mirror of https://github.com/ArduPilot/ardupilot
580 lines
27 KiB
C++
580 lines
27 KiB
C++
/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-
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#include <AP_HAL/AP_HAL.h>
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#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
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#include "AP_NavEKF2.h"
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#include "AP_NavEKF2_core.h"
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#include <AP_AHRS/AP_AHRS.h>
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#include <AP_Vehicle/AP_Vehicle.h>
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#include <stdio.h>
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extern const AP_HAL::HAL& hal;
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/********************************************************
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* OPT FLOW AND RANGE FINDER *
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********************************************************/
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// Read the range finder and take new measurements if available
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// Apply a median filter
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void NavEKF2_core::readRangeFinder(void)
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{
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uint8_t midIndex;
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uint8_t maxIndex;
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uint8_t minIndex;
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// get theoretical correct range when the vehicle is on the ground
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rngOnGnd = frontend->_rng.ground_clearance_cm() * 0.01f;
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// read range finder at 20Hz
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// TODO better way of knowing if it has new data
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if ((imuSampleTime_ms - lastRngMeasTime_ms) > 50) {
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// reset the timer used to control the measurement rate
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lastRngMeasTime_ms = imuSampleTime_ms;
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// store samples and sample time into a ring buffer if valid
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if (frontend->_rng.status() == RangeFinder::RangeFinder_Good) {
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rngMeasIndex ++;
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if (rngMeasIndex > 2) {
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rngMeasIndex = 0;
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}
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storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms - 25;
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storedRngMeas[rngMeasIndex] = frontend->_rng.distance_cm() * 0.01f;
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}
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// check for three fresh samples
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bool sampleFresh[3];
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for (uint8_t index = 0; index <= 2; index++) {
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sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500;
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}
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// find the median value if we have three fresh samples
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if (sampleFresh[0] && sampleFresh[1] && sampleFresh[2]) {
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if (storedRngMeas[0] > storedRngMeas[1]) {
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minIndex = 1;
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maxIndex = 0;
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} else {
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maxIndex = 0;
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minIndex = 1;
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}
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if (storedRngMeas[2] > storedRngMeas[maxIndex]) {
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midIndex = maxIndex;
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} else if (storedRngMeas[2] < storedRngMeas[minIndex]) {
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midIndex = minIndex;
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} else {
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midIndex = 2;
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}
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rangeDataNew.time_ms = storedRngMeasTime_ms[midIndex];
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// limit the measured range to be no less than the on-ground range
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rangeDataNew.rng = MAX(storedRngMeas[midIndex],rngOnGnd);
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rngValidMeaTime_ms = imuSampleTime_ms;
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// write data to buffer with time stamp to be fused when the fusion time horizon catches up with it
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storedRange.push(rangeDataNew);
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} else if (!takeOffDetected) {
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// before takeoff we assume on-ground range value if there is no data
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rangeDataNew.time_ms = imuSampleTime_ms;
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rangeDataNew.rng = rngOnGnd;
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rngValidMeaTime_ms = imuSampleTime_ms;
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// write data to buffer with time stamp to be fused when the fusion time horizon catches up with it
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storedRange.push(rangeDataNew);
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}
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}
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}
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// write the raw optical flow measurements
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// this needs to be called externally.
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void NavEKF2_core::writeOptFlowMeas(uint8_t &rawFlowQuality, Vector2f &rawFlowRates, Vector2f &rawGyroRates, uint32_t &msecFlowMeas)
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{
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// The raw measurements need to be optical flow rates in radians/second averaged across the time since the last update
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// The PX4Flow sensor outputs flow rates with the following axis and sign conventions:
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// A positive X rate is produced by a positive sensor rotation about the X axis
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// A positive Y rate is produced by a positive sensor rotation about the Y axis
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// This filter uses a different definition of optical flow rates to the sensor with a positive optical flow rate produced by a
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// 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
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flowMeaTime_ms = imuSampleTime_ms;
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// calculate bias errors on flow sensor gyro rates, but protect against spikes in data
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// reset the accumulated body delta angle and time
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// don't do the calculation if not enough time lapsed for a reliable body rate measurement
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if (delTimeOF > 0.01f) {
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flowGyroBias.x = 0.99f * flowGyroBias.x + 0.01f * constrain_float((rawGyroRates.x - delAngBodyOF.x/delTimeOF),-0.1f,0.1f);
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flowGyroBias.y = 0.99f * flowGyroBias.y + 0.01f * constrain_float((rawGyroRates.y - delAngBodyOF.y/delTimeOF),-0.1f,0.1f);
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delAngBodyOF.zero();
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delTimeOF = 0.0f;
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}
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// check for takeoff if relying on optical flow and zero measurements until takeoff detected
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// if we haven't taken off - constrain position and velocity states
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if (frontend->_fusionModeGPS == 3) {
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detectOptFlowTakeoff();
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}
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// calculate rotation matrices at mid sample time for flow observations
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stateStruct.quat.rotation_matrix(Tbn_flow);
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Tnb_flow = Tbn_flow.transposed();
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// don't use data with a low quality indicator or extreme rates (helps catch corrupt sensor data)
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if ((rawFlowQuality > 0) && rawFlowRates.length() < 4.2f && rawGyroRates.length() < 4.2f) {
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// correct flow sensor rates for bias
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omegaAcrossFlowTime.x = rawGyroRates.x - flowGyroBias.x;
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omegaAcrossFlowTime.y = rawGyroRates.y - flowGyroBias.y;
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// write uncorrected flow rate measurements that will be used by the focal length scale factor estimator
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// note correction for different axis and sign conventions used by the px4flow sensor
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ofDataNew.flowRadXY = - rawFlowRates; // raw (non motion compensated) optical flow angular rate about the X axis (rad/sec)
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// write flow rate measurements corrected for body rates
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ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + omegaAcrossFlowTime.x;
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ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + omegaAcrossFlowTime.y;
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// record time last observation was received so we can detect loss of data elsewhere
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flowValidMeaTime_ms = imuSampleTime_ms;
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// estimate sample time of the measurement
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ofDataNew.time_ms = imuSampleTime_ms - frontend->_flowDelay_ms - frontend->flowTimeDeltaAvg_ms/2;
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// Correct for the average intersampling delay due to the filter updaterate
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ofDataNew.time_ms -= localFilterTimeStep_ms/2;
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// Prevent time delay exceeding age of oldest IMU data in the buffer
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ofDataNew.time_ms = MAX(ofDataNew.time_ms,imuDataDelayed.time_ms);
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// Save data to buffer
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storedOF.push(ofDataNew);
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// Check for data at the fusion time horizon
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flowDataToFuse = storedOF.recall(ofDataDelayed, imuDataDelayed.time_ms);
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}
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}
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/********************************************************
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* MAGNETOMETER *
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********************************************************/
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// check for new magnetometer data and update store measurements if available
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void NavEKF2_core::readMagData()
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{
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// If we are a vehicle with a sideslip constraint to aid yaw estimation and we have timed out on our last avialable
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// magnetometer, then declare the magnetometers as failed for this flight
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uint8_t maxCount = _ahrs->get_compass()->get_count();
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if (allMagSensorsFailed || (magTimeout && assume_zero_sideslip() && magSelectIndex >= maxCount-1 && inFlight)) {
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allMagSensorsFailed = true;
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return;
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}
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// do not accept new compass data faster than 14Hz (nominal rate is 10Hz) to prevent high processor loading
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// because magnetometer fusion is an expensive step and we could overflow the FIFO buffer
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if (use_compass() && _ahrs->get_compass()->last_update_usec() - lastMagUpdate_us > 70000) {
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frontend->logging.log_compass = true;
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// If the magnetometer has timed out (been rejected too long) we find another magnetometer to use if available
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// 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
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// 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
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if (magTimeout && (maxCount > 1) && !onGround && imuSampleTime_ms - ekfStartTime_ms > 30000) {
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// search through the list of magnetometers
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for (uint8_t i=1; i<maxCount; i++) {
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uint8_t tempIndex = magSelectIndex + i;
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// loop back to the start index if we have exceeded the bounds
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if (tempIndex >= maxCount) {
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tempIndex -= maxCount;
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}
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// if the magnetometer is allowed to be used for yaw and has a different index, we start using it
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if (_ahrs->get_compass()->use_for_yaw(tempIndex) && tempIndex != magSelectIndex) {
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magSelectIndex = tempIndex;
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hal.console->printf("EKF2 IMU%u switching to compass %u\n",(unsigned)imu_index,magSelectIndex);
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// reset the timeout flag and timer
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magTimeout = false;
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lastHealthyMagTime_ms = imuSampleTime_ms;
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// zero the learned magnetometer bias states
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stateStruct.body_magfield.zero();
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// clear the measurement buffer
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storedMag.reset();
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}
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}
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}
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// detect changes to magnetometer offset parameters and reset states
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Vector3f nowMagOffsets = _ahrs->get_compass()->get_offsets(magSelectIndex);
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bool changeDetected = lastMagOffsetsValid && (nowMagOffsets != lastMagOffsets);
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if (changeDetected) {
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// zero the learned magnetometer bias states
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stateStruct.body_magfield.zero();
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// clear the measurement buffer
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storedMag.reset();
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}
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lastMagOffsets = nowMagOffsets;
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lastMagOffsetsValid = true;
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// store time of last measurement update
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lastMagUpdate_us = _ahrs->get_compass()->last_update_usec(magSelectIndex);
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// estimate of time magnetometer measurement was taken, allowing for delays
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magDataNew.time_ms = imuSampleTime_ms - frontend->magDelay_ms;
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// Correct for the average intersampling delay due to the filter updaterate
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magDataNew.time_ms -= localFilterTimeStep_ms/2;
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// read compass data and scale to improve numerical conditioning
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magDataNew.mag = _ahrs->get_compass()->get_field(magSelectIndex) * 0.001f;
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// check for consistent data between magnetometers
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consistentMagData = _ahrs->get_compass()->consistent();
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// save magnetometer measurement to buffer to be fused later
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storedMag.push(magDataNew);
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}
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}
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/********************************************************
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* Inertial Measurements *
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********************************************************/
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/*
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* Read IMU delta angle and delta velocity measurements and downsample to 100Hz
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* for storage in the data buffers used by the EKF. If the IMU data arrives at
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* lower rate than 100Hz, then no downsampling or upsampling will be performed.
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* Downsampling is done using a method that does not introduce coning or sculling
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* errors.
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*/
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void NavEKF2_core::readIMUData()
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{
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const AP_InertialSensor &ins = _ahrs->get_ins();
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// average IMU sampling rate
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dtIMUavg = ins.get_loop_delta_t();
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// the imu sample time is used as a common time reference throughout the filter
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imuSampleTime_ms = AP_HAL::millis();
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// use the nominated imu or primary if not available
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if (ins.use_accel(imu_index)) {
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readDeltaVelocity(imu_index, imuDataNew.delVel, imuDataNew.delVelDT);
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} else {
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readDeltaVelocity(ins.get_primary_accel(), imuDataNew.delVel, imuDataNew.delVelDT);
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}
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// Get delta angle data from primary gyro or primary if not available
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if (ins.use_gyro(imu_index)) {
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readDeltaAngle(imu_index, imuDataNew.delAng);
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} else {
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readDeltaAngle(ins.get_primary_gyro(), imuDataNew.delAng);
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}
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imuDataNew.delAngDT = MAX(ins.get_delta_angle_dt(imu_index),1.0e-4f);
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// Get current time stamp
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imuDataNew.time_ms = imuSampleTime_ms;
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// remove gyro scale factor errors
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imuDataNew.delAng.x = imuDataNew.delAng.x * stateStruct.gyro_scale.x;
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imuDataNew.delAng.y = imuDataNew.delAng.y * stateStruct.gyro_scale.y;
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imuDataNew.delAng.z = imuDataNew.delAng.z * stateStruct.gyro_scale.z;
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// remove sensor bias errors
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imuDataNew.delAng -= stateStruct.gyro_bias * (imuDataNew.delAngDT / dtEkfAvg);
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imuDataNew.delVel.z -= stateStruct.accel_zbias * (imuDataNew.delVelDT / dtEkfAvg);
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// Accumulate the measurement time interval for the delta velocity and angle data
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imuDataDownSampledNew.delAngDT += imuDataNew.delAngDT;
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imuDataDownSampledNew.delVelDT += imuDataNew.delVelDT;
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// Rotate quaternon atitude from previous to new and normalise.
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// Accumulation using quaternions prevents introduction of coning errors due to downsampling
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Quaternion deltaQuat;
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deltaQuat.rotate(imuDataNew.delAng);
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imuQuatDownSampleNew = imuQuatDownSampleNew*deltaQuat;
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imuQuatDownSampleNew.normalize();
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// Rotate the accumulated delta velocity into the new frame of reference created by the latest delta angle
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// This prevents introduction of sculling errors due to downsampling
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Matrix3f deltaRotMat;
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deltaQuat.inverse().rotation_matrix(deltaRotMat);
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imuDataDownSampledNew.delVel = deltaRotMat*imuDataDownSampledNew.delVel;
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// accumulate the latest delta velocity
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imuDataDownSampledNew.delVel += imuDataNew.delVel;
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// Keep track of the number of IMU frames since the last state prediction
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framesSincePredict++;
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// If 10msec has elapsed, and the frontend has allowed us to start a new predict cycle, then store the accumulated IMU data
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// to be used by the state prediction, ignoring the frontend permission if more than 20msec has lapsed
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if ((dtIMUavg*(float)framesSincePredict >= 0.01f && startPredictEnabled) || (dtIMUavg*(float)framesSincePredict >= 0.02f)) {
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// convert the accumulated quaternion to an equivalent delta angle
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imuQuatDownSampleNew.to_axis_angle(imuDataDownSampledNew.delAng);
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// Time stamp the data
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imuDataDownSampledNew.time_ms = imuSampleTime_ms;
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// Write data to the FIFO IMU buffer
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storedIMU.push_youngest_element(imuDataDownSampledNew);
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// zero the accumulated IMU data and quaternion
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imuDataDownSampledNew.delAng.zero();
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imuDataDownSampledNew.delVel.zero();
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imuDataDownSampledNew.delAngDT = 0.0f;
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imuDataDownSampledNew.delVelDT = 0.0f;
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imuQuatDownSampleNew[0] = 1.0f;
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imuQuatDownSampleNew[3] = imuQuatDownSampleNew[2] = imuQuatDownSampleNew[1] = 0.0f;
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// reset the counter used to let the frontend know how many frames have elapsed since we started a new update cycle
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framesSincePredict = 0;
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// set the flag to let the filter know it has new IMU data nad needs to run
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runUpdates = true;
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} else {
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// we don't have new IMU data in the buffer so don't run filter updates on this time step
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runUpdates = false;
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}
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// extract the oldest available data from the FIFO buffer
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imuDataDelayed = storedIMU.pop_oldest_element();
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float minDT = 0.1f*dtEkfAvg;
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imuDataDelayed.delAngDT = MAX(imuDataDelayed.delAngDT,minDT);
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imuDataDelayed.delVelDT = MAX(imuDataDelayed.delVelDT,minDT);
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}
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// read the delta velocity and corresponding time interval from the IMU
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// return false if data is not available
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bool NavEKF2_core::readDeltaVelocity(uint8_t ins_index, Vector3f &dVel, float &dVel_dt) {
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const AP_InertialSensor &ins = _ahrs->get_ins();
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if (ins_index < ins.get_accel_count()) {
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ins.get_delta_velocity(ins_index,dVel);
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dVel_dt = MAX(ins.get_delta_velocity_dt(ins_index),1.0e-4f);
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return true;
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}
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return false;
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}
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/********************************************************
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* Global Position Measurement *
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********************************************************/
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// check for new valid GPS data and update stored measurement if available
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void NavEKF2_core::readGpsData()
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{
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// check for new GPS data
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// do not accept data at a faster rate than 14Hz to avoid overflowing the FIFO buffer
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if (_ahrs->get_gps().last_message_time_ms() - lastTimeGpsReceived_ms > 70) {
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if (_ahrs->get_gps().status() >= AP_GPS::GPS_OK_FIX_3D) {
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// report GPS fix status
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gpsCheckStatus.bad_fix = false;
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// store fix time from previous read
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secondLastGpsTime_ms = lastTimeGpsReceived_ms;
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// get current fix time
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lastTimeGpsReceived_ms = _ahrs->get_gps().last_message_time_ms();
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// estimate when the GPS fix was valid, allowing for GPS processing and other delays
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// ideally we should be using a timing signal from the GPS receiver to set this time
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gpsDataNew.time_ms = lastTimeGpsReceived_ms - frontend->_gpsDelay_ms;
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// Correct for the average intersampling delay due to the filter updaterate
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gpsDataNew.time_ms -= localFilterTimeStep_ms/2;
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// Prevent time delay exceeding age of oldest IMU data in the buffer
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gpsDataNew.time_ms = MAX(gpsDataNew.time_ms,imuDataDelayed.time_ms);
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// read the NED velocity from the GPS
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gpsDataNew.vel = _ahrs->get_gps().velocity();
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// Use the speed accuracy from the GPS if available, otherwise set it to zero.
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// Apply a decaying envelope filter with a 5 second time constant to the raw speed accuracy data
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float alpha = constrain_float(0.0002f * (lastTimeGpsReceived_ms - secondLastGpsTime_ms),0.0f,1.0f);
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gpsSpdAccuracy *= (1.0f - alpha);
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float gpsSpdAccRaw;
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if (!_ahrs->get_gps().speed_accuracy(gpsSpdAccRaw)) {
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gpsSpdAccuracy = 0.0f;
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} else {
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gpsSpdAccuracy = MAX(gpsSpdAccuracy,gpsSpdAccRaw);
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}
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// check if we have enough GPS satellites and increase the gps noise scaler if we don't
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if (_ahrs->get_gps().num_sats() >= 6 && (PV_AidingMode == AID_ABSOLUTE)) {
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gpsNoiseScaler = 1.0f;
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} else if (_ahrs->get_gps().num_sats() == 5 && (PV_AidingMode == AID_ABSOLUTE)) {
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gpsNoiseScaler = 1.4f;
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} else { // <= 4 satellites or in constant position mode
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gpsNoiseScaler = 2.0f;
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}
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// Check if GPS can output vertical velocity and set GPS fusion mode accordingly
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if (_ahrs->get_gps().have_vertical_velocity() && frontend->_fusionModeGPS == 0) {
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useGpsVertVel = true;
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} else {
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useGpsVertVel = false;
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}
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// Monitor quality of the GPS velocity data before and after alignment using separate checks
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if (PV_AidingMode != AID_ABSOLUTE) {
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// Pre-alignment checks
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gpsGoodToAlign = calcGpsGoodToAlign();
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} else {
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// Post-alignment checks
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calcGpsGoodForFlight();
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}
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// Read the GPS locaton in WGS-84 lat,long,height coordinates
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const struct Location &gpsloc = _ahrs->get_gps().location();
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// Set the EKF origin and magnetic field declination if not previously set and GPS checks have passed
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if (gpsGoodToAlign && !validOrigin) {
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setOrigin();
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// 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();
|
||
}
|
||
|
||
frontend->logging.log_gps = true;
|
||
} 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);
|
||
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 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) {
|
||
frontend->logging.log_baro = true;
|
||
|
||
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
|