mirror of https://github.com/ArduPilot/ardupilot
1038 lines
46 KiB
C++
1038 lines
46 KiB
C++
#include <AP_HAL/AP_HAL.h>
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#include "AP_NavEKF3.h"
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#include "AP_NavEKF3_core.h"
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#include <AP_AHRS/AP_AHRS.h>
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#include <AP_Vehicle/AP_Vehicle.h>
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#include <GCS_MAVLink/GCS.h>
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#include <AP_RangeFinder/RangeFinder_Backend.h>
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#include <AP_GPS/AP_GPS.h>
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#include <AP_Baro/AP_Baro.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 NavEKF3_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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// don't allow range to go below 5cm because this can cause problems with optical flow processing
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rngOnGnd = MAX(frontend->_rng.ground_clearance_cm_orient(ROTATION_PITCH_270) * 0.01f, 0.05f);
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// limit update rate to maximum allowed by data buffers
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if ((imuSampleTime_ms - lastRngMeasTime_ms) > frontend->sensorIntervalMin_ms) {
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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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// use data from two range finders if available
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for (uint8_t sensorIndex = 0; sensorIndex <= 1; sensorIndex++) {
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AP_RangeFinder_Backend *sensor = frontend->_rng.get_backend(sensorIndex);
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if (sensor == nullptr) {
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continue;
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}
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if ((sensor->orientation() == ROTATION_PITCH_270) && (sensor->status() == RangeFinder::RangeFinder_Good)) {
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rngMeasIndex[sensorIndex] ++;
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if (rngMeasIndex[sensorIndex] > 2) {
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rngMeasIndex[sensorIndex] = 0;
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}
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storedRngMeasTime_ms[sensorIndex][rngMeasIndex[sensorIndex]] = imuSampleTime_ms - 25;
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storedRngMeas[sensorIndex][rngMeasIndex[sensorIndex]] = sensor->distance_cm() * 0.01f;
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}
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// check for three fresh samples
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bool sampleFresh[2][3] = {};
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for (uint8_t index = 0; index <= 2; index++) {
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sampleFresh[sensorIndex][index] = (imuSampleTime_ms - storedRngMeasTime_ms[sensorIndex][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[sensorIndex][0] && sampleFresh[sensorIndex][1] && sampleFresh[sensorIndex][2]) {
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if (storedRngMeas[sensorIndex][0] > storedRngMeas[sensorIndex][1]) {
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minIndex = 1;
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maxIndex = 0;
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} else {
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minIndex = 0;
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maxIndex = 1;
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}
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if (storedRngMeas[sensorIndex][2] > storedRngMeas[sensorIndex][maxIndex]) {
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midIndex = maxIndex;
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} else if (storedRngMeas[sensorIndex][2] < storedRngMeas[sensorIndex][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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// don't allow time to go backwards
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if (storedRngMeasTime_ms[sensorIndex][midIndex] > rangeDataNew.time_ms) {
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rangeDataNew.time_ms = storedRngMeasTime_ms[sensorIndex][midIndex];
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}
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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[sensorIndex][midIndex],rngOnGnd);
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// get position in body frame for the current sensor
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rangeDataNew.sensor_idx = sensorIndex;
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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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// indicate we have updated the measurement
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rngValidMeaTime_ms = imuSampleTime_ms;
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} else if (!takeOffDetected && ((imuSampleTime_ms - rngValidMeaTime_ms) > 200)) {
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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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rangeDataNew.time_ms = imuSampleTime_ms;
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// don't allow time to go backwards
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if (imuSampleTime_ms > rangeDataNew.time_ms) {
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rangeDataNew.time_ms = imuSampleTime_ms;
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}
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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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// indicate we have updated the measurement
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rngValidMeaTime_ms = imuSampleTime_ms;
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}
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}
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}
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}
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void NavEKF3_core::writeBodyFrameOdom(float quality, const Vector3f &delPos, const Vector3f &delAng, float delTime, uint32_t timeStamp_ms, const Vector3f &posOffset)
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{
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// limit update rate to maximum allowed by sensor buffers and fusion process
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// don't try to write to buffer until the filter has been initialised
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if (((timeStamp_ms - bodyOdmMeasTime_ms) < frontend->sensorIntervalMin_ms) || (delTime < dtEkfAvg) || !statesInitialised) {
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return;
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}
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bodyOdmDataNew.body_offset = &posOffset;
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bodyOdmDataNew.vel = delPos * (1.0f/delTime);
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bodyOdmDataNew.time_ms = timeStamp_ms;
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bodyOdmDataNew.angRate = delAng * (1.0f/delTime);
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bodyOdmMeasTime_ms = timeStamp_ms;
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// simple model of accuracy
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// TODO move this calculation outside of EKF into the sensor driver
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bodyOdmDataNew.velErr = frontend->_visOdmVelErrMin + (frontend->_visOdmVelErrMax - frontend->_visOdmVelErrMin) * (1.0f - 0.01f * quality);
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storedBodyOdm.push(bodyOdmDataNew);
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}
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void NavEKF3_core::writeWheelOdom(float delAng, float delTime, uint32_t timeStamp_ms, const Vector3f &posOffset, float radius)
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{
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// This is a simple hack to get wheel encoder data into the EKF and verify the interface sign conventions and units
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// It uses the exisiting body frame velocity fusion.
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// TODO implement a dedicated wheel odometry observation model
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// limit update rate to maximum allowed by sensor buffers and fusion process
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// don't try to write to buffer until the filter has been initialised
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if (((timeStamp_ms - wheelOdmMeasTime_ms) < frontend->sensorIntervalMin_ms) || (delTime < dtEkfAvg) || !statesInitialised) {
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return;
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}
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wheelOdmDataNew.hub_offset = &posOffset;
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wheelOdmDataNew.delAng = delAng;
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wheelOdmDataNew.radius = radius;
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wheelOdmDataNew.delTime = delTime;
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wheelOdmMeasTime_ms = timeStamp_ms;
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// because we are currently converting to an equivalent velocity measurement before fusing
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// the measurement time is moved back to the middle of the sampling period
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wheelOdmDataNew.time_ms = timeStamp_ms - (uint32_t)(500.0f * delTime);
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storedWheelOdm.push(wheelOdmDataNew);
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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 NavEKF3_core::writeOptFlowMeas(const uint8_t rawFlowQuality, const Vector2f &rawFlowRates, const Vector2f &rawGyroRates, const uint32_t msecFlowMeas, const Vector3f &posOffset)
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{
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// limit update rate to maximum allowed by sensor buffers
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if ((imuSampleTime_ms - flowMeaTime_ms) < frontend->sensorIntervalMin_ms) {
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return;
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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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// by definition if this function is called, then flow measurements have been provided so we
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// need to run the optical flow takeoff detection
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detectOptFlowTakeoff();
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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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// 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 body rates for bias and write
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ofDataNew.bodyRadXYZ.x = rawGyroRates.x - flowGyroBias.x;
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ofDataNew.bodyRadXYZ.y = rawGyroRates.y - flowGyroBias.y;
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// the sensor interface doesn't provide a z axis rate so use the rate from the nav sensor instead
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if (delTimeOF > 0.001f) {
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// first preference is to use the rate averaged over the same sampling period as the flow sensor
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ofDataNew.bodyRadXYZ.z = delAngBodyOF.z / delTimeOF;
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} else if (imuDataNew.delAngDT > 0.001f){
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// second preference is to use most recent IMU data
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ofDataNew.bodyRadXYZ.z = imuDataNew.delAng.z / imuDataNew.delAngDT;
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} else {
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// third preference is use zero
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ofDataNew.bodyRadXYZ.z = 0.0f;
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}
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// write uncorrected flow rate measurements
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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 the flow sensor position in body frame
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ofDataNew.body_offset = &posOffset;
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// write flow rate measurements corrected for body rates
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ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + ofDataNew.bodyRadXYZ.x;
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ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + ofDataNew.bodyRadXYZ.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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}
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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 NavEKF3_core::readMagData()
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{
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if (!_ahrs->get_compass()) {
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allMagSensorsFailed = true;
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return;
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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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if (_ahrs->get_compass()->learn_offsets_enabled()) {
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// while learning offsets keep all mag states reset
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InitialiseVariablesMag();
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wasLearningCompass_ms = imuSampleTime_ms;
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} else if (wasLearningCompass_ms != 0 && imuSampleTime_ms - wasLearningCompass_ms > 1000) {
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wasLearningCompass_ms = 0;
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// force a new yaw alignment 1s after learning completes. The
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// delay is to ensure any buffered mag samples are discarded
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yawAlignComplete = false;
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InitialiseVariablesMag();
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}
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// limit compass update rate to prevent high processor loading 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) > 1000 * frontend->sensorIntervalMin_ms)) {
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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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gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u switching to compass %u",(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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// clear the data waiting flag so that we do not use any data pending from the previous sensor
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magDataToFuse = false;
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// request a reset of the magnetic field states
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magStateResetRequest = true;
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// declare the field unlearned so that the reset request will be obeyed
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magFieldLearned = false;
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break;
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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 NavEKF3_core::readIMUData()
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{
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const AP_InertialSensor &ins = AP::ins();
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// calculate an averaged IMU update rate using a spike and lowpass filter combination
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dtIMUavg = 0.02f * constrain_float(ins.get_loop_delta_t(),0.5f * dtIMUavg, 2.0f * dtIMUavg) + 0.98f * dtIMUavg;
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// the imu sample time is used as a common time reference throughout the filter
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imuSampleTime_ms = frontend->imuSampleTime_us / 1000;
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uint8_t accel_active, gyro_active;
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if (ins.use_accel(imu_index)) {
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accel_active = imu_index;
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} else {
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accel_active = ins.get_primary_accel();
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}
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if (ins.use_gyro(imu_index)) {
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gyro_active = imu_index;
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} else {
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gyro_active = ins.get_primary_gyro();
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}
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if (gyro_active != gyro_index_active) {
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// we are switching active gyro at runtime. Copy over the
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// bias we have learned from the previously inactive
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// gyro. We don't re-init the bias uncertainty as it should
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// have the same uncertainty as the previously active gyro
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stateStruct.gyro_bias = inactiveBias[gyro_active].gyro_bias;
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gyro_index_active = gyro_active;
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}
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if (accel_active != accel_index_active) {
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// switch to the learned accel bias for this IMU
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stateStruct.accel_bias = inactiveBias[accel_active].accel_bias;
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accel_index_active = accel_active;
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}
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// update the inactive bias states
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learnInactiveBiases();
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readDeltaVelocity(accel_index_active, imuDataNew.delVel, imuDataNew.delVelDT);
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accelPosOffset = ins.get_imu_pos_offset(accel_index_active);
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imuDataNew.accel_index = accel_index_active;
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// Get delta angle data from primary gyro or primary if not available
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readDeltaAngle(gyro_index_active, imuDataNew.delAng);
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imuDataNew.delAngDT = MAX(ins.get_delta_angle_dt(gyro_index_active),1.0e-4f);
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imuDataNew.gyro_index = gyro_index_active;
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// Get current time stamp
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imuDataNew.time_ms = imuSampleTime_ms;
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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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// use the most recent IMU index for the downsampled IMU
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// data. This isn't strictly correct if we switch IMUs between
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// samples
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imuDataDownSampledNew.gyro_index = imuDataNew.gyro_index;
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imuDataDownSampledNew.accel_index = imuDataNew.accel_index;
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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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imuQuatDownSampleNew.rotate(imuDataNew.delAng);
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imuQuatDownSampleNew.normalize();
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// Rotate the latest delta velocity into body frame at the start of accumulation
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Matrix3f deltaRotMat;
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imuQuatDownSampleNew.rotation_matrix(deltaRotMat);
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// Apply the delta velocity to the delta velocity accumulator
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imuDataDownSampledNew.delVel += deltaRotMat*imuDataNew.delVel;
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// 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);
|
|
|
|
}
|
|
|
|
if (gpsGoodToAlign && !have_table_earth_field) {
|
|
table_earth_field_ga = AP_Declination::get_earth_field_ga(gpsloc);
|
|
table_declination = radians(AP_Declination::get_declination(gpsloc.lat*1.0e-7,
|
|
gpsloc.lng*1.0e-7));
|
|
have_table_earth_field = true;
|
|
if (frontend->_mag_ef_limit > 0) {
|
|
// initialise earth field from tables
|
|
stateStruct.earth_magfield = table_earth_field_ga;
|
|
}
|
|
}
|
|
|
|
// 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<INS_MAX_INSTANCES; i++) {
|
|
if (!ins.use_gyro(i)) {
|
|
// can't use this gyro
|
|
continue;
|
|
}
|
|
if (gyro_index_active == i) {
|
|
// use current estimates from main filter of gyro bias
|
|
inactiveBias[i].gyro_bias = stateStruct.gyro_bias;
|
|
} else {
|
|
// get filtered gyro and use the difference between the
|
|
// corrected gyro on the active IMU and the inactive IMU
|
|
// to move the inactive bias towards the right value
|
|
Vector3f filtered_gyro_active = ins.get_gyro(gyro_index_active) - (stateStruct.gyro_bias/dtEkfAvg);
|
|
Vector3f filtered_gyro_inactive = ins.get_gyro(i) - (inactiveBias[i].gyro_bias/dtEkfAvg);
|
|
Vector3f error = filtered_gyro_active - filtered_gyro_inactive;
|
|
|
|
// prevent a single large error from contaminating bias estimate
|
|
const float bias_limit = radians(5);
|
|
error.x = constrain_float(error.x, -bias_limit, bias_limit);
|
|
error.y = constrain_float(error.y, -bias_limit, bias_limit);
|
|
error.z = constrain_float(error.z, -bias_limit, bias_limit);
|
|
|
|
// slowly bring the inactive gyro in line with the active gyro. This corrects a 5 deg/sec
|
|
// gyro bias error in around 1 minute
|
|
inactiveBias[i].gyro_bias -= error * (1.0e-4f * dtEkfAvg);
|
|
}
|
|
}
|
|
|
|
// learn accel biases
|
|
for (uint8_t i=0; i<INS_MAX_INSTANCES; i++) {
|
|
if (!ins.use_accel(i)) {
|
|
// can't use this accel
|
|
continue;
|
|
}
|
|
if (accel_index_active == i) {
|
|
// use current estimates from main filter of accel bias
|
|
inactiveBias[i].accel_bias = stateStruct.accel_bias;
|
|
} else {
|
|
// get filtered accel and use the difference between the
|
|
// corrected accel on the active IMU and the inactive IMU
|
|
// to move the inactive bias towards the right value
|
|
Vector3f filtered_accel_active = ins.get_accel(accel_index_active) - (stateStruct.accel_bias/dtEkfAvg);
|
|
Vector3f filtered_accel_inactive = ins.get_accel(i) - (inactiveBias[i].accel_bias/dtEkfAvg);
|
|
Vector3f error = filtered_accel_active - filtered_accel_inactive;
|
|
|
|
// prevent a single large error from contaminating bias estimate
|
|
const float bias_limit = 1.0; // m/s/s
|
|
error.x = constrain_float(error.x, -bias_limit, bias_limit);
|
|
error.y = constrain_float(error.y, -bias_limit, bias_limit);
|
|
error.z = constrain_float(error.z, -bias_limit, bias_limit);
|
|
|
|
// slowly bring the inactive accel in line with the active
|
|
// accel. This corrects a 0.5 m/s/s accel bias error in
|
|
// around 1 minute
|
|
inactiveBias[i].accel_bias -= error * (1.0e-4f * dtEkfAvg);
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
return declination in radians
|
|
*/
|
|
float NavEKF3_core::MagDeclination(void) const
|
|
{
|
|
// if we are using the WMM tables then use the table declination
|
|
// to ensure consistency with the table mag field. Otherwise use
|
|
// the declination from the compass library
|
|
if (have_table_earth_field && frontend->_mag_ef_limit > 0) {
|
|
return table_declination;
|
|
}
|
|
if (!use_compass()) {
|
|
return 0;
|
|
}
|
|
return _ahrs->get_compass()->get_declination();
|
|
}
|