#include #include "AP_NavEKF3.h" #include "AP_NavEKF3_core.h" #include #include #include #include #include #include extern const AP_HAL::HAL& hal; /******************************************************** * OPT FLOW AND RANGE FINDER * ********************************************************/ // Read the range finder and take new measurements if available // Apply a median filter void NavEKF3_core::readRangeFinder(void) { uint8_t midIndex; uint8_t maxIndex; uint8_t minIndex; // get theoretical correct range when the vehicle is on the ground // don't allow range to go below 5cm because this can cause problems with optical flow processing rngOnGnd = MAX(frontend->_rng.ground_clearance_cm_orient(ROTATION_PITCH_270) * 0.01f, 0.05f); // limit update rate to maximum allowed by data buffers if ((imuSampleTime_ms - lastRngMeasTime_ms) > frontend->sensorIntervalMin_ms) { // reset the timer used to control the measurement rate lastRngMeasTime_ms = imuSampleTime_ms; // store samples and sample time into a ring buffer if valid // use data from two range finders if available for (uint8_t sensorIndex = 0; sensorIndex <= 1; sensorIndex++) { AP_RangeFinder_Backend *sensor = frontend->_rng.get_backend(sensorIndex); if (sensor == nullptr) { continue; } if ((sensor->orientation() == ROTATION_PITCH_270) && (sensor->status() == RangeFinder::RangeFinder_Good)) { rngMeasIndex[sensorIndex] ++; if (rngMeasIndex[sensorIndex] > 2) { rngMeasIndex[sensorIndex] = 0; } storedRngMeasTime_ms[sensorIndex][rngMeasIndex[sensorIndex]] = imuSampleTime_ms - 25; storedRngMeas[sensorIndex][rngMeasIndex[sensorIndex]] = sensor->distance_cm() * 0.01f; } // check for three fresh samples bool sampleFresh[2][3] = {}; for (uint8_t index = 0; index <= 2; index++) { sampleFresh[sensorIndex][index] = (imuSampleTime_ms - storedRngMeasTime_ms[sensorIndex][index]) < 500; } // find the median value if we have three fresh samples if (sampleFresh[sensorIndex][0] && sampleFresh[sensorIndex][1] && sampleFresh[sensorIndex][2]) { if (storedRngMeas[sensorIndex][0] > storedRngMeas[sensorIndex][1]) { minIndex = 1; maxIndex = 0; } else { minIndex = 0; maxIndex = 1; } if (storedRngMeas[sensorIndex][2] > storedRngMeas[sensorIndex][maxIndex]) { midIndex = maxIndex; } else if (storedRngMeas[sensorIndex][2] < storedRngMeas[sensorIndex][minIndex]) { midIndex = minIndex; } else { midIndex = 2; } // don't allow time to go backwards if (storedRngMeasTime_ms[sensorIndex][midIndex] > rangeDataNew.time_ms) { rangeDataNew.time_ms = storedRngMeasTime_ms[sensorIndex][midIndex]; } // limit the measured range to be no less than the on-ground range rangeDataNew.rng = MAX(storedRngMeas[sensorIndex][midIndex],rngOnGnd); // get position in body frame for the current sensor rangeDataNew.sensor_idx = sensorIndex; // write data to buffer with time stamp to be fused when the fusion time horizon catches up with it storedRange.push(rangeDataNew); // indicate we have updated the measurement rngValidMeaTime_ms = imuSampleTime_ms; } else if (!takeOffDetected && ((imuSampleTime_ms - rngValidMeaTime_ms) > 200)) { // before takeoff we assume on-ground range value if there is no data rangeDataNew.time_ms = imuSampleTime_ms; rangeDataNew.rng = rngOnGnd; rangeDataNew.time_ms = imuSampleTime_ms; // don't allow time to go backwards if (imuSampleTime_ms > rangeDataNew.time_ms) { rangeDataNew.time_ms = imuSampleTime_ms; } // write data to buffer with time stamp to be fused when the fusion time horizon catches up with it storedRange.push(rangeDataNew); // indicate we have updated the measurement rngValidMeaTime_ms = imuSampleTime_ms; } } } } void NavEKF3_core::writeBodyFrameOdom(float quality, const Vector3f &delPos, const Vector3f &delAng, float delTime, uint32_t timeStamp_ms, const Vector3f &posOffset) { // limit update rate to maximum allowed by sensor buffers and fusion process // don't try to write to buffer until the filter has been initialised if (((timeStamp_ms - bodyOdmMeasTime_ms) < frontend->sensorIntervalMin_ms) || (delTime < dtEkfAvg) || !statesInitialised) { return; } bodyOdmDataNew.body_offset = &posOffset; bodyOdmDataNew.vel = delPos * (1.0f/delTime); bodyOdmDataNew.time_ms = timeStamp_ms; bodyOdmDataNew.angRate = delAng * (1.0f/delTime); bodyOdmMeasTime_ms = timeStamp_ms; // simple model of accuracy // TODO move this calculation outside of EKF into the sensor driver bodyOdmDataNew.velErr = frontend->_visOdmVelErrMin + (frontend->_visOdmVelErrMax - frontend->_visOdmVelErrMin) * (1.0f - 0.01f * quality); storedBodyOdm.push(bodyOdmDataNew); } void NavEKF3_core::writeWheelOdom(float delAng, float delTime, uint32_t timeStamp_ms, const Vector3f &posOffset, float radius) { // This is a simple hack to get wheel encoder data into the EKF and verify the interface sign conventions and units // It uses the exisiting body frame velocity fusion. // TODO implement a dedicated wheel odometry observation model // limit update rate to maximum allowed by sensor buffers and fusion process // don't try to write to buffer until the filter has been initialised if (((timeStamp_ms - wheelOdmMeasTime_ms) < frontend->sensorIntervalMin_ms) || (delTime < dtEkfAvg) || !statesInitialised) { return; } wheelOdmDataNew.hub_offset = &posOffset; wheelOdmDataNew.delAng = delAng; wheelOdmDataNew.radius = radius; wheelOdmDataNew.delTime = delTime; wheelOdmMeasTime_ms = timeStamp_ms; // because we are currently converting to an equivalent velocity measurement before fusing // the measurement time is moved back to the middle of the sampling period wheelOdmDataNew.time_ms = timeStamp_ms - (uint32_t)(500.0f * delTime); storedWheelOdm.push(wheelOdmDataNew); } // write the raw optical flow measurements // this needs to be called externally. void NavEKF3_core::writeOptFlowMeas(const uint8_t rawFlowQuality, const Vector2f &rawFlowRates, const Vector2f &rawGyroRates, const uint32_t msecFlowMeas, const Vector3f &posOffset) { // limit update rate to maximum allowed by sensor buffers if ((imuSampleTime_ms - flowMeaTime_ms) < frontend->sensorIntervalMin_ms) { return; } // 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; } // by definition if this function is called, then flow measurements have been provided so we // need to run the optical flow takeoff detection detectOptFlowTakeoff(); // calculate rotation matrices at mid sample time for flow observations stateStruct.quat.rotation_matrix(Tbn_flow); // 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 body rates for bias and write ofDataNew.bodyRadXYZ.x = rawGyroRates.x - flowGyroBias.x; ofDataNew.bodyRadXYZ.y = rawGyroRates.y - flowGyroBias.y; // the sensor interface doesn't provide a z axis rate so use the rate from the nav sensor instead if (delTimeOF > 0.001f) { // first preference is to use the rate averaged over the same sampling period as the flow sensor ofDataNew.bodyRadXYZ.z = delAngBodyOF.z / delTimeOF; } else if (imuDataNew.delAngDT > 0.001f){ // second preference is to use most recent IMU data ofDataNew.bodyRadXYZ.z = imuDataNew.delAng.z / imuDataNew.delAngDT; } else { // third preference is use zero ofDataNew.bodyRadXYZ.z = 0.0f; } // write uncorrected flow rate measurements // 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 the flow sensor position in body frame ofDataNew.body_offset = &posOffset; // write flow rate measurements corrected for body rates ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + ofDataNew.bodyRadXYZ.x; ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + ofDataNew.bodyRadXYZ.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; // Correct for the average intersampling delay due to the filter updaterate ofDataNew.time_ms -= localFilterTimeStep_ms/2; // 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 storedOF.push(ofDataNew); } } /******************************************************** * MAGNETOMETER * ********************************************************/ // check for new magnetometer data and update store measurements if available void NavEKF3_core::readMagData() { if (!_ahrs->get_compass()) { allMagSensorsFailed = true; return; } // If we are a vehicle with a sideslip constraint to aid yaw estimation and we have timed out on our last avialable // magnetometer, then declare the magnetometers as failed for this flight uint8_t maxCount = _ahrs->get_compass()->get_count(); if (allMagSensorsFailed || (magTimeout && assume_zero_sideslip() && magSelectIndex >= maxCount-1 && inFlight)) { allMagSensorsFailed = true; return; } if (_ahrs->get_compass()->learn_offsets_enabled()) { // while learning offsets keep all mag states reset InitialiseVariablesMag(); wasLearningCompass_ms = imuSampleTime_ms; } else if (wasLearningCompass_ms != 0 && imuSampleTime_ms - wasLearningCompass_ms > 1000) { wasLearningCompass_ms = 0; // force a new yaw alignment 1s after learning completes. The // delay is to ensure any buffered mag samples are discarded yawAlignComplete = false; InitialiseVariablesMag(); } // limit compass update rate 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) > 1000 * frontend->sensorIntervalMin_ms)) { frontend->logging.log_compass = true; // If the magnetometer has timed out (been rejected too long) we find another magnetometer to use if available // Don't do this if we are on the ground because there can be magnetic interference and we need to know if there is a problem // before taking off. Don't do this within the first 30 seconds from startup because the yaw error could be due to large yaw gyro bias affsets if (magTimeout && (maxCount > 1) && !onGround && imuSampleTime_ms - ekfStartTime_ms > 30000) { // search through the list of magnetometers for (uint8_t i=1; i= maxCount) { tempIndex -= maxCount; } // if the magnetometer is allowed to be used for yaw and has a different index, we start using it if (_ahrs->get_compass()->use_for_yaw(tempIndex) && tempIndex != magSelectIndex) { magSelectIndex = tempIndex; gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u switching to compass %u",(unsigned)imu_index,magSelectIndex); // reset the timeout flag and timer magTimeout = false; lastHealthyMagTime_ms = imuSampleTime_ms; // zero the learned magnetometer bias states stateStruct.body_magfield.zero(); // clear the measurement buffer storedMag.reset(); // clear the data waiting flag so that we do not use any data pending from the previous sensor magDataToFuse = false; // request a reset of the magnetic field states magStateResetRequest = true; // declare the field unlearned so that the reset request will be obeyed magFieldLearned = false; break; } } } // detect changes to magnetometer offset parameters and reset states Vector3f nowMagOffsets = _ahrs->get_compass()->get_offsets(magSelectIndex); bool changeDetected = lastMagOffsetsValid && (nowMagOffsets != lastMagOffsets); if (changeDetected) { // zero the learned magnetometer bias states stateStruct.body_magfield.zero(); // clear the measurement buffer storedMag.reset(); } lastMagOffsets = nowMagOffsets; lastMagOffsetsValid = true; // store time of last measurement update lastMagUpdate_us = _ahrs->get_compass()->last_update_usec(magSelectIndex); // estimate of time magnetometer measurement was taken, allowing for delays magDataNew.time_ms = imuSampleTime_ms - frontend->magDelay_ms; // Correct for the average intersampling delay due to the filter updaterate magDataNew.time_ms -= localFilterTimeStep_ms/2; // read compass data and scale to improve numerical conditioning magDataNew.mag = _ahrs->get_compass()->get_field(magSelectIndex) * 0.001f; // check for consistent data between magnetometers consistentMagData = _ahrs->get_compass()->consistent(); // save magnetometer measurement to buffer to be fused later storedMag.push(magDataNew); } } /******************************************************** * Inertial Measurements * ********************************************************/ /* * Read IMU delta angle and delta velocity measurements and downsample to 100Hz * for storage in the data buffers used by the EKF. If the IMU data arrives at * lower rate than 100Hz, then no downsampling or upsampling will be performed. * Downsampling is done using a method that does not introduce coning or sculling * errors. */ void NavEKF3_core::readIMUData() { const AP_InertialSensor &ins = AP::ins(); // calculate an averaged IMU update rate using a spike and lowpass filter combination dtIMUavg = 0.02f * constrain_float(ins.get_loop_delta_t(),0.5f * dtIMUavg, 2.0f * dtIMUavg) + 0.98f * dtIMUavg; // the imu sample time is used as a common time reference throughout the filter imuSampleTime_ms = frontend->imuSampleTime_us / 1000; uint8_t accel_active, gyro_active; if (ins.use_accel(imu_index)) { accel_active = imu_index; } else { accel_active = ins.get_primary_accel(); } if (ins.use_gyro(imu_index)) { gyro_active = imu_index; } else { gyro_active = ins.get_primary_gyro(); } if (gyro_active != gyro_index_active) { // we are switching active gyro at runtime. Copy over the // bias we have learned from the previously inactive // gyro. We don't re-init the bias uncertainty as it should // have the same uncertainty as the previously active gyro stateStruct.gyro_bias = inactiveBias[gyro_active].gyro_bias; gyro_index_active = gyro_active; } if (accel_active != accel_index_active) { // switch to the learned accel bias for this IMU stateStruct.accel_bias = inactiveBias[accel_active].accel_bias; accel_index_active = accel_active; } // update the inactive bias states learnInactiveBiases(); readDeltaVelocity(accel_index_active, imuDataNew.delVel, imuDataNew.delVelDT); accelPosOffset = ins.get_imu_pos_offset(accel_index_active); imuDataNew.accel_index = accel_index_active; // Get delta angle data from primary gyro or primary if not available readDeltaAngle(gyro_index_active, imuDataNew.delAng); imuDataNew.delAngDT = MAX(ins.get_delta_angle_dt(gyro_index_active),1.0e-4f); imuDataNew.gyro_index = gyro_index_active; // Get current time stamp imuDataNew.time_ms = imuSampleTime_ms; // Accumulate the measurement time interval for the delta velocity and angle data imuDataDownSampledNew.delAngDT += imuDataNew.delAngDT; imuDataDownSampledNew.delVelDT += imuDataNew.delVelDT; // use the most recent IMU index for the downsampled IMU // data. This isn't strictly correct if we switch IMUs between // samples imuDataDownSampledNew.gyro_index = imuDataNew.gyro_index; imuDataDownSampledNew.accel_index = imuDataNew.accel_index; // Rotate quaternon atitude from previous to new and normalise. // Accumulation using quaternions prevents introduction of coning errors due to downsampling imuQuatDownSampleNew.rotate(imuDataNew.delAng); imuQuatDownSampleNew.normalize(); // Rotate the latest delta velocity into body frame at the start of accumulation Matrix3f deltaRotMat; imuQuatDownSampleNew.rotation_matrix(deltaRotMat); // Apply the delta velocity to the delta velocity accumulator imuDataDownSampledNew.delVel += deltaRotMat*imuDataNew.delVel; // Keep track of the number of IMU frames since the last state prediction framesSincePredict++; /* * If the target EKF time step has been accumulated, and the frontend has allowed start of a new predict cycle, * then store the accumulated IMU data to be used by the state prediction, ignoring the frontend permission if more * than twice the target time has lapsed. Adjust the target EKF step time threshold to allow for timing jitter in the * IMU data. */ if ((imuDataDownSampledNew.delAngDT >= (EKF_TARGET_DT-(dtIMUavg*0.5f)) && startPredictEnabled) || (imuDataDownSampledNew.delAngDT >= 2.0f*EKF_TARGET_DT)) { // convert the accumulated quaternion to an equivalent delta angle imuQuatDownSampleNew.to_axis_angle(imuDataDownSampledNew.delAng); // Time stamp the data imuDataDownSampledNew.time_ms = imuSampleTime_ms; // Write data to the FIFO IMU buffer storedIMU.push_youngest_element(imuDataDownSampledNew); // calculate the achieved average time step rate for the EKF using a combination spike and LPF float dtNow = constrain_float(0.5f*(imuDataDownSampledNew.delAngDT+imuDataDownSampledNew.delVelDT),0.5f * dtEkfAvg, 2.0f * dtEkfAvg); dtEkfAvg = 0.98f * dtEkfAvg + 0.02f * dtNow; // zero the accumulated IMU data and quaternion imuDataDownSampledNew.delAng.zero(); imuDataDownSampledNew.delVel.zero(); imuDataDownSampledNew.delAngDT = 0.0f; imuDataDownSampledNew.delVelDT = 0.0f; imuQuatDownSampleNew[0] = 1.0f; imuQuatDownSampleNew[3] = imuQuatDownSampleNew[2] = imuQuatDownSampleNew[1] = 0.0f; // reset the counter used to let the frontend know how many frames have elapsed since we started a new update cycle framesSincePredict = 0; // set the flag to let the filter know it has new IMU data and needs to run runUpdates = true; // extract the oldest available data from the FIFO buffer imuDataDelayed = storedIMU.pop_oldest_element(); // protect against delta time going to zero float minDT = 0.1f * dtEkfAvg; imuDataDelayed.delAngDT = MAX(imuDataDelayed.delAngDT,minDT); imuDataDelayed.delVelDT = MAX(imuDataDelayed.delVelDT,minDT); updateTimingStatistics(); // correct the extracted IMU data for sensor errors delAngCorrected = imuDataDelayed.delAng; delVelCorrected = imuDataDelayed.delVel; correctDeltaAngle(delAngCorrected, imuDataDelayed.delAngDT, imuDataDelayed.gyro_index); correctDeltaVelocity(delVelCorrected, imuDataDelayed.delVelDT, imuDataDelayed.accel_index); } else { // we don't have new IMU data in the buffer so don't run filter updates on this time step runUpdates = false; } } // read the delta velocity and corresponding time interval from the IMU // return false if data is not available bool NavEKF3_core::readDeltaVelocity(uint8_t ins_index, Vector3f &dVel, float &dVel_dt) { const AP_InertialSensor &ins = AP::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 NavEKF3_core::readGpsData() { // check for new GPS data // limit update rate to avoid overflowing the FIFO buffer const AP_GPS &gps = AP::gps(); if (gps.last_message_time_ms() - lastTimeGpsReceived_ms > frontend->sensorIntervalMin_ms) { if (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 = 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 // Use the driver specified delay float gps_delay_sec = 0; gps.get_lag(gps_delay_sec); gpsDataNew.time_ms = lastTimeGpsReceived_ms - (uint32_t)(gps_delay_sec * 1000.0f); // Correct for the average intersampling delay due to the filter updaterate gpsDataNew.time_ms -= localFilterTimeStep_ms/2; // Prevent the time stamp falling outside the oldest and newest IMU data in the buffer gpsDataNew.time_ms = MIN(MAX(gpsDataNew.time_ms,imuDataDelayed.time_ms),imuDataDownSampledNew.time_ms); // Get which GPS we are using for position information gpsDataNew.sensor_idx = gps.primary_sensor(); // read the NED velocity from the GPS gpsDataNew.vel = gps.velocity(); // Use the speed and position 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 accuracy data float alpha = constrain_float(0.0002f * (lastTimeGpsReceived_ms - secondLastGpsTime_ms),0.0f,1.0f); gpsSpdAccuracy *= (1.0f - alpha); float gpsSpdAccRaw; if (!gps.speed_accuracy(gpsSpdAccRaw)) { gpsSpdAccuracy = 0.0f; } else { gpsSpdAccuracy = MAX(gpsSpdAccuracy,gpsSpdAccRaw); gpsSpdAccuracy = MIN(gpsSpdAccuracy,50.0f); } gpsPosAccuracy *= (1.0f - alpha); float gpsPosAccRaw; if (!gps.horizontal_accuracy(gpsPosAccRaw)) { gpsPosAccuracy = 0.0f; } else { gpsPosAccuracy = MAX(gpsPosAccuracy,gpsPosAccRaw); gpsPosAccuracy = MIN(gpsPosAccuracy,100.0f); } gpsHgtAccuracy *= (1.0f - alpha); float gpsHgtAccRaw; if (!gps.vertical_accuracy(gpsHgtAccRaw)) { gpsHgtAccuracy = 0.0f; } else { gpsHgtAccuracy = MAX(gpsHgtAccuracy,gpsHgtAccRaw); gpsHgtAccuracy = MIN(gpsHgtAccuracy,100.0f); } // check if we have enough GPS satellites and increase the gps noise scaler if we don't if (gps.num_sats() >= 6 && (PV_AidingMode == AID_ABSOLUTE)) { gpsNoiseScaler = 1.0f; } else if (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, vertical velocity use is permitted and set GPS fusion mode accordingly if (gps.have_vertical_velocity() && (frontend->_fusionModeGPS == 0) && !frontend->inhibitGpsVertVelUse) { useGpsVertVel = true; } else { useGpsVertVel = false; } // Monitor quality of the GPS velocity data before and after alignment calcGpsGoodToAlign(); // Post-alignment checks calcGpsGoodForFlight(); // see if we can get an origin from the frontend if (!validOrigin && frontend->common_origin_valid) { setOrigin(frontend->common_EKF_origin); } // Read the GPS location in WGS-84 lat,long,height coordinates const struct Location &gpsloc = gps.location(); // Set the EKF origin and magnetic field declination if not previously set and GPS checks have passed if (gpsGoodToAlign && !validOrigin) { setOrigin(gpsloc); // set the NE earth magnetic field states using the published declination // and set the corresponding variances and covariances alignMagStateDeclination(); // Set the height of the NED origin ekfGpsRefHgt = (double)0.01 * (double)gpsloc.alt + (double)outputDataNew.position.z; // Set the uncertainty of the GPS origin height ekfOriginHgtVar = sq(gpsHgtAccuracy); } // convert GPS measurements to local NED and save to buffer to be fused later if we have a valid origin if (validOrigin) { gpsDataNew.pos = EKF_origin.get_distance_NE(gpsloc); if ((frontend->_originHgtMode & (1<<2)) == 0) { gpsDataNew.hgt = (float)((double)0.01 * (double)gpsloc.alt - ekfGpsRefHgt); } else { gpsDataNew.hgt = 0.01 * (gpsloc.alt - EKF_origin.alt); } storedGPS.push(gpsDataNew); // declare GPS available for use gpsNotAvailable = false; } // if the GPS has yaw data then input that as well float yaw_deg, yaw_accuracy_deg; if (AP::gps().gps_yaw_deg(yaw_deg, yaw_accuracy_deg)) { // GPS modules are rather too optimistic about their // accuracy. Set to min of 5 degrees here to prevent // the user constantly receiving warnings about high // normalised yaw innovations const float min_yaw_accuracy_deg = 5.0f; yaw_accuracy_deg = MAX(yaw_accuracy_deg, min_yaw_accuracy_deg); writeEulerYawAngle(radians(yaw_deg), radians(yaw_accuracy_deg), gpsDataNew.time_ms, 2); } } else { // report GPS fix status gpsCheckStatus.bad_fix = true; hal.util->snprintf(prearm_fail_string, sizeof(prearm_fail_string), "Waiting for 3D fix"); } } } // read the delta angle and corresponding time interval from the IMU // return false if data is not available bool NavEKF3_core::readDeltaAngle(uint8_t ins_index, Vector3f &dAng) { const AP_InertialSensor &ins = AP::ins(); if (ins_index < ins.get_gyro_count()) { ins.get_delta_angle(ins_index,dAng); frontend->logging.log_imu = true; return true; } return false; } /******************************************************** * Height Measurements * ********************************************************/ // check for new pressure altitude measurement data and update stored measurement if available void NavEKF3_core::readBaroData() { // check to see if baro measurement has changed so we know if a new measurement has arrived // limit update rate to avoid overflowing the FIFO buffer const AP_Baro &baro = AP::baro(); if (baro.get_last_update() - lastBaroReceived_ms > frontend->sensorIntervalMin_ms) { frontend->logging.log_baro = true; baroDataNew.hgt = baro.get_altitude(); // 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 (getTakeoffExpected()) { baroDataNew.hgt = MAX(baroDataNew.hgt, meaHgtAtTakeOff); } // time stamp used to check for new measurement lastBaroReceived_ms = baro.get_last_update(); // estimate of time height measurement was taken, allowing for delays baroDataNew.time_ms = lastBaroReceived_ms - frontend->_hgtDelay_ms; // Correct for the average intersampling delay due to the filter updaterate baroDataNew.time_ms -= localFilterTimeStep_ms/2; // 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 storedBaro.push(baroDataNew); } } // calculate filtered offset between baro height measurement and EKF height estimate // offset should be subtracted from baro measurement to match filter estimate // offset is used to enable reversion to baro from alternate height data source void NavEKF3_core::calcFiltBaroOffset() { // Apply a first order LPF with spike protection baroHgtOffset += 0.1f * constrain_float(baroDataDelayed.hgt + stateStruct.position.z - baroHgtOffset, -5.0f, 5.0f); } // correct the height of the EKF origin to be consistent with GPS Data using a Bayes filter. void NavEKF3_core::correctEkfOriginHeight() { // Estimate the WGS-84 height of the EKF's origin using a Bayes filter // calculate the variance of our a-priori estimate of the ekf origin height float deltaTime = constrain_float(0.001f * (imuDataDelayed.time_ms - lastOriginHgtTime_ms), 0.0f, 1.0f); if (activeHgtSource == HGT_SOURCE_BARO) { // Use the baro drift rate const float baroDriftRate = 0.05f; ekfOriginHgtVar += sq(baroDriftRate * deltaTime); } else if (activeHgtSource == HGT_SOURCE_RNG) { // use the worse case expected terrain gradient and vehicle horizontal speed const float maxTerrGrad = 0.25f; ekfOriginHgtVar += sq(maxTerrGrad * norm(stateStruct.velocity.x , stateStruct.velocity.y) * deltaTime); } else { // by definition our height source is absolute so cannot run this filter return; } lastOriginHgtTime_ms = imuDataDelayed.time_ms; // calculate the observation variance assuming EKF error relative to datum is independent of GPS observation error // when not using GPS as height source float originHgtObsVar = sq(gpsHgtAccuracy) + P[9][9]; // calculate the correction gain float gain = ekfOriginHgtVar / (ekfOriginHgtVar + originHgtObsVar); // calculate the innovation float innovation = - stateStruct.position.z - gpsDataDelayed.hgt; // check the innovation variance ratio float ratio = sq(innovation) / (ekfOriginHgtVar + originHgtObsVar); // correct the EKF origin and variance estimate if the innovation is less than 5-sigma if (ratio < 25.0f && gpsAccuracyGood) { ekfGpsRefHgt -= (double)(gain * innovation); ekfOriginHgtVar -= MAX(gain * ekfOriginHgtVar , 0.0f); } } /******************************************************** * Air Speed Measurements * ********************************************************/ // check for new airspeed data and update stored measurements if available void NavEKF3_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) > frontend->sensorIntervalMin_ms) { tasDataNew.tas = aspeed->get_raw_airspeed() * AP::ahrs().get_EAS2TAS(); timeTasReceived_ms = aspeed->last_update_ms(); tasDataNew.time_ms = timeTasReceived_ms - frontend->tasDelay_ms; // Correct for the average intersampling delay due to the filter update rate tasDataNew.time_ms -= localFilterTimeStep_ms/2; // Save data into the buffer to be fused when the fusion time horizon catches up with it storedTAS.push(tasDataNew); } // Check the buffer for measurements that have been overtaken by the fusion time horizon and need to be fused tasDataToFuse = storedTAS.recall(tasDataDelayed,imuDataDelayed.time_ms); } /******************************************************** * Range Beacon Measurements * ********************************************************/ // check for new range beacon data and push to data buffer if available void NavEKF3_core::readRngBcnData() { // get the location of the beacon data const AP_Beacon *beacon = AP::beacon(); // exit immediately if no beacon object if (beacon == nullptr) { return; } // get the number of beacons in use N_beacons = beacon->count(); // search through all the beacons for new data and if we find it stop searching and push the data into the observation buffer bool newDataToPush = false; uint8_t numRngBcnsChecked = 0; // start the search one index up from where we left it last time uint8_t index = lastRngBcnChecked; while (!newDataToPush && numRngBcnsChecked < N_beacons) { // track the number of beacons checked numRngBcnsChecked++; // move to next beacon, wrap index if necessary index++; if (index >= N_beacons) { index = 0; } // check that the beacon is healthy and has new data if (beacon->beacon_healthy(index) && beacon->beacon_last_update_ms(index) != lastTimeRngBcn_ms[index]) { // set the timestamp, correcting for measurement delay and average intersampling delay due to the filter update rate lastTimeRngBcn_ms[index] = beacon->beacon_last_update_ms(index); rngBcnDataNew.time_ms = lastTimeRngBcn_ms[index] - frontend->_rngBcnDelay_ms - localFilterTimeStep_ms/2; // set the range noise // TODO the range library should provide the noise/accuracy estimate for each beacon rngBcnDataNew.rngErr = frontend->_rngBcnNoise; // set the range measurement rngBcnDataNew.rng = beacon->beacon_distance(index); // set the beacon position rngBcnDataNew.beacon_posNED = beacon->beacon_position(index); // identify the beacon identifier rngBcnDataNew.beacon_ID = index; // indicate we have new data to push to the buffer newDataToPush = true; // update the last checked index lastRngBcnChecked = index; } } // Check if the beacon system has returned a 3D fix if (beacon->get_vehicle_position_ned(beaconVehiclePosNED, beaconVehiclePosErr)) { rngBcnLast3DmeasTime_ms = imuSampleTime_ms; } // Check if the range beacon data can be used to align the vehicle position if (imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250 && beaconVehiclePosErr < 1.0f && rngBcnAlignmentCompleted) { // check for consistency between the position reported by the beacon and the position from the 3-State alignment filter const float posDiffSq = sq(receiverPos.x - beaconVehiclePosNED.x) + sq(receiverPos.y - beaconVehiclePosNED.y); const float posDiffVar = sq(beaconVehiclePosErr) + receiverPosCov[0][0] + receiverPosCov[1][1]; if (posDiffSq < 9.0f * posDiffVar) { rngBcnGoodToAlign = true; // Set the EKF origin and magnetic field declination if not previously set if (!validOrigin && PV_AidingMode != AID_ABSOLUTE) { // get origin from beacon system Location origin_loc; if (beacon->get_origin(origin_loc)) { setOriginLLH(origin_loc); // set the NE earth magnetic field states using the published declination // and set the corresponding variances and covariances alignMagStateDeclination(); // Set the uncertainty of the origin height ekfOriginHgtVar = sq(beaconVehiclePosErr); } } } else { rngBcnGoodToAlign = false; } } else { rngBcnGoodToAlign = false; } // Save data into the buffer to be fused when the fusion time horizon catches up with it if (newDataToPush) { storedRangeBeacon.push(rngBcnDataNew); } // Check the buffer for measurements that have been overtaken by the fusion time horizon and need to be fused rngBcnDataToFuse = storedRangeBeacon.recall(rngBcnDataDelayed, imuDataDelayed.time_ms); // Correct the range beacon earth frame origin for estimated offset relative to the EKF earth frame origin if (rngBcnDataToFuse) { rngBcnDataDelayed.beacon_posNED.x += bcnPosOffsetNED.x; rngBcnDataDelayed.beacon_posNED.y += bcnPosOffsetNED.y; } } /******************************************************** * Independant yaw sensor measurements * ********************************************************/ void NavEKF3_core::writeEulerYawAngle(float yawAngle, float yawAngleErr, uint32_t timeStamp_ms, uint8_t type) { // limit update rate to maximum allowed by sensor buffers and fusion process // don't try to write to buffer until the filter has been initialised if (((timeStamp_ms - yawMeasTime_ms) < frontend->sensorIntervalMin_ms) || !statesInitialised) { return; } yawAngDataNew.yawAng = yawAngle; yawAngDataNew.yawAngErr = yawAngleErr; yawAngDataNew.type = type; yawAngDataNew.time_ms = timeStamp_ms; storedYawAng.push(yawAngDataNew); yawMeasTime_ms = timeStamp_ms; } /* update timing statistics structure */ void NavEKF3_core::updateTimingStatistics(void) { if (timing.count == 0) { timing.dtIMUavg_max = dtIMUavg; timing.dtIMUavg_min = dtIMUavg; timing.dtEKFavg_max = dtEkfAvg; timing.dtEKFavg_min = dtEkfAvg; timing.delAngDT_max = imuDataDelayed.delAngDT; timing.delAngDT_min = imuDataDelayed.delAngDT; timing.delVelDT_max = imuDataDelayed.delVelDT; timing.delVelDT_min = imuDataDelayed.delVelDT; } else { timing.dtIMUavg_max = MAX(timing.dtIMUavg_max, dtIMUavg); timing.dtIMUavg_min = MIN(timing.dtIMUavg_min, dtIMUavg); timing.dtEKFavg_max = MAX(timing.dtEKFavg_max, dtEkfAvg); timing.dtEKFavg_min = MIN(timing.dtEKFavg_min, dtEkfAvg); timing.delAngDT_max = MAX(timing.delAngDT_max, imuDataDelayed.delAngDT); timing.delAngDT_min = MIN(timing.delAngDT_min, imuDataDelayed.delAngDT); timing.delVelDT_max = MAX(timing.delVelDT_max, imuDataDelayed.delVelDT); timing.delVelDT_min = MIN(timing.delVelDT_min, imuDataDelayed.delVelDT); } timing.count++; } // get timing statistics structure void NavEKF3_core::getTimingStatistics(struct ekf_timing &_timing) { _timing = timing; memset(&timing, 0, sizeof(timing)); } /* update estimates of inactive bias states. This keeps inactive IMUs as hot-spares so we can switch to them without causing a jump in the error */ void NavEKF3_core::learnInactiveBiases(void) { const AP_InertialSensor &ins = AP::ins(); // learn gyro biases for (uint8_t i=0; i