||
}
|
||
}
|
||
|
||
// store baro in a history array
|
||
void NavEKF2_core::StoreBaro()
|
||
{
|
||
if (baroStoreIndex >= OBS_BUFFER_LENGTH) {
|
||
baroStoreIndex = 0;
|
||
}
|
||
storedBaro[baroStoreIndex] = baroDataNew;
|
||
baroStoreIndex += 1;
|
||
}
|
||
|
||
// return newest un-used baro data that has fallen behind the fusion time horizon
|
||
// if no un-used data is available behind the fusion horizon, return false
|
||
bool NavEKF2_core::RecallBaro()
|
||
{
|
||
baro_elements dataTemp;
|
||
baro_elements dataTempZero;
|
||
dataTempZero.time_ms = 0;
|
||
uint32_t temp_ms = 0;
|
||
uint8_t bestIndex = 0;
|
||
for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
|
||
dataTemp = storedBaro[i];
|
||
// find a measurement older than the fusion time horizon that we haven't checked before
|
||
if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
|
||
// Find the most recent non-stale measurement that meets the time horizon criteria
|
||
if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
|
||
baroDataDelayed = dataTemp;
|
||
temp_ms = dataTemp.time_ms;
|
||
bestIndex = i;
|
||
}
|
||
}
|
||
}
|
||
if (temp_ms != 0) {
|
||
// zero the time stamp for that piece of data so we won't use it again
|
||
storedBaro[bestIndex]=dataTempZero;
|
||
return true;
|
||
} else {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
|
||
|
||
/********************************************************
|
||
* Air Speed Measurements *
|
||
********************************************************/
|
||
|
||
// check for new airspeed data and update stored measurements if available
|
||
void NavEKF2_core::readAirSpdData()
|
||
{
|
||
// if airspeed reading is valid and is set by the user to be used and has been updated then
|
||
// we take a new reading, convert from EAS to TAS and set the flag letting other functions
|
||
// know a new measurement is available
|
||
const AP_Airspeed *aspeed = _ahrs->get_airspeed();
|
||
if (aspeed &&
|
||
aspeed->use() &&
|
||
aspeed->last_update_ms() != timeTasReceived_ms) {
|
||
tasDataNew.tas = aspeed->get_airspeed() * aspeed->get_EAS2TAS();
|
||
timeTasReceived_ms = aspeed->last_update_ms();
|
||
tasDataNew.time_ms = timeTasReceived_ms - frontend->tasDelay_ms;
|
||
// Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
|
||
// This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
|
||
tasDataNew.time_ms = roundToNearest(tasDataNew.time_ms, frontend->fusionTimeStep_ms);
|
||
newDataTas = true;
|
||
StoreTAS();
|
||
RecallTAS();
|
||
} else {
|
||
newDataTas = false;
|
||
}
|
||
}
|
||
|
||
// Round to the nearest multiple of a integer
|
||
uint32_t NavEKF2_core::roundToNearest(uint32_t dividend, uint32_t divisor )
|
||
{
|
||
return ((uint32_t)round((float)dividend/float(divisor)))*divisor;
|
||
}
|
||
|
||
#endif // HAL_CPU_CLASS
|