/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*- #include #if HAL_CPU_CLASS >= HAL_CPU_CLASS_150 #include "AP_NavEKF2.h" #include "AP_NavEKF2_core.h" #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 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; // read range finder at 20Hz // TODO better way of knowing if it has new data if ((imuSampleTime_ms - lastRngMeasTime_ms) > 50) { // reset the timer used to control the measurement rate lastRngMeasTime_ms = imuSampleTime_ms; // store samples and sample time into a ring buffer if valid if (frontend->_rng.status() == RangeFinder::RangeFinder_Good) { rngMeasIndex ++; if (rngMeasIndex > 2) { rngMeasIndex = 0; } storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms - 25; storedRngMeas[rngMeasIndex] = frontend->_rng.distance_cm() * 0.01f; } // check for three fresh samples bool sampleFresh[3]; for (uint8_t index = 0; index <= 2; index++) { sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500; } // find the median value if we have three fresh samples 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; } rangeDataNew.time_ms = storedRngMeasTime_ms[midIndex]; // limit the measured range to be no less than the on-ground range rangeDataNew.rng = MAX(storedRngMeas[midIndex],rngOnGnd); rngValidMeaTime_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); } else if (!takeOffDetected) { // before takeoff we assume on-ground range value if there is no data rangeDataNew.time_ms = imuSampleTime_ms; rangeDataNew.rng = rngOnGnd; rngValidMeaTime_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); } } } // 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; // 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); // Check for data at the fusion time horizon flowDataToFuse = storedOF.recall(ofDataDelayed, imuDataDelayed.time_ms); } } /******************************************************** * 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(magSelectIndex)) { magOffsets = _ahrs->get_compass()->get_offsets(magSelectIndex) - stateStruct.body_magfield*1000.0f; return true; } else { magOffsets = _ahrs->get_compass()->get_offsets(magSelectIndex); return false; } } // check for new magnetometer data and update store measurements if available void NavEKF2_core::readMagData() { // 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; } // 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) { // 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; hal.console->printf("EKF2 IMU%u switching to compass %u\n",(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(); } } } // 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 NavEKF2_core::readIMUData() { const AP_InertialSensor &ins = _ahrs->get_ins(); // average IMU sampling rate dtIMUavg = ins.get_loop_delta_t(); // the imu sample time is used as a common time reference throughout the filter imuSampleTime_ms = AP_HAL::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; // remove gyro scale factor errors imuDataNew.delAng.x = imuDataNew.delAng.x * stateStruct.gyro_scale.x; imuDataNew.delAng.y = imuDataNew.delAng.y * stateStruct.gyro_scale.y; imuDataNew.delAng.z = imuDataNew.delAng.z * stateStruct.gyro_scale.z; // remove sensor bias errors imuDataNew.delAng -= stateStruct.gyro_bias; imuDataNew.delVel.z -= stateStruct.accel_zbias; // Accumulate the measurement time interval for the delta velocity and angle data imuDataDownSampledNew.delAngDT += imuDataNew.delAngDT; imuDataDownSampledNew.delVelDT += imuDataNew.delVelDT; // Rotate quaternon atitude from previous to new and normalise. // Accumulation using quaternions prevents introduction of coning errors due to downsampling Quaternion deltaQuat; deltaQuat.rotate(imuDataNew.delAng); imuQuatDownSampleNew = imuQuatDownSampleNew*deltaQuat; imuQuatDownSampleNew.normalize(); // Rotate the accumulated delta velocity into the new frame of reference created by the latest delta angle // This prevents introduction of sculling errors due to downsampling Matrix3f deltaRotMat; deltaQuat.inverse().rotation_matrix(deltaRotMat); imuDataDownSampledNew.delVel = deltaRotMat*imuDataDownSampledNew.delVel; // accumulate the latest delta velocity imuDataDownSampledNew.delVel += imuDataNew.delVel; // Keep track of the number of IMU frames since the last state prediction framesSincePredict++; // If 10msec has elapsed, and the frontend has allowed us to start a new predict cycle, then store the accumulated IMU data // to be used by the state prediction, ignoring the frontend permission if more than 20msec has lapsed if ((dtIMUavg*(float)framesSincePredict >= 0.01f && startPredictEnabled) || (dtIMUavg*(float)framesSincePredict >= 0.02f)) { // 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); // 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 nad needs to run runUpdates = true; } else { // we don't have new IMU data in the buffer so don't run filter updates on this time step runUpdates = false; } // extract the oldest available data from the FIFO buffer imuDataDelayed = storedIMU.pop_oldest_element(); float minDT = 0.1f*dtEkfAvg; 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; // Correct for the average intersampling delay due to the filter updaterate gpsDataNew.time_ms -= localFilterTimeStep_ms/2; // 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); gpsDataNew.hgt = 0.01f * (gpsloc.alt - EKF_origin.alt); storedGPS.push(gpsDataNew); // 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; } } } } // 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 pressure altitude measurement data and update stored measurement if available void NavEKF2_core::readBaroData() { // 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() - lastBaroReceived_ms > 70) { baroDataNew.hgt = frontend->_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 (isAiding && getTakeoffExpected()) { baroDataNew.hgt = MAX(baroDataNew.hgt, meaHgtAtTakeOff); } // time stamp used to check for new measurement lastBaroReceived_ms = frontend->_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 if alternate height data sources fail void NavEKF2_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); } /******************************************************** * 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; // 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); } #endif // HAL_CPU_CLASS