PX4-Autopilot/EKF/terrain_estimator.cpp

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/**
 * @file terrain_estimator.cpp
 * Function for fusing rangefinder measurements to estimate terrain vertical position/
 *
 * @author Paul Riseborough <p_riseborough@live.com.au>
 *
 */

#include "ekf.h"
#include <ecl.h>
#include <mathlib/mathlib.h>

bool Ekf::initHagl()
{
	bool initialized = false;
	// get most recent range measurement from buffer
	const rangeSample &latest_measurement = _range_buffer.get_newest();

	if (!_control_status.flags.in_air) {
		// if on ground, do not trust the range sensor, but assume a ground clearance
		_terrain_vpos = _state.pos(2) + _params.rng_gnd_clearance;
		// use the ground clearance value as our uncertainty
		_terrain_var = sq(_params.rng_gnd_clearance);
		initialized = true;

	} else if (_rng_hgt_valid
		   && (_time_last_imu - latest_measurement.time_us) < (uint64_t)2e5
		   && _R_rng_to_earth_2_2 > _params.range_cos_max_tilt) {
		// if we have a fresh measurement, use it to initialise the terrain estimator
		_terrain_vpos = _state.pos(2) + latest_measurement.rng * _R_rng_to_earth_2_2;
		// initialise state variance to variance of measurement
		_terrain_var = sq(_params.range_noise);
		// success
		initialized = true;

	} else if (_flow_for_terrain_data_ready) {
		// initialise terrain vertical position to origin as this is the best guess we have
		_terrain_vpos = fmaxf(0.0f,  _state.pos(2));
		_terrain_var = 100.0f;
		initialized = true;

	} else {
		// no information - cannot initialise
		initialized = false;
	}

	if (initialized) {
		// has initialized with valid data
		_time_last_hagl_fuse = _time_last_imu;
	}

	return initialized;
}

void Ekf::runTerrainEstimator()
{
	// Perform initialisation check and
	// on ground, continuously reset the terrain estimator
	if (!_terrain_initialised || !_control_status.flags.in_air) {
		_terrain_initialised = initHagl();

	} else {

		// predict the state variance growth where the state is the vertical position of the terrain underneath the vehicle

		// process noise due to errors in vehicle height estimate
		_terrain_var += sq(_imu_sample_delayed.delta_vel_dt * _params.terrain_p_noise);

		// process noise due to terrain gradient
		_terrain_var += sq(_imu_sample_delayed.delta_vel_dt * _params.terrain_gradient)
				* (sq(_state.vel(0)) + sq(_state.vel(1)));

		// limit the variance to prevent it becoming badly conditioned
		_terrain_var = math::constrain(_terrain_var, 0.0f, 1e4f);

		// Fuse range finder data if available
		if (_range_data_ready && _rng_hgt_valid) {
			fuseHagl();

			// update range sensor angle parameters in case they have changed
			// we do this here to avoid doing those calculations at a high rate
			_sin_tilt_rng = sinf(_params.rng_sens_pitch);
			_cos_tilt_rng = cosf(_params.rng_sens_pitch);
		}

		if (_flow_for_terrain_data_ready) {
			fuseFlowForTerrain();
			_flow_for_terrain_data_ready = false;
		}

		// constrain _terrain_vpos to be a minimum of _params.rng_gnd_clearance larger than _state.pos(2)
		if (_terrain_vpos - _state.pos(2) < _params.rng_gnd_clearance) {
			_terrain_vpos = _params.rng_gnd_clearance + _state.pos(2);
		}
	}

	updateTerrainValidity();
}

void Ekf::fuseHagl()
{
	// If the vehicle is excessively tilted, do not try to fuse range finder observations
	if (_R_rng_to_earth_2_2 > _params.range_cos_max_tilt) {
		// get a height above ground measurement from the range finder assuming a flat earth
		float meas_hagl = _range_sample_delayed.rng * _R_rng_to_earth_2_2;

		// predict the hagl from the vehicle position and terrain height
		float pred_hagl = _terrain_vpos - _state.pos(2);

		// calculate the innovation
		_hagl_innov = pred_hagl - meas_hagl;

		// calculate the observation variance adding the variance of the vehicles own height uncertainty
		float obs_variance = fmaxf(P[9][9] * _params.vehicle_variance_scaler, 0.0f)
				     + sq(_params.range_noise)
				     + sq(_params.range_noise_scaler * _range_sample_delayed.rng);

		// calculate the innovation variance - limiting it to prevent a badly conditioned fusion
		_hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance);

		// perform an innovation consistency check and only fuse data if it passes
		float gate_size = fmaxf(_params.range_innov_gate, 1.0f);
		_terr_test_ratio = sq(_hagl_innov) / (sq(gate_size) * _hagl_innov_var);

		if (_terr_test_ratio <= 1.0f) {
			// calculate the Kalman gain
			float gain = _terrain_var / _hagl_innov_var;
			// correct the state
			_terrain_vpos -= gain * _hagl_innov;
			// correct the variance
			_terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f);
			// record last successful fusion event
			_time_last_hagl_fuse = _time_last_imu;
			_innov_check_fail_status.flags.reject_hagl = false;

		} else {
			// If we have been rejecting range data for too long, reset to measurement
			if ((_time_last_imu - _time_last_hagl_fuse) > (uint64_t)10E6) {
				_terrain_vpos = _state.pos(2) + meas_hagl;
				_terrain_var = obs_variance;

			} else {
				_innov_check_fail_status.flags.reject_hagl = true;
			}
		}

	} else {
		_innov_check_fail_status.flags.reject_hagl = true;
	}
}

void Ekf::fuseFlowForTerrain()
{
	// calculate optical LOS rates using optical flow rates that have had the body angular rate contribution removed
	// correct for gyro bias errors in the data used to do the motion compensation
	// Note the sign convention used: A positive LOS rate is a RH rotation of the scene about that axis.
	Vector2f opt_flow_rate;
	opt_flow_rate(0) = _flowRadXYcomp(0) / _flow_sample_delayed.dt + _flow_gyro_bias(0);
	opt_flow_rate(1) = _flowRadXYcomp(1) / _flow_sample_delayed.dt + _flow_gyro_bias(1);

	// get latest estimated orientation
	float q0 = _state.quat_nominal(0);
	float q1 = _state.quat_nominal(1);
	float q2 = _state.quat_nominal(2);
	float q3 = _state.quat_nominal(3);

	// calculate the optical flow observation variance
	float R_LOS = calcOptFlowMeasVar();

	// get rotation matrix from earth to body
	Dcmf earth_to_body = quat_to_invrotmat(_state.quat_nominal);

	// calculate the sensor position relative to the IMU
	Vector3f pos_offset_body = _params.flow_pos_body - _params.imu_pos_body;

	// calculate the velocity of the sensor relative to the imu in body frame
	// Note: _flow_sample_delayed.gyroXYZ is the negative of the body angular velocity, thus use minus sign
	Vector3f vel_rel_imu_body = cross_product(-_flow_sample_delayed.gyroXYZ / _flow_sample_delayed.dt, pos_offset_body);

	// calculate the velocity of the sensor in the earth frame
	Vector3f vel_rel_earth = _state.vel + _R_to_earth * vel_rel_imu_body;

	// rotate into body frame
	Vector3f vel_body = earth_to_body * vel_rel_earth;

	float t0 = q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3;

	// constrain terrain to minimum allowed value and predict height above ground
	_terrain_vpos = fmaxf(_terrain_vpos, _params.rng_gnd_clearance + _state.pos(2));
	float pred_hagl = _terrain_vpos - _state.pos(2);

	// Calculate observation matrix for flow around the vehicle x axis
	float Hx = vel_body(1) * t0 / (pred_hagl * pred_hagl);

	// Constrain terrain variance to be non-negative
	_terrain_var = fmaxf(_terrain_var, 0.0f);

	// Cacluate innovation variance
	_flow_innov_var[0] = Hx * Hx * _terrain_var + R_LOS;

	// calculate the kalman gain for the flow x measurement
	float Kx = _terrain_var * Hx / _flow_innov_var[0];

	// calculate prediced optical flow about x axis
	float pred_flow_x = vel_body(1) * earth_to_body(2, 2) / pred_hagl;

	// calculate flow innovation (x axis)
	_flow_innov[0] = pred_flow_x - opt_flow_rate(0);

	// calculate correction term for terrain variance
	float KxHxP =  Kx * Hx * _terrain_var;

	// innovation consistency check
	float gate_size = fmaxf(_params.flow_innov_gate, 1.0f);
	float flow_test_ratio = sq(_flow_innov[0]) / (sq(gate_size) * _flow_innov_var[0]);

	// do not perform measurement update if badly conditioned
	if (flow_test_ratio <= 1.0f) {
		_terrain_vpos += Kx * _flow_innov[0];
		// guard against negative variance
		_terrain_var = fmaxf(_terrain_var - KxHxP, 0.0f);
		_time_last_of_fuse = _time_last_imu;
	}

	// Calculate observation matrix for flow around the vehicle y axis
	float Hy = -vel_body(0) * t0 / (pred_hagl * pred_hagl);

	// Calculuate innovation variance
	_flow_innov_var[1] = Hy * Hy * _terrain_var + R_LOS;

	// calculate the kalman gain for the flow y measurement
	float Ky = _terrain_var * Hy / _flow_innov_var[1];

	// calculate prediced optical flow about y axis
	float pred_flow_y = -vel_body(0) * earth_to_body(2, 2) / pred_hagl;

	// calculate flow innovation (y axis)
	_flow_innov[1] = pred_flow_y - opt_flow_rate(1);

	// calculate correction term for terrain variance
	float KyHyP =  Ky * Hy * _terrain_var;

	// innovation consistency check
	flow_test_ratio = sq(_flow_innov[1]) / (sq(gate_size) * _flow_innov_var[1]);

	if (flow_test_ratio <= 1.0f) {
		_terrain_vpos += Ky * _flow_innov[1];
		// guard against negative variance
		_terrain_var = fmaxf(_terrain_var - KyHyP, 0.0f);
		_time_last_of_fuse = _time_last_imu;
	}
}

bool Ekf::isTerrainEstimateValid() const
{
	return _hagl_valid;
}

void Ekf::updateTerrainValidity()
{
	// we have been fusing range finder measurements in the last 5 seconds
	bool recent_range_fusion = (_time_last_imu - _time_last_hagl_fuse) < (uint64_t)5e6;

	// we have been fusing optical flow measurements for terrain estimation within the last 5 seconds
	// this can only be the case if the main filter does not fuse optical flow
	bool recent_flow_for_terrain_fusion = ((_time_last_imu - _time_last_of_fuse) < (uint64_t)5e6)
					      && !_control_status.flags.opt_flow;

	_hagl_valid = (_terrain_initialised && (recent_range_fusion || recent_flow_for_terrain_fusion));
}

// get the estimated vertical position of the terrain relative to the NED origin
void Ekf::getTerrainVertPos(float *ret)
{
	memcpy(ret, &_terrain_vpos, sizeof(float));
}

void Ekf::getHaglInnov(float *hagl_innov)
{
	memcpy(hagl_innov, &_hagl_innov, sizeof(_hagl_innov));
}


void Ekf::getHaglInnovVar(float *hagl_innov_var)
{
	memcpy(hagl_innov_var, &_hagl_innov_var, sizeof(_hagl_innov_var));
}